content
stringlengths 0
14.9M
| filename
stringlengths 44
136
|
|---|---|
phi.test <- function(yuima, H0, H1, phi, print=FALSE,...){
phiname <- deparse(substitute(phi))
if(missing(phi)){
phi <- function(x) 1-x+x*log(x)
phiname <- "1-x+x*log(x)"
}
d.size <- yuima@[email protected]
n <- length(yuima)[1]
env <- new.env()
assign("X", as.matrix(onezoo(yuima)), envir=env)
assign("deltaX", matrix(0, n-1, d.size), envir=env)
for(t in 1:(n-1))
env$deltaX[t,] <- env$X[t+1,] - env$X[t,]
assign("h", deltat(yuima@[email protected][[1]]), envir=env)
assign("time", as.numeric(index(yuima@[email protected][[1]])), envir=env)
est <- FALSE
if(missing(H1)){
cat("\nestimating parameters via QMLE...\n")
H1 <- coef(qmle(yuima, ...))
est <- TRUE
}
g0 <- quasiloglvec(yuima=yuima, param=H0, print=print, env)
g1 <- quasiloglvec(yuima=yuima, param=H1, print=print, env)
y <- exp(g1-g0)
div <- mean(phi(y), na.rm=TRUE)
stat <- 2*sum(phi(y), na.rm=TRUE)
df <- length(H0)
val <- list(div=div, stat=stat, H0=H0, H1=H1, phi=phiname, phi=phi, pvalue=1-pchisq(stat, df=df), df=df,est=est)
attr(val, "class") <- "phitest"
return( val )
}
print.phitest <- function(x,...){
symnum(x$pvalue, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("***", "**", "*", ".", " "))->Signif
cat(sprintf("Phi-Divergence test statistic based on phi = '%s'\n",x$phi) )
nm <- names(x$H0)
cat("H0: ")
cat(sprintf("%s = %s", nm, format(x$H0,digits=3,nsmall=3)))
cat("\nversus\n")
cat("H1: ")
cat(sprintf("%s = %s", nm, format(x$H1[nm],digits=3,nsmall=3)))
if(x$est)
cat("\nH1 parameters estimated using QMLE")
cat(sprintf("\n\nTest statistic = %s, df = %d, p-value = %s %s\n", format(x$stat,digits=2,nsmall=3), x$df, format(x$pvalue), Signif))
cat("---\nSignif. codes: ", attr(Signif, "legend"), "\n")
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/phi.test.R |
# poisson.random.sampling
# returns sample of data using poisson sampling
setGeneric("poisson.random.sampling",
function(x, rate, n)
standardGeneric("poisson.random.sampling")
)
setMethod("poisson.random.sampling", "yuima.data",
function(x, rate, n){
Data <- get.zoo.data(x)
if(missing(rate)){
rate <- rep(0.01,length(Data))
}
for(i in 1:(length(Data))) {
T <- end(Data[[i]])-start(Data[[i]])
Num <- length(Data[[i]])
deltat <- T /(Num-1)
# expectation value of length
Elength <- T*n*rate[i]
## make random numbers following exponential distribution
Time <- diffinv(rexp(Elength,rate=deltat*n*rate[i]))
# make up a deficit
while(Time[length(Time)]< Num){
# adding random numbers (by 10% of Elength)
Time2 <- diffinv(rexp(trunc(Elength/10+1),rate=deltat*n*rate[i]))+Time[length(Time)]
# restrain duplication
Time <- append(Time,Time2[-1])
}
## making time index
# get rid of first element of X and elements that over n
# round Time to unit
xTime<-trunc(Time[0<Time & Num>Time])
# get rid of elements of value = 0
xTime<-xTime[0<xTime]
# time index : (xTime-1)*deltat(X)
idx <- unique(xTime)
Data[[i]] <- Data[[i]][idx]
}
return(setData(original.data=Data))
}
)
setMethod("poisson.random.sampling","yuima",function(x,rate,n) return(poisson.random.sampling(x@data,rate,n)))
| /scratch/gouwar.j/cran-all/cranData/yuima/R/poisson.random.sampling.R |
# Podolskij-Ziggel's Jump Test
pz.test <- function(yuima, p = 4, threshold = "local", tau = 0.05){
data <- get.zoo.data(yuima)
d.size <- length(data)
adx <- vector(d.size,mode="list")
for(d in 1:d.size){
X <- as.numeric(data[[d]])
idt <- which(is.na(X))
if(length(idt>0)){
X <- X[-idt]
}
if(length(X)<2) {
stop("length of data (w/o NA) must be more than 1")
}
adx[[d]] <- abs(diff(X))
}
n <- sapply(adx, "length")
if(threshold == "local"){
thr <- local_univ_threshold(data)
}else if(threshold == "PZ"){
thr <- 2.3^2 * mpv(yuima, r = c(1, 1)) * n^(-0.8)
}else{
thr <- threshold
}
# core part
result <- vector(d.size, mode = "list")
for(d in 1:d.size){
# thresholding
tr.idx <- (adx[[d]]^2 <= thr[[d]])
eta <- sample(c(1 - tau, 1 + tau), size = n[d], replace = TRUE)
numer <- sum(adx[[d]]^p * (1 - eta * tr.idx))
denom <- tau * sqrt(sum(adx[[d]]^(2*p) * tr.idx))
PZ <- numer/denom
pval <- pnorm(PZ,lower.tail=FALSE)
result[[d]] <- list(statistic=c(PZ=PZ),p.value=pval,
method="Podolskij-Ziggel jump test",
data.names=paste("x",d,sep=""))
class(result[[d]]) <- "htest"
}
return(result)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/pz.test.R |
##:: function qgv
##:: Estimating the local Holder exponent of the path and the constant
qgv<- function(yuima, filter.type="Daubechies", order=2, a=NULL){
call <- match.call()
if(missing(yuima)){
yuima.stop("yuima object is missing.")
}
# if(class(yuima)!="yuima"){
if(!inherits(yuima,"yuima")){
yuima.stop("an object of class yuima is needed.")
}
Ddiffx0 <- D(yuima@model@diffusion[[1]],yuima@[email protected]) == 0
Ddifft0 <- D(yuima@model@diffusion[[1]],yuima@[email protected]) == 0
bconst<-FALSE
Hconst<-FALSE
diffnoparam<-length(yuima@model@parameter@diffusion)==0
if (Ddiffx0 & Ddifft0 & diffnoparam){
if(eval(yuima@model@diffusion[[1]])==0){
yuima.stop("the model has no fractional part.")
}
bconst<-TRUE
}
y<-yuima@model@drift
dx <- D(y,yuima@[email protected])
bt <- D(y,yuima@[email protected])==0
bxx <- D(dx,yuima@[email protected])==0
dbx <- D(yuima@model@diffusion[[1]],yuima@[email protected])==0
dbt <- D(yuima@model@diffusion[[1]],yuima@[email protected])==0
isfOU<-(bxx && bt) && (dbx && dbt)
if (!is.na(yuima@model@hurst)){
yuima.warn("No Hurst exponent will be estimated")
Hconst<-TRUE
}
H <- yuima@model@hurst
sdH<-NA
if(Hconst & bconst){
x<-c(H,eval(yuima@model@diffusion[[1]]))
names(x)<-c("hurst","const")
sdx<-diag(c(NA,NA))
colnames(sdx)<-names(x)
rownames(sdx)<-names(x)
sdx[2,1] <- sdx[1,2] <- NA
obj <- list(coefficients=x,vcov=sdx,call=call)
class(obj) <- "qgv"
return(obj)
}
isregular<-yuima@sampling@regular
if (!isregular){
yuima.stop("qgv method is only working for regular grid.")
}
if (!(order %in% 1:10)){
yuima.warn("Classical filter implement are of order ranged in [1,10], order have been fixed to 2.")
order=2
}
if (missing(a)){
if (filter.type=="Daubechies"){
if (order==2){
a<-1/sqrt(2)*c(.4829629131445341,
-.8365163037378077,
.2241438680420134,
.1294095225512603)
}else{
yuima.warn("There is no such order Daubechies' filter implemented, order have been fixed to 2.")
a<-1/sqrt(2)*c(.4829629131445341,
-.8365163037378077,
.2241438680420134,
.1294095225512603)
order=2
}
}else if (filter.type=="Classical"){
mesh<-0:order
a=(-1)^(1-mesh)/2^order*choose(order,mesh)
}else{
yuima.warn("No such type of filter. Filter have been fixed to Daubechies' filter of order 2.")
a<-1/sqrt(2)*c(.4829629131445341,
-.8365163037378077,
.2241438680420134,
.1294095225512603)
order=2
}
}
L<-length(a)
a2<-rep(0,2*L)
a2[seq(1,2*L,by=2)]<-a
process<-yuima@[email protected][[1]]
N<-length(process)
#Computation of the generalized quadratic variations
V1<-sum(filter(process,a)^2,na.rm=TRUE)
V2<-sum(filter(process,a2)^2,na.rm=TRUE)
if(!Hconst){
H<-1/2*log2(V2/V1)
}
nconst <- "const"
sdC<-NA
C <- NA
if(isfOU){
if( bconst||(H>=1)||(H<=0)){
if(diffnoparam){
C<-eval(yuima@model@diffusion[[1]])
}
}else{
#Compute the estimation of the constant C.
delta<-yuima@sampling@delta
constfilt<-sum(a%*%t(a)*abs(matrix(0:(L-1),L,L)-matrix(0:(L-1),L,L,byrow=TRUE))^(2*H))
C<- sqrt(-2*V1/(N-L)/(constfilt*delta^(2*H)))
nconst<-as.character(yuima@model@diffusion[[1]])
}
} else {
nconst<- as.character(yuima@model@diffusion[[1]])
}
if(isfOU){
#Compute the standard error
infty<-100
C11<-rep(0,2*infty+1)
C11bis<-rep(0,2*infty+1)
C12<-rep(0,2*infty+1)
C22<-rep(0,2*infty+1)
l<-order+1
for (i in (-infty:infty)){
for (q in 0:l){
for (r in 0:l){
C11[i+infty+1]<-C11[i+infty+1]+a[q+1]*a[r+1]*abs(q-r+i)^(2*H)
}
}
for (q in 0:l){
for (r in 0:l){
C12[i+infty+1]<-C12[i+infty+1]+a[q+1]*a[r+1]*abs(2*q-r+i)^(2*H)
}
}
for (q in 0:l){
for (r in 0:l){
C22[i+infty+1]<-C22[i+infty+1]+a[q+1]*a[r+1]*abs(2*q-2*r+i)^(2*H)
}
}
for (q in 0:(2*l)){
for (r in 0:(2*l)){
C11bis[i+infty+1]<-C11bis[i+infty+1]+a2[q+1]*a2[r+1]*abs(q-r+i)^(2*H)
}
}
}
rho11<-1/2*sum((C11/C11[infty+1])^2)
rho11dil<-1/2*sum((C11bis/C11bis[infty+1])^2)
rho12<-1/2*sum((C12/2^H/C11[infty+1])^2)
rho22<-1/2*sum((C22/2^H/2^H/C11[infty+1])^2)
sigma1<-1/(log(2))^2*(rho11+rho11dil-2*rho12)
if((!Hconst)&(H>0)){
sdH<-sqrt(sigma1)/sqrt(N)
}
if ((H>0)&(H<1)&(!bconst)){
sigma2<-C^2/2*sigma1
sdC<-sqrt(sigma2)/sqrt(N)*log(N)
}
}
nconst <- gsub("[()]", "", nconst)
x<-c(H,C)
names(x)<-c("hurst",nconst)
sdx<-diag(c(sdH,sdC))
colnames(sdx)<-names(x)
rownames(sdx)<-names(x)
sdx[2,1] <- sdx[1,2] <- NA
obj <- list(coefficients=x,vcov=sdx,call=call)
class(obj) <- "qgv"
return(obj)
}
print.qgv<-function(x,...){
tmp <- rbind(x$coefficients, diag(x$vcov))
rownames(tmp) <- c("Estimate", "Std. Error")
cat("\nFractional OU estimation\n")
print(tmp)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/qgv.R |
##::quasi-likelihood function
##::extract drift term from yuima
##::para: parameter of drift term (theta2)
### TO BE FIXED: all caculations should be made on a private environment to
### avoid problems.
### I have rewritten drift.term and diff.term instead of calc.drift and
### calc.diffusion to make them independent of the specification of the
### parameters. S.M.I. 22/06/2010
drift.term <- function(yuima, theta, env){
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
modelstate <- yuima@[email protected]
DRIFT <- yuima@model@drift
# n <- length(yuima)[1]
n <- dim(env$X)[1]
drift <- matrix(0,n,d.size)
tmp.env <- new.env(parent = env) ##Kurisaki 4/4/2021
assign(yuima@[email protected], env$time, envir=tmp.env)
for(i in 1:length(theta)){
assign(names(theta)[i],theta[[i]], envir=tmp.env)
}
for(d in 1:d.size){
assign(modelstate[d], env$X[,d], envir=tmp.env)
}
for(d in 1:d.size){
drift[,d] <- eval(DRIFT[d], envir=tmp.env)
}
return(drift)
}
diffusion.term <- function(yuima, theta, env){
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
modelstate <- yuima@[email protected]
DIFFUSION <- yuima@model@diffusion
# n <- length(yuima)[1]
n <- dim(env$X)[1]
tmp.env <- new.env(parent = env) ##Kurisaki 4/4/2021
assign(yuima@[email protected], env$time, envir=tmp.env)
diff <- array(0, dim=c(d.size, r.size, n))
for(i in 1:length(theta)){
assign(names(theta)[i],theta[[i]],envir=tmp.env)
}
for(d in 1:d.size){
assign(modelstate[d], env$X[,d], envir=tmp.env)
}
for(r in 1:r.size){
for(d in 1:d.size){
diff[d, r, ] <- eval(DIFFUSION[[d]][r], envir=tmp.env)
}
}
return(diff)
}
## Koike's code
##::extract jump term from yuima
##::gamma: parameter of diffusion term (theta3)
measure.term <- function(yuima, theta, env){
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
modelstate <- yuima@[email protected]
n <- dim(env$X)[1]
tmp.env <- new.env(parent = env) # 4/17/2021 Kito
assign(yuima@[email protected], env$time, envir =tmp.env)
JUMP <- yuima@[email protected]
measure <- array(0, dim=c(d.size, r.size, n))
for(i in 1:length(theta)){
assign(names(theta)[i],theta[[i]],envir=tmp.env)
}
for(d in 1:d.size){
assign(modelstate[d], env$X[,d],envir=tmp.env)
}
for(r in 1:r.size){
#for(d.tmp in 1:d){
if(d.size==1){
measure[1,r,] <- eval(JUMP[[r]],envir=tmp.env)
}else{
for(d in 1:d.size){
measure[d,r,] <- eval(JUMP[[d]][r],envir=tmp.env)
}
}
}
return(measure)
}
### I have rewritten qmle as a version of ml.ql
### This function has an interface more similar to mle.
### ml.ql is limited in that it uses fixed names for drift and diffusion
### parameters, while yuima model allows for any names.
### also, I am using the same interface of optim to specify upper and lower bounds
### S.M.I. 22/06/2010
is.Poisson <- function(obj){
if(is(obj,"yuima"))
return(is(obj@model, "yuima.poisson"))
if(is(obj,"yuima.model"))
return(is(obj, "yuima.poisson"))
return(FALSE)
}
is.CARMA <- function(obj){
if(is(obj,"yuima"))
return(is(obj@model, "yuima.carma"))
if(is(obj,"yuima.model"))
return(is(obj, "yuima.carma"))
return(FALSE)
}
qmle <- function(yuima, start, method="L-BFGS-B", fixed = list(), print=FALSE, envir=globalenv(), ##Kurisaki 4/4/2021
lower, upper, joint=FALSE, Est.Incr="NoIncr",aggregation=TRUE, threshold=NULL,rcpp=FALSE, ...){
if(Est.Incr=="Carma.Inc"){
Est.Incr<-"Incr"
}
if(Est.Incr=="Carma.Par"){
Est.Incr<-"NoIncr"
}
if(Est.Incr=="Carma.IncPar"){
Est.Incr<-"IncrPar"
}
if(is(yuima@model, "yuima.carma")){
NoNeg.Noise<-FALSE
cat("\nStarting qmle for carma ... \n")
}
if(is.CARMA(yuima)&& length(yuima@model@[email protected])!=0){
method<-"L-BFGS-B"
}
call <- match.call()
if( missing(yuima))
yuima.stop("yuima object is missing.")
if(is.COGARCH(yuima)){
if(missing(lower))
lower <- list()
if(missing(upper))
upper <- list()
res <- NULL
if("grideq" %in% names(as.list(call)[-(1:2)])){
res <- PseudoLogLik.COGARCH(yuima, start, method=method, fixed = list(),
lower, upper, Est.Incr, call, aggregation = aggregation, ...)
}else{
res <- PseudoLogLik.COGARCH(yuima, start, method=method, fixed = list(),
lower, upper, Est.Incr, call, grideq = FALSE, aggregation = aggregation,...)
}
return(res)
}
if(is.PPR(yuima)){
if(missing(lower))
lower <- list()
if(missing(upper))
upper <- list()
# res <- NULL
# if("grideq" %in% names(as.list(call)[-(1:2)])){
res <- quasiLogLik.PPR(yuimaPPR = yuima, parLambda = start, method=method, fixed = list(),
lower, upper, call, ...)
# }else{
# res <- PseudoLogLik.COGARCH(yuima, start, method=method, fixed = list(),
# lower, upper, Est.Incr, call, grideq = FALSE, aggregation = aggregation,...)
# }
return(res)
}
orig.fixed <- fixed
orig.fixed.par <- names(orig.fixed)
if(is.Poisson(yuima))
threshold <- 0
## param handling
## FIXME: maybe we should choose initial values at random within lower/upper
## at present, qmle stops
if( missing(start) )
yuima.stop("Starting values for the parameters are missing.")
#14/12/2013 We modify the QMLE function when the model is a Carma(p,q).
# In this case we use a two step procedure:
# First) The Coefficient are obtained by QMLE computed using the Kalman Filter.
# Second) Using the result in Brockwell, Davis and Yang (2007) we retrieve
# the underlying Levy. The estimated increments are used to find the L?vy parameters.
# if(is(yuima@model, "yuima.carma")){
# yuima.warm("two step procedure for carma(p,q)")
# return(null)
# }
#
### 7/8/2021 Kito
if(length(fixed) > 0 && !is.Poisson(yuima) && !is.CARMA(yuima) && !is.COGARCH(yuima)) {
new.yuima.list <- changeFixedParametersToConstant(yuima, fixed)
new.yuima <- new.yuima.list$new.yuima
qmle.env <- new.yuima.list$env
# new params
new.start = start[!is.element(names(start), names(fixed))]
new.lower = lower[!is.element(names(lower), names(fixed))]
new.upper = upper[!is.element(names(upper), names(fixed))]
#Kurisaki 5/23/2021
res <- qmle(new.yuima, start = new.start, method = method, fixed = list(), print = print, envir = qmle.env,
lower = new.lower, upper = new.upper, joint = joint, Est.Incr = Est.Incr, aggregation = aggregation, threshold = threshold, rcpp = rcpp, ...)
res@call <- match.call()
res@model <- yuima@model
fixed.res <- fixed
mode(fixed.res) <- "numeric"
res@fullcoef <- c(res@fullcoef,fixed.res)
res@fixed <- fixed.res
return(res)
}
yuima.nobs <- as.integer(max(unlist(lapply(get.zoo.data(yuima),length))-1,na.rm=TRUE))
diff.par <- yuima@model@parameter@diffusion
# 24/12
if(is.CARMA(yuima) && length(diff.par)==0
&& length(yuima@model@parameter@jump)!=0){
diff.par<-yuima@model@parameter@jump
}
if(is.CARMA(yuima) && length(yuima@model@parameter@jump)!=0){
CPlist <- c("dgamma", "dexp")
codelist <- c("rIG", "rgamma")
if(yuima@[email protected]=="CP"){
tmp <- regexpr("\\(", yuima@model@measure$df$exp)[1]
measurefunc <- substring(yuima@model@measure$df$exp, 1, tmp-1)
if(!is.na(match(measurefunc,CPlist))){
yuima.warn("carma(p,q): the qmle for a carma(p,q) driven by a Compound Poisson with no-negative random size")
NoNeg.Noise<-TRUE
# we need to add a new parameter for the mean of the Noise
if((yuima@model@info@q+1)==(yuima@model@info@q+1)){
start[["mean.noise"]]<-1
}
# return(NULL)
}
}
if(yuima@[email protected]=="code"){
#if(class(yuima@model@measure$df)=="yuima.law"){
if(inherits(yuima@model@measure$df, "yuima.law")){ # YK, Mar. 22, 2022
measurefunc <- "yuima.law"
}
else{
tmp <- regexpr("\\(", yuima@model@measure$df$exp)[1]
measurefunc <- substring(yuima@model@measure$df$exp, 1, tmp-1)
}
if(!is.na(match(measurefunc,codelist))){
yuima.warn("carma(p,q): the qmle for a carma(p,q) driven by a non-Negative Levy will be implemented as soon as possible")
NoNeg.Noise<-TRUE
if((yuima@model@info@q+1)==(yuima@model@info@q+1)){
start[["mean.noise"]]<-1
}
#return(NULL)
}
}
# yuima.warn("carma(p,q): the qmle for a carma(p,q) driven by a Jump process will be implemented as soon as possible ")
# return(NULL)
}
# 24/12
if(is.CARMA(yuima) && length(yuima@model@[email protected])>0){
yuima.warn("carma(p,q): the case of lin.par will be implemented as soon as")
return(NULL)
}
drift.par <- yuima@model@parameter@drift
#01/01 we introduce the new variable in order
# to take into account the parameters in the starting conditions
if(is.CARMA(yuima)){
#if(length(yuima@model@[email protected])!=0){
xinit.par <- yuima@model@parameter@xinit
#}
}
# SMI-2/9/14: measure.par is used for Compound Poisson
# and CARMA, jump.par only by CARMA
jump.par <- NULL
if(is.CARMA(yuima)){
jump.par <- yuima@model@parameter@jump
measure.par <- yuima@model@parameter@measure
} else {
if(length(yuima@model@parameter@jump)!=0){
measure.par <- unique(c(yuima@model@parameter@measure,yuima@model@parameter@jump))
} else {
measure.par <- yuima@model@parameter@measure
}
}
# jump.par is used for CARMA
common.par <- yuima@model@parameter@common
JointOptim <- joint
if(is.CARMA(yuima) && length(yuima@model@parameter@jump)!=0){
if(any((match(jump.par, drift.par)))){
JointOptim <- TRUE
yuima.warn("Drift and diffusion parameters must be different. Doing
joint estimation, asymptotic theory may not hold true.")
}
}
if(length(common.par)>0){
JointOptim <- TRUE
yuima.warn("Drift and diffusion parameters must be different. Doing
joint estimation, asymptotic theory may not hold true.")
# 24/12
# if(is(yuima@model, "yuima.carma")){
# JointOptim <- TRUE
# yuima.warm("Carma(p.q): The case of common parameters in Drift and Diffusion Term will be implemented as soon as possible,")
# #return(NULL)
# }
}
# if(!is(yuima@model, "yuima.carma")){
# if(length(jump.par)+yuima@[email protected] == "CP")
# yuima.stop("Cannot estimate the jump models, yet")
# }
if(!is.list(start))
yuima.stop("Argument 'start' must be of list type.")
fullcoef <- NULL
if(length(diff.par)>0)
fullcoef <- diff.par
if(length(drift.par)>0)
fullcoef <- c(fullcoef, drift.par)
if(is.CARMA(yuima) &&
(length(yuima@model@[email protected])!=0)){
# 01/01 We modify the code for considering
# the loc.par in yuima.carma model
fullcoef<-c(fullcoef, yuima@model@[email protected])
}
if(is.CARMA(yuima) && (NoNeg.Noise==TRUE)){
if((yuima@model@info@q+1)==yuima@model@info@p){
mean.noise<-"mean.noise"
fullcoef<-c(fullcoef, mean.noise)
}
}
# if(is.CARMA(yuima) && (yuima@[email protected] == "CP")){
fullcoef<-c(fullcoef, measure.par)
#}
if(is.CARMA(yuima)){
if(length(yuima@model@parameter@xinit)>1){
#fullcoef<-unique(c(fullcoef,yuima@model@parameter@xinit))
condIniCarma<-!(yuima@model@parameter@xinit%in%fullcoef)
if(sum(condIniCarma)>0){
NamesInitial<- yuima@model@parameter@xinit[condIniCarma]
start <- as.list(unlist(start)[!names(unlist(start))%in%(NamesInitial)])
}
}
}
npar <- length(fullcoef)
fixed.par <- names(fixed) # We use Fixed.par when we consider a Carma with scale parameter
fixed.carma=NULL
if(is.CARMA(yuima) && (length(measure.par) > 0)){
if(!missing(fixed)){
if(names(fixed) %in% measure.par){
idx.fixed.carma<-match(names(fixed),measure.par)
idx.fixed.carma<-idx.fixed.carma[!is.na(idx.fixed.carma)]
if(length(idx.fixed.carma)!=0){
fixed.carma<-as.numeric(fixed[measure.par[idx.fixed.carma]])
names(fixed.carma)<-measure.par[idx.fixed.carma]
}
}
}
upper.carma=NULL
if(!missing(upper)){
if(names(upper) %in% measure.par){
idx.upper.carma<-match(names(upper),measure.par)
idx.upper.carma<-idx.upper.carma[!is.na(idx.upper.carma)]
if(length(idx.upper.carma)!=0){
upper.carma<-as.numeric(upper[measure.par[idx.upper.carma]])
names(upper.carma)<-measure.par[idx.upper.carma]
}
}
}
lower.carma=NULL
if(!missing(lower)){
if(names(lower) %in% measure.par){
idx.lower.carma<-match(names(lower),measure.par)
idx.lower.carma<-idx.lower.carma[!is.na(idx.lower.carma)]
if(length(idx.lower.carma)!=0){
lower.carma<-as.numeric(lower[measure.par[idx.lower.carma]])
names(lower.carma)<-measure.par[idx.lower.carma]
}
}
}
for( j in c(1:length(measure.par))){
if(is.na(match(measure.par[j],names(fixed)))){
fixed.par <- c(fixed.par,measure.par[j])
fixed[measure.par[j]]<-start[measure.par[j]]
}
}
}
if (any(!(fixed.par %in% fullcoef)))
yuima.stop("Some named arguments in 'fixed' are not arguments to the supplied yuima model")
nm <- names(start)
oo <- match(nm, fullcoef)
if(any(is.na(oo)))
yuima.stop("some named arguments in 'start' are not arguments to the supplied yuima model")
start <- start[order(oo)]
nm <- names(start)
idx.diff <- match(diff.par, nm)
idx.drift <- match(drift.par, nm)
# SMI-2/9/14: idx.measure for CP
idx.measure <- match(measure.par, nm)
#01/01
if(is.CARMA(yuima)){
# if(length(yuima@model@[email protected])!=0){
idx.xinit <- as.integer(na.omit(match(xinit.par,nm)))# We need to add idx if NoNeg.Noise is TRUE
#}
}
#if(is.null(fixed.carma)){
idx.fixed <- match(fixed.par, nm)
# }else{
# dummynm <- nm[!(nm %in% fixed.par)]
# idx.fixed <- match(fixed.par, dummynm)
# }
orig.idx.fixed <- idx.fixed
tmplower <- as.list( rep( -Inf, length(nm)))
names(tmplower) <- nm
if(!missing(lower)){
idx <- match(names(lower), names(tmplower))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmplower[ idx ] <- lower
}
lower <- tmplower
tmpupper <- as.list( rep( Inf, length(nm)))
names(tmpupper) <- nm
if(!missing(upper)){
idx <- match(names(upper), names(tmpupper))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmpupper[ idx ] <- upper
}
upper <- tmpupper
d.size <- yuima@[email protected]
if (is.CARMA(yuima)){
# 24/12
d.size <-1
}
n <- length(yuima)[1]
env <- new.env(parent = envir) ##Kurisaki 4/4/2021
assign("X", as.matrix(onezoo(yuima)), envir=env)
assign("deltaX", matrix(0, n-1, d.size), envir=env)
# SMI-2/9/14: for CP
assign("Cn.r", numeric(n-1), envir=env)
if(length(yuima@[email protected]) == 0)
threshold <- 0 # there are no jumps, we take all observations
if (is.CARMA(yuima)){
#24/12 If we consider a carma model,
# the observations are only the first column of env$X
# assign("X", as.matrix(onezoo(yuima)), envir=env)
# env$X<-as.matrix(env$X[,1])
# assign("deltaX", matrix(0, n-1, d.size)[,1], envir=env)
env$X<-as.matrix(env$X[,1])
# env$X<-na.omit(as.matrix(env$X[,1]))
env$deltaX<-as.matrix(env$deltaX[,1])
assign("time.obs",length(env$X),envir=env)
# env$time.obs<-length(env$X)
#p <-yuima@model@info@p
assign("p", yuima@model@info@p, envir=env)
assign("q", yuima@model@info@q, envir=env)
assign("V_inf0", matrix(diag(rep(1,env$p)),env$p,env$p), envir=env)
# env$X<-as.matrix(env$X[,1])
# env$deltaX<-as.matrix(env$deltaX[,1])
# assign("time.obs",length(env$X), envir=env)
# p <-yuima@model@info@p
# assign("V_inf0", matrix(diag(rep(1,p)),p,p), envir=env)
}
assign("time", as.numeric(index(yuima@[email protected][[1]])), envir=env)
for(t in 1:(n-1)){
env$deltaX[t,] <- env$X[t+1,] - env$X[t,]
if(!is.CARMA(yuima))
env$Cn.r[t] <- ((sqrt( env$deltaX[t,] %*% env$deltaX[t,])) <= threshold)
}
if(length(yuima@[email protected]) == 0)
env$Cn.r <- rep(1, length(env$Cn.r)) # there are no jumps, we take all observations
assign("h", deltat(yuima@[email protected][[1]]), envir=env)
#SMI: 2/9/214 jump
if(length(yuima@[email protected]) > 0 && yuima@[email protected] == "CP"){
# "yuima.law" LM 13/05/2018
#if(class(yuima@model@measure$df)=="yuima.law"){
if(inherits(yuima@model@measure$df, "yuima.law")){ # YK, Mar. 22, 2022
args <- yuima@model@parameter@measure
}else{
args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1",yuima@model@measure$df$expr,perl=TRUE)), ","))
}
idx.intensity <- numeric(0)
if(length(measure.par) > 0){
for(i in 1:length(measure.par)){
if(sum(grepl(measure.par[i],yuima@model@measure$intensity)))
idx.intensity <- append(idx.intensity,i)
}
}
assign("idx.intensity", idx.intensity, envir=env)
assign("measure.var", args[1], envir=env)
}
f <- function(p) {
mycoef <- as.list(p)
if(!is.CARMA(yuima)){
if(length(c(idx.fixed,idx.measure))>0) ## SMI 2/9/14
names(mycoef) <- nm[-c(idx.fixed,idx.measure)] ## SMI 2/9/14
else
names(mycoef) <- nm
} else {
if(length(idx.fixed)>0)
names(mycoef) <- nm[-idx.fixed]
else
names(mycoef) <- nm
}
mycoef[fixed.par] <- fixed
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
# SMI-2/9/14:
fpsi <- function(p){
mycoef <- as.list(p)
idx.cont <- c(idx.diff,idx.drift)
if(length(c(idx.fixed,idx.cont))>0)
names(mycoef) <- nm[-c(idx.fixed,idx.cont)]
else
names(mycoef) <- nm
mycoef[fixed.par] <- fixed
# print(mycoef)
#print(p)
minusquasipsi(yuima=yuima, param=mycoef, print=print, env=env)
}
fj <- function(p) {
mycoef <- as.list(p)
# names(mycoef) <- nm
if(!is.CARMA(yuima)){
idx.fixed <- orig.idx.fixed
if(length(c(idx.fixed,idx.measure))>0) ## SMI 2/9/14
names(mycoef) <- nm[-c(idx.fixed,idx.measure)] ## SMI 2/9/14
else
names(mycoef) <- nm
} else {
names(mycoef) <- nm
mycoef[fixed.par] <- fixed
}
mycoef[fixed.par] <- fixed
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
oout <- NULL
HESS <- matrix(0, length(nm), length(nm))
colnames(HESS) <- nm
rownames(HESS) <- nm
HaveDriftHess <- FALSE
HaveDiffHess <- FALSE
HaveMeasHess <- FALSE
if(length(start)){
if(JointOptim){ ### joint optimization
old.fixed <- fixed
new.start <- start
old.start <- start
if(!is.CARMA(yuima)){
if(length(c(idx.fixed,idx.measure))>0)
new.start <- start[-c(idx.fixed,idx.measure)] # considering only initial guess for
}
if(length(new.start)>1){ #??multidimensional optim # Adjust lower for no negative Noise
if(is.CARMA(yuima) && (NoNeg.Noise==TRUE))
if(mean.noise %in% names(lower)){lower[mean.noise]<-10^-7}
oout <- optim(new.start, fj, method = method, hessian = TRUE, lower=lower, upper=upper)
if(length(fixed)>0)
oout$par[fixed.par]<- unlist(fixed)[fixed.par]
if(is.CARMA(yuima)){
HESS <- oout$hessian
} else {
HESS[names(new.start),names(new.start)] <- oout$hessian
}
if(is.CARMA(yuima) && length(yuima@model@[email protected])!=0){
b0<-paste0(yuima@model@[email protected],"0",collapse="")
idx.b0<-match(b0,rownames(HESS))
HESS<-HESS[-idx.b0,]
HESS<-HESS[,-idx.b0]
}
# if(is.CARMA(yuima) && length(yuima@model@parameter@measure)!=0){
# for(i in c(1:length(fixed.par))){
# indx.fixed<-match(fixed.par[i],rownames(HESS))
# HESS<-HESS[-indx.fixed,]
# HESS<-HESS[,-indx.fixed]
# }
# if(is.CARMA(yuima) && (NoNeg.Noise==TRUE)){
# idx.noise<-(match(mean.noise,rownames(HESS)))
# HESS<-HESS[-idx.noise,]
# HESS<-HESS[,-idx.noise]
# }
# }
if(is.CARMA(yuima)&& length(fixed)>0 && length(yuima@model@parameter@measure)==0){
for(i in c(1:length(fixed.par))){
indx.fixed<-match(fixed.par[i],rownames(HESS))
HESS<-HESS[-indx.fixed,]
HESS<-HESS[,-indx.fixed]
}
}
if(is.CARMA(yuima) && length(yuima@model@parameter@measure)!=0){
for(i in c(1:length(fixed.par))){
indx.fixed<-match(fixed.par[i],rownames(HESS))
HESS<-HESS[-indx.fixed,]
HESS<-HESS[,-indx.fixed]
}
if(is.CARMA(yuima) && (NoNeg.Noise==TRUE)){
idx.noise<-(match(mean.noise,rownames(HESS)))
HESS<-HESS[-idx.noise,]
HESS<-HESS[,-idx.noise]
}
}
HaveDriftHess <- TRUE
HaveDiffHess <- TRUE
} else { ### one dimensional optim
# YK Mar. 13, 2021: bug fixed
#opt1 <- optimize(f, ...) ## an interval should be provided
#oout <- list(par = opt1$minimum, value = opt1$objective)
opt1 <- optimize(f, lower = lower[[names(new.start)]],
upper = upper[[names(new.start)]], ...)
oout <- list(par = opt1$minimum, value = opt1$objective)
names(oout$par) <- names(new.start)
} ### endif( length(start)>1 )
theta1 <- oout$par[diff.par]
theta2 <- oout$par[drift.par]
} else { ### first diffusion, then drift
theta1 <- NULL
old.fixed <- fixed
old.start <- start
if(length(idx.diff)>0){
## DIFFUSION ESTIMATIOn first
old.fixed <- fixed
old.start <- start
old.fixed.par <- fixed.par
new.start <- start[idx.diff] # considering only initial guess for diffusion
new.fixed <- fixed
if(length(idx.drift)>0)
new.fixed[nm[idx.drift]] <- start[idx.drift]
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.diff]
mydots <- as.list(call)[-(1:2)]
mydots$print <- NULL
mydots$rcpp <- NULL #KK 08/07/16
mydots$fixed <- NULL
mydots$fn <- as.name("f")
mydots$start <- NULL
mydots$par <- unlist(new.start)
mydots$hessian <- FALSE
mydots$upper <- as.numeric(unlist( upper[ nm[idx.diff] ]))
mydots$lower <- as.numeric(unlist( lower[ nm[idx.diff] ]))
mydots$joint <- NULL # LM 08/03/16
mydots$aggregation <- NULL # LM 08/03/16
mydots$threshold <- NULL #SMI 2/9/14
mydots$envir <- NULL ##Kurisaki 4/4/2021
mydots$Est.Incr <- NULL ##Kurisaki 4/10/2021
mydots$print <- NULL ##Kito 4/17/2021
mydots$aggregation <- NULL ##Kito 4/17/2021
mydots$rcpp <- NULL ##Kito 4/17/2021
if((length(mydots$par)>1) | any(is.infinite(c(mydots$upper,mydots$lower)))){
mydots$method<-method ##song
oout <- do.call(optim, args=mydots)
} else {
mydots$f <- mydots$fn
mydots$fn <- NULL
mydots$par <- NULL
mydots$hessian <- NULL
mydots$interval <- as.numeric(c(unlist(lower[diff.par]),unlist(upper[diff.par])))
mydots$lower <- NULL
mydots$upper <- NULL
mydots$method<- NULL
mydots$envir <- NULL ##Kurisaki 4/4/2021
mydots$Est.Incr <- NULL ##Kurisaki 4/8/2021
mydots$print <- NULL ##Kito 4/17/2021
mydots$aggregation <- NULL ##Kito 4/17/2021
mydots$rcpp <- NULL ##Kito 4/17/2021
opt1 <- do.call(optimize, args=mydots)
theta1 <- opt1$minimum
names(theta1) <- diff.par
oout <- list(par = theta1, value = opt1$objective)
}
theta1 <- oout$par
fixed <- old.fixed
start <- old.start
fixed.par <- old.fixed.par
} ## endif(length(idx.diff)>0)
theta2 <- NULL
if(length(idx.drift)>0){
## DRIFT estimation with first state diffusion estimates
fixed <- old.fixed
start <- old.start
old.fixed.par <- fixed.par
new.start <- start[idx.drift] # considering only initial guess for drift
new.fixed <- fixed
new.fixed[names(theta1)] <- theta1
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.drift]
mydots <- as.list(call)[-(1:2)]
mydots$print <- NULL
mydots$rcpp <- NULL #KK 08/07/16
mydots$fixed <- NULL
mydots$fn <- as.name("f")
mydots$threshold <- NULL #SMI 2/9/14
mydots$start <- NULL
mydots$par <- unlist(new.start)
mydots$hessian <- FALSE
mydots$upper <- unlist( upper[ nm[idx.drift] ])
mydots$lower <- unlist( lower[ nm[idx.drift] ])
mydots$joint <- NULL # LM 08/03/16
mydots$aggregation <- NULL # LM 08/03/16# LM 08/03/16
mydots$envir <- NULL ##Kurisaki 4/4/2021
mydots$Est.Incr <- NULL ##Kurisaki 4/8/2021
mydots$print <- NULL ##Kito 4/17/2021
mydots$aggregation <- NULL ##Kito 4/17/2021
mydots$rcpp <- NULL ##Kito 4/17/2021
if(length(mydots$par)>1 | any(is.infinite(c(mydots$upper,mydots$lower)))){
if(is.CARMA(yuima)){
if(NoNeg.Noise==TRUE){
if((yuima@model@info@q+1)==yuima@model@info@p){
mydots$lower[names(start["NoNeg.Noise"])]<-10^(-7)
}
}
if(length(yuima@model@[email protected])!=0){
name_b0<-paste0(yuima@model@[email protected],"0",collapse="")
index_b0<-match(name_b0,nm)
mydots$lower[index_b0]<-1
mydots$upper[index_b0]<-1+10^(-7)
}
if (length(yuima@model@[email protected])!=0){
mydots$upper <- unlist( upper[ nm ])
mydots$lower <- unlist( lower[ nm ])
idx.tot<-unique(c(idx.drift,idx.xinit))
new.start <- start[idx.tot]
names(new.start) <- nm[idx.tot]
mydots$par <- unlist(new.start)
}
} # END if(is.CARMA)
mydots$method <- method #song
oout1 <- do.call(optim, args=mydots)
# oout1 <- optim(mydots$par,f,method = "L-BFGS-B" , lower = mydots$lower, upper = mydots$upper)
} else {
mydots$f <- mydots$fn
mydots$fn <- NULL
mydots$par <- NULL
mydots$hessian <- NULL
mydots$method<-NULL
mydots$interval <- as.numeric(c(lower[drift.par],upper[drift.par]))
mydots$envir <- NULL ##Kurisaki 4/4/2021
mydots$Est.Incr <- NULL ##Kurisaki 4/8/2021
mydots$print <- NULL ##Kito 4/17/2021
mydots$aggregation <- NULL ##Kito 4/17/2021
mydots$rcpp <- NULL ##Kito 4/17/2021
opt1 <- do.call(optimize, args=mydots)
theta2 <- opt1$minimum
names(theta2) <- drift.par
oout1 <- list(par = theta2, value = as.numeric(opt1$objective))
}
theta2 <- oout1$par
fixed <- old.fixed
start <- old.start
old.fixed.par <- fixed.par
} ## endif(length(idx.drift)>0)
oout1 <- list(par= c(theta1, theta2))
if (! is.CARMA(yuima)){
if(length(c(diff.par, diff.par))>0)
names(oout1$par) <- c(diff.par,drift.par)
}
oout <- oout1
} ### endif JointOptim
} else {
list(par = numeric(0L), value = f(start))
}
fMeas <- function(p) {
mycoef <- as.list(p)
# if(! is.CARMA(yuima)){
# # names(mycoef) <- drift.par
# mycoef[measure.par] <- coef[measure.par]
#}
minusquasipsi(yuima=yuima, param=mycoef, print=print, env=env)
# minusquasilogl(yuima=yuima, param=mycoef, print=print, env)
}
fDrift <- function(p) {
mycoef <- as.list(p)
if(! is.CARMA(yuima)){
names(mycoef) <- drift.par
mycoef[diff.par] <- coef[diff.par]
}
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
fDiff <- function(p) {
mycoef <- as.list(p)
if(! is.CARMA(yuima)){
names(mycoef) <- diff.par
mycoef[drift.par] <- coef[drift.par]
}
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
# coef <- oout$par
#control=list()
#par <- coef
#names(par) <- unique(c(diff.par, drift.par))
# nm <- unique(c(diff.par, drift.par))
# START: ESTIMATION OF CP part
theta3 <- NULL
if(length(idx.measure)>0 & !is.CARMA(yuima)){
idx.cont <- c(idx.drift,idx.diff)
fixed <- old.fixed
start <- old.start
old.fixed.par <- fixed.par
new.fixed <- fixed
new.start <- start[idx.measure] # considering only initial guess for measure
new.fixed <- fixed
new.fixed[names(theta1)] <- theta1
new.fixed[names(theta2)] <- theta2
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
# names(new.start) <- nm[idx.drift]
names(new.start) <- nm[idx.measure]
mydots <- as.list(call)[-(1:2)]
# mydots$print <- NULL
mydots$threshold <- NULL
mydots$fixed <- NULL
mydots$fn <- as.name("fpsi")
mydots$start <- NULL
mydots$threshold <- NULL #SMI 2/9/14
mydots$envir <- NULL ##Kurisaki 4/4/2021
mydots$Est.Incr <- NULL ##Kurisaki 4/8/2021
mydots$print <- NULL ##Kito 4/17/2021
mydots$aggregation <- NULL ##Kito 4/17/2021
mydots$rcpp <- NULL ##Kito 4/17/2021
mydots$par <- unlist(new.start)
mydots$hessian <- TRUE
mydots$joint <- NULL
mydots$upper <- unlist( upper[ nm[idx.measure] ])
mydots$lower <- unlist( lower[ nm[idx.measure] ])
mydots$method <- method
oout3 <- do.call(optim, args=mydots)
theta3 <- oout3$par
#print(theta3)
HESS[measure.par,measure.par] <- oout3$hessian
HaveMeasHess <- TRUE
fixed <- old.fixed
start <- old.start
fixed.par <- old.fixed.par
}
# END: ESTIMATION OF CP part
if(!is.CARMA(yuima)){
oout4 <- list(par= c(theta1, theta2, theta3))
names(oout4$par) <- c(diff.par,drift.par,measure.par)
oout <- oout4
}
coef <- oout$par
control=list()
par <- coef
if(!is.CARMA(yuima)){
names(par) <- unique(c(diff.par, drift.par,measure.par))
nm <- unique(c(diff.par, drift.par,measure.par))
} else {
names(par) <- unique(c(diff.par, drift.par))
nm <- unique(c(diff.par, drift.par))
}
#return(oout)
if(is.CARMA(yuima) && length(yuima@model@parameter@measure)!=0){
nm <-c(nm,measure.par)
if((NoNeg.Noise==TRUE)){nm <-c(nm,mean.noise)}
nm<-unique(nm)
}
if(is.CARMA(yuima) && (length(yuima@model@[email protected])!=0)){
nm <-unique(c(nm,yuima@model@[email protected]))
}
conDrift <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(drift.par)),
ndeps = rep.int(0.001, length(drift.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
conDiff <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(diff.par)),
ndeps = rep.int(0.001, length(diff.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
conMeas <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(measure.par)),
ndeps = rep.int(0.001, length(measure.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
if(is.CARMA(yuima) && length(yuima@model@[email protected])!=0 ){
conDrift <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(c(drift.par,yuima@model@[email protected]))),
ndeps = rep.int(0.001, length(c(drift.par,yuima@model@[email protected]))),
maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
conDiff <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(diff.par)),
ndeps = rep.int(0.001, length(diff.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
}
if(!HaveDriftHess & (length(drift.par)>0)){
#hess2 <- .Internal(optimhess(coef[drift.par], fDrift, NULL, conDrift))
if(!is.CARMA(yuima)){
hess2 <- optimHess(coef[drift.par], fDrift, NULL, control=conDrift)
HESS[drift.par,drift.par] <- hess2
} else{
names(coef) <- c(drift.par,yuima@model@[email protected])
hess2 <- optimHess(coef, fDrift, NULL, control=conDrift)
HESS <- hess2
}
if(is.CARMA(yuima) && length(yuima@model@[email protected])!=0){
b0<-paste0(yuima@model@[email protected],"0",collapse="")
idx.b0<-match(b0,rownames(HESS))
HESS<-HESS[-idx.b0,]
HESS<-HESS[,-idx.b0]
}
}
if(!HaveDiffHess & (length(diff.par)>0)){
hess1 <- optimHess(coef[diff.par], fDiff, NULL, control=conDiff)
HESS[diff.par,diff.par] <- hess1
}
oout$hessian <- HESS
if(!HaveMeasHess & (length(measure.par) > 0) & !is.CARMA(yuima)){
hess1 <- optimHess(coef[measure.par], fMeas, NULL, control=conMeas)
oout$hessian[measure.par,measure.par] <- hess1
}
# vcov <- if (length(coef))
# solve(oout$hessian)
# else matrix(numeric(0L), 0L, 0L)
vcov <- matrix(NA, length(coef), length(coef))
if (length(coef)) {
rrr <- try(solve(oout$hessian), TRUE)
if(class(rrr)[1] != "try-error")
vcov <- rrr
}
mycoef <- as.list(coef)
if(!is.CARMA(yuima)){
names(mycoef) <- nm
}
idx.fixed <- orig.idx.fixed
mycoef.cont <- mycoef
if(length(c(idx.fixed,idx.measure)>0)) # SMI 2/9/14
mycoef.cont <- mycoef[-c(idx.fixed,idx.measure)] # SMI 2/9/14
min.diff <- 0
min.jump <- 0
if(length(c(diff.par,drift.par))>0 & !is.CARMA(yuima)){ # LM 04/09/14
min.diff <- minusquasilogl(yuima=yuima, param=mycoef[c(diff.par,drift.par)], print=print, env,rcpp=rcpp)
}else{
if(length(c(diff.par,drift.par))>0 & is.CARMA(yuima)){
min.diff <- minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
}
if(length(c(measure.par))>0 & !is.CARMA(yuima))
min.jump <- minusquasipsi(yuima=yuima, param=mycoef[measure.par], print=print, env=env)
min <- min.diff + min.jump
if(min==0)
min <- NA
dummycov<-matrix(0,length(coef),length(coef))
rownames(dummycov)<-names(coef)
colnames(dummycov)<-names(coef)
dummycov[rownames(vcov),colnames(vcov)]<-vcov
vcov<-dummycov
# new("mle", call = call, coef = coef, fullcoef = unlist(mycoef),
# vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
# method = method)
#LM 11/01
if(!is.CARMA(yuima)){
if(length(yuima@[email protected]) > 0 && yuima@[email protected] == "CP"){
final_res<-new("yuima.CP.qmle",
Jump.times=env$time[env$Cn.r==0],
Jump.values=env$deltaX[env$Cn.r==0,],
X.values=env$X[env$Cn.r==0,],
model=yuima@model,
call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs, threshold=threshold)
} else {
final_res<-new("yuima.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs, model=yuima@model)
}
} else {
if( Est.Incr=="IncrPar" || Est.Incr=="Incr" ){
final_res<-new("yuima.carma.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs, logL.Incr = NULL)
}else{
if(Est.Incr=="NoIncr"){
final_res<-new("yuima.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs , model=yuima@model)
return(final_res)
}else{
yuima.warn("The variable Est.Incr is not correct. See qmle documentation for the allowed values ")
final_res<-new("mle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs)
return(final_res)
}
}
}
if(!is.CARMA(yuima)){
return(final_res)
}else {
param<-coef(final_res)
observ<-yuima@data
model<-yuima@model
info<-model@info
numb.ar<-info@p
name.ar<-paste([email protected],c(numb.ar:1),sep="")
ar.par<-param[name.ar]
numb.ma<-info@q
name.ma<-paste([email protected],c(0:numb.ma),sep="")
ma.par<-param[name.ma]
loc.par=NULL
if (length([email protected])!=0){
loc.par<-param[[email protected]]
}
scale.par=NULL
if (length([email protected])!=0){
scale.par<-param[[email protected]]
}
lin.par=NULL
if (length([email protected])!=0){
lin.par<-param[[email protected]]
}
if(min(yuima.PhamBreton.Alg(ar.par[numb.ar:1]))>=0){
cat("\n Stationarity condition is satisfied...\n Starting Estimation Increments ...\n")
}else{
yuima.warn("Insert constraints in Autoregressive parameters for enforcing stationarity" )
cat("\n Starting Estimation Increments ...\n")
}
ttt<[email protected][[1]]
tt<-index(ttt)
y<-coredata(ttt)
if(NoNeg.Noise==TRUE && (info@p==(info@q+1))){final_res@coef[mean.noise]<-mean(y)/tail(ma.par,n=1)*ar.par[1]}
levy<-yuima.CarmaNoise(y,tt,ar.par,ma.par, loc.par, scale.par, lin.par, NoNeg.Noise)
inc.levy<-NULL
if (!is.null(levy)){
inc.levy<-diff(t(levy))
}
# INSERT HERE THE NECESSARY STEPS FOR FINDING THE PARAMETERS OF LEVY
if(Est.Incr=="Carma.Inc"||Est.Incr=="Incr"){
# inc.levy.fin<-zoo(inc.levy,tt,frequency=1/env$h)
inc.levy.fin<-zoo(inc.levy,tt[(1+length(tt)-length(inc.levy)):length(tt)])
carma_final_res<-new("yuima.carma.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, Incr.Lev = inc.levy.fin,
model = yuima@model, nobs=yuima.nobs, logL.Incr = NULL)
return(carma_final_res)
}
cat("\nStarting Estimation parameter Noise ...\n")
dummycovCarmapar<-vcov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]
if(!is.null(loc.par)){
dummycovCarmapar<-vcov[unique(c(drift.par,diff.par,[email protected])),
unique(c(drift.par,diff.par,[email protected]))]
}
dummycovCarmaNoise<-vcov[unique(measure.par),unique(c(measure.par))] #we need to adjusted
dummycoeffCarmapar<-coef[unique(c(drift.par,diff.par))]
if(!is.null(loc.par)){
dummycoeffCarmapar<-coef[unique(c(drift.par,diff.par,[email protected]))]
}
dummycoeffCarmaNoise<-coef[unique(c(measure.par))]
coef<-NULL
coef<-c(dummycoeffCarmapar,dummycoeffCarmaNoise)
names.par<-c(unique(c(drift.par,diff.par)),unique(c(measure.par)))
if(!is.null(loc.par)){
names.par<-c(unique(c(drift.par,diff.par,[email protected])),unique(c(measure.par)))
}
names(coef)<-names.par
cov<-NULL
cov<-matrix(0,length(names.par),length(names.par))
rownames(cov)<-names.par
colnames(cov)<-names.par
if(is.null(loc.par)){
cov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]<-dummycovCarmapar
}else{
cov[unique(c(drift.par,diff.par,[email protected])),unique(c(drift.par,diff.par,[email protected]))]<-dummycovCarmapar
}
cov[unique(c(measure.par)),unique(c(measure.par))]<-dummycovCarmaNoise
if(length([email protected])!=0){
if([email protected]=="CP"){
name.func.dummy <- as.character(model@measure$df$expr[1])
name.func<- substr(name.func.dummy,1,(nchar(name.func.dummy)-1))
names.measpar<-as.vector(strsplit(name.func,', '))[[1]][-1]
valuemeasure<-as.numeric(names.measpar)
name.int.dummy <- as.character(model@measure$intensity)
valueintensity<-as.numeric(name.int.dummy)
NaIdx<-which(!is.na(c(valueintensity,valuemeasure)))
if(length(NaIdx)!=0){
yuima.warn("the constrained MLE for levy increment will be implemented as soon as possible")
carma_final_res<-new("yuima.carma.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, Incr.Lev = inc.levy,
model = yuima@model, logL.Incr = NULL)
return(carma_final_res)
}
if(aggregation==TRUE){
if(floor(yuima@sampling@n/yuima@sampling@Terminal)!=yuima@sampling@n/yuima@sampling@Terminal){
yuima.stop("the n/Terminal in sampling information is not an integer. Set Aggregation=FALSE")
}
inc.levy1<-diff(cumsum(c(0,inc.levy))[seq(from=1,
to=yuima@sampling@n[1],
by=(yuima@sampling@n/yuima@sampling@Terminal)[1]
)])
}else{
inc.levy1<-inc.levy
}
names.measpar<-c(name.int.dummy, names.measpar)
if(measurefunc=="dnorm"){
# result.Lev<-yuima.Estimation.CPN(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
}
if(measurefunc=="dgamma"){
# result.Lev<-yuima.Estimation.CPGam(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
}
if(measurefunc=="dexp"){
# result.Lev<-yuima.Estimation.CPExp(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
}
Inc.Parm<-result.Lev$estLevpar
IncVCOV<-result.Lev$covLev
names(Inc.Parm)[NaIdx]<-measure.par
rownames(IncVCOV)[NaIdx]<-as.character(measure.par)
colnames(IncVCOV)[NaIdx]<-as.character(measure.par)
coef<-NULL
coef<-c(dummycoeffCarmapar,Inc.Parm)
names.par<-names(coef)
cov<-NULL
cov<-matrix(0,length(names.par),length(names.par))
rownames(cov)<-names.par
colnames(cov)<-names.par
if(is.null(loc.par)){
cov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]<-dummycovCarmapar
}else{
cov[unique(c(drift.par,diff.par,[email protected])),unique(c(drift.par,diff.par,[email protected]))]<-dummycovCarmapar
}
cov[names(Inc.Parm),names(Inc.Parm)]<-IncVCOV
}
if(yuima@[email protected]=="code"){
# # "rIG", "rNIG", "rgamma", "rbgamma", "rvgamma"
#if(class(model@measure$df)=="yuima.law"){
if(inherits(model@measure$df, "yuima.law")){ # YK, Mar. 22, 2022
valuemeasure <- "yuima.law"
NaIdx<-NULL
}else{
name.func.dummy <- as.character(model@measure$df$expr[1])
name.func<- substr(name.func.dummy,1,(nchar(name.func.dummy)-1))
names.measpar<-as.vector(strsplit(name.func,', '))[[1]][-1]
valuemeasure<-as.numeric(names.measpar)
NaIdx<-which(!is.na(valuemeasure))
}
if(length(NaIdx)!=0){
yuima.warn("the constrained MLE for levy increment will be implemented as soon as possible")
carma_final_res<-new("yuima.carma.qmle", call = call, coef = coef, fullcoef = unlist(mycoef),
vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, Incr.Lev = inc.levy,
model = yuima@model, logL.Incr = NULL)
return(carma_final_res)
}
if(aggregation==TRUE){
if(all(floor(yuima@sampling@n/yuima@sampling@Terminal)!=yuima@sampling@n/yuima@sampling@Terminal)){
yuima.stop("the n/Terminal in sampling information is not an integer. Aggregation=FALSE is recommended")
}
inc.levy1<-diff(cumsum(c(0,inc.levy))[seq(from=1,
to=yuima@sampling@n[1],
by=(yuima@sampling@n/yuima@sampling@Terminal)[1]
)])
}else{
inc.levy1<-inc.levy
}
if(measurefunc=="yuima.law"){
dummyParMeas<-c(coef[measure.par],1)
names(dummyParMeas)<-c(measure.par,yuima@[email protected])
cond <- length(dens(yuima@model@measure$df,x=as.numeric(inc.levy1),param=as.list(dummyParMeas)))
if(cond==0){
result.Lev <- list(estLevpar=coef[measure.par],
covLev=matrix(NA,
length(coef[measure.par]),
length(coef[measure.par]))
)
yuima.warn("Levy measure parameters can not be estimated.")
}else{
dummyMyfunMeas<-function(par, Law, Data, time, param.name, name.time){
dummyParMeas<-c(par,time)
names(dummyParMeas)<-c(param.name,name.time)
v <- log(pmax(na.omit(dens(Law,x=Data,param=as.list(dummyParMeas))), 10^(-40)))
v1 <- v[!is.infinite(v)]
return(-sum(v1))
#-sum(dens(Law,x=Data,param=as.list(dummyParMeas),log = TRUE),na.rm=TRUE)
}
# aa <- dummyMyfunMeas(par=coef[measure.par], Law=yuima@model@measure$df,
# Data=as.numeric(inc.levy),
# time=yuima@sampling@delta, param.name=measure.par,
# name.time = yuima@[email protected])
if(aggregation){
mytime<-1
}else{
mytime<-yuima@sampling@delta
inc.levy1<- as.numeric(inc.levy1)
}
prova <- optim(fn = dummyMyfunMeas, par = coef[measure.par],
method = method,Law=yuima@model@measure$df,
Data=inc.levy1,
time=mytime, param.name=measure.par,
name.time = yuima@[email protected])
Heeee<-optimHess(fn = dummyMyfunMeas, par = coef[measure.par],
Law=yuima@model@measure$df,
Data=inc.levy1,
time=mytime, param.name=measure.par,
name.time = yuima@[email protected])
result.Lev <- list(estLevpar=prova$par,covLev=solve(Heeee))
}
}
if(measurefunc=="rIG"){
# result.Lev<-list(estLevpar=coef[ names.measpar],
# covLev=matrix(NA,
# length(coef[ names.measpar]),
# length(coef[ names.measpar]))
# )
# result.Lev<-yuima.Estimation.IG(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
# result.Levy<-gigFit(inc.levy)
# Inc.Parm<-coef(result.Levy)
# IncVCOV<--solve(gigHessian(inc.levy, param=Inc.Parm))
}
if(measurefunc=="rNIG"){
# result.Lev<-yuima.Estimation.NIG(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
}
if(measurefunc=="rbgamma"){
result.Lev<-list(estLevpar=coef[ names.measpar],
covLev=matrix(NA,
length(coef[ names.measpar]),
length(coef[ names.measpar]))
)
}
if(measurefunc=="rvgamma"){
# result.Lev<-yuima.Estimation.VG(Increment.lev=inc.levy1,param0=coef[ names.measpar],
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma)
result.Lev<-yuima.Estimation.Lev(Increment.lev=inc.levy1,
param0=coef[ names.measpar],
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measurefunc,
[email protected],
dt=env$h,
aggregation=aggregation)
}
Inc.Parm<-result.Lev$estLevpar
IncVCOV<-result.Lev$covLev
names(Inc.Parm)[NaIdx]<-measure.par
rownames(IncVCOV)[NaIdx]<-as.character(measure.par)
colnames(IncVCOV)[NaIdx]<-as.character(measure.par)
coef<-NULL
coef<-c(dummycoeffCarmapar,Inc.Parm)
names.par<-names(coef)
cov<-NULL
cov<-matrix(0,length(names.par),length(names.par))
rownames(cov)<-names.par
colnames(cov)<-names.par
if(is.null(loc.par)){
cov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]<-dummycovCarmapar
}else{
cov[unique(c(drift.par,diff.par,[email protected])),unique(c(drift.par,diff.par,[email protected]))]<-dummycovCarmapar
}
cov[names(Inc.Parm),names(Inc.Parm)]<-IncVCOV
}
}
# dummycovCarmapar<-vcov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]
# dummycovCarmaNoise<-vcov[unique(measure.par),unique(c(measure.par))] #we need to adjusted
# dummycoeffCarmapar<-coef[unique(c(drift.par,diff.par))]
# dummycoeffCarmaNoise<-coef[unique(c(measure.par))]
# coef<-NULL
# coef<-c(dummycoeffCarmapar,dummycoeffCarmaNoise)
# names.par<-c(unique(c(drift.par,diff.par)),unique(c(measure.par)))
# names(coef)<-names.par
# cov<-NULL
# cov<-matrix(0,length(names.par),length(names.par))
# rownames(cov)<-names.par
# colnames(cov)<-names.par
# cov[unique(c(drift.par,diff.par)),unique(c(drift.par,diff.par))]<-dummycovCarmapar
# cov[unique(c(measure.par)),unique(c(measure.par))]<-dummycovCarmaNoise
# carma_final_res<-list(mle=final_res,Incr=inc.levy,model=yuima)
if(Est.Incr=="Carma.IncPar"||Est.Incr=="IncrPar"){
#inc.levy.fin<-zoo(inc.levy,tt,frequency=1/env$h)
inc.levy.fin<-zoo(inc.levy,tt[(1+length(tt)-length(inc.levy)):length(tt)])
carma_final_res<-new("yuima.carma.qmle", call = call, coef = coef, fullcoef = unlist(coef),
vcov = cov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, Incr.Lev = inc.levy.fin,
model = yuima@model, nobs=yuima.nobs,
logL.Incr = tryCatch(-result.Lev$value,error=function(theta){NULL}))
}else{
if(Est.Incr=="Carma.Par"||Est.Incr=="NoIncr"){
carma_final_res<-new("mle", call = call, coef = coef, fullcoef = unlist(coef),
vcov = cov, min = min, details = oout, minuslogl = minusquasilogl,
method = method, nobs=yuima.nobs)
}
}
return(carma_final_res)
}
}
# SMI-2/9/14 CP
minusquasipsi <- function(yuima, param, print=FALSE, env){
idx.intensity <- env$idx.intensity
fullcoef <- yuima@model@parameter@all
measurecoef <- param[unique(c(yuima@model@parameter@measure,yuima@model@parameter@jump))]
#print(measurecoef)
#cat("\n***\n")
npar <- length(fullcoef)
nm <- names(param)
oo <- match(nm, fullcoef)
#print(param)
#cat("\n***\n")
#print(fullcoef)
#cat("\n***\n")
if(any(is.na(oo)))
yuima.stop("some named arguments in 'param' are not arguments to the supplied yuima model")
param <- param[order(oo)]
h <- env$h
Dn.r <- !env$Cn.r
# if(length(idx.intensity)){
# intensity <- unlist(measurecoef[idx.intensity])
#}else{
# intensity <- eval(yuima@model@measure$intensity, envir=env)
#}
# print(intensity)
#print(str(env$time))
# tmp.env <- new.env()
#for(i in 1:length(param)){
# assign(names(param)[i],param[[i]],envir=tmp.env)
#}
#print(ls(env))
d.size <- yuima@[email protected]
n <- length(yuima)[1]
myidx <- which(Dn.r)[-n]
measure <- measure.term(yuima, param, env)
QL <- 0
dx <- env$deltaX
measure.var <- env$measure.var
for(i in 1:length(measurecoef))
#if(!is.Poisson(yuima)){
# if(is.na(match(i,idx.intensity)))
# assign(names(measurecoef)[i],measurecoef[i][[1]], envir=env)
# } else {
assign(names(measurecoef)[i],measurecoef[i][[1]], envir=env)
# }
# print("### ls(env)")
# print(ls(env))
if(is.null(dim(measure[,,1]))){ # one-dimensional
for(t in myidx){
iC <- 1/measure[, , t]
assign(measure.var,iC%*%dx[t,],envir=env)
assign(yuima@[email protected], env$time[t], envir=env)
# print("### t")
#print(t)
#print(env$time[t])
intensity <- eval(yuima@model@measure$intensity, envir=env)
#print("intensity")
#print(intensity)
dF <- intensity*eval(yuima@model@measure$df$expr,envir=env)/iC
logpsi <- 0
if(dF>0)
logpsi <- log(dF)
QL <- QL + logpsi
}
} else {
for(t in myidx){
iC <- solve(measure[, , t])
assign(measure.var,iC%*%dx[t,], envir=env)
assign(yuima@[email protected], env$time[t], envir=env)
intensity <- eval(yuima@model@measure$intensity, envir=env)
dF <- intensity*eval(yuima@model@measure$df$expr,envir=env)*det(iC)
logpsi <- 0
if(dF>0)
logpsi <- log(dF)
QL <- QL + logpsi
}
}
myf <- function(x) {
f1 <- function(u){
assign(yuima@[email protected], u, envir=env)
intensity <- eval(yuima@model@measure$intensity, envir=env)
}
sapply(x, f1)
}
# print(myf(1))
# print(str( try(integrate(f=myf, lower=yuima@sampling@Initial, upper=yuima@sampling@Terminal,subdivisions=100),silent=TRUE )))
myint <- integrate(f=myf, lower=yuima@sampling@Initial, upper=yuima@sampling@Terminal,subdivisions=100)$value
# print(myint)
#print(-h*intensity*(n-1))
# QL <- QL -h*intensity*(n-1)
QL <- QL -myint
if(!is.finite(QL)){
yuima.warn("quasi likelihood is too small to calculate.")
return(1e10)
}
if(print==TRUE){
yuima.warn(sprintf("NEG-QL: %f, %s", -QL, paste(names(param),param,sep="=",collapse=", ")))
}
if(is.infinite(QL)) return(1e10)
return(as.numeric(-QL))
}
quasilogl <- function(yuima, param, print=FALSE,rcpp=FALSE){
d.size <- yuima@[email protected]
if (is(yuima@model, "yuima.carma")){
# 24/12
d.size <-1
}
n <- length(yuima)[1]
env <- new.env()
assign("X", as.matrix(onezoo(yuima)), envir=env)
assign("deltaX", matrix(0, n-1, d.size), envir=env)
assign("Cn.r", rep(1,n-1), envir=env)
if(is.CARMA(yuima)){
env$X<-as.matrix(env$X[,1])
env$deltaX<-as.matrix(env$deltaX[,1])
env$time.obs<-length(env$X)
assign("p", yuima@model@info@p, envir=env)
assign("q", yuima@model@info@q, envir=env)
assign("V_inf0", matrix(diag(rep(1,env$p)),env$p,env$p), envir=env)
}
for(t in 1:(n-1))
env$deltaX[t,] <- env$X[t+1,] - env$X[t,]
assign("h", deltat(yuima@[email protected][[1]]), envir=env)
assign("time", as.numeric(index(yuima@[email protected][[1]])), envir=env)
-minusquasilogl(yuima=yuima, param=param, print=print, env,rcpp=rcpp)
}
minusquasilogl <- function(yuima, param, print=FALSE, env,rcpp=FALSE){
diff.par <- yuima@model@parameter@diffusion
drift.par <- yuima@model@parameter@drift
if(is.CARMA(yuima)){
if(length(yuima@model@[email protected])!=0){
xinit.par <- yuima@model@parameter@xinit
}
}
if(is.CARMA(yuima) && length(yuima@model@[email protected])==0
&& length(yuima@model@parameter@jump)!=0){
diff.par<-yuima@model@parameter@jump
# measure.par<-yuima@model@parameter@measure
}
if(is.CARMA(yuima) && length(yuima@model@[email protected])==0
&& length(yuima@model@parameter@measure)!=0){
measure.par<-yuima@model@parameter@measure
}
# 24/12
if(is.CARMA(yuima) && length(yuima@model@[email protected])>0 ){
yuima.warn("carma(p,q): the case of lin.par will be implemented as soon as")
return(NULL)
}
if(is.CARMA(yuima)){
xinit.par <- yuima@model@parameter@xinit
}
drift.par <- yuima@model@parameter@drift
fullcoef <- NULL
if(length(diff.par)>0)
fullcoef <- diff.par
if(length(drift.par)>0)
fullcoef <- c(fullcoef, drift.par)
if(is.CARMA(yuima)){
if(length(xinit.par)>0)
fullcoef <- c(fullcoef, xinit.par)
}
if(is.CARMA(yuima) && (length(yuima@model@parameter@measure)!=0))
fullcoef<-c(fullcoef, measure.par)
if(is.CARMA(yuima)){
if("mean.noise" %in% names(param)){
mean.noise<-"mean.noise"
fullcoef <- c(fullcoef, mean.noise)
NoNeg.Noise<-TRUE
}
}
npar <- length(fullcoef)
nm <- names(param)
oo <- match(nm, fullcoef)
if(any(is.na(oo)))
yuima.stop("some named arguments in 'param' are not arguments to the supplied yuima model")
param <- param[order(oo)]
nm <- names(param)
idx.diff <- match(diff.par, nm)
idx.drift <- match(drift.par, nm)
if(is.CARMA(yuima)){
idx.xinit <-as.integer(na.omit(match(xinit.par, nm)))
}
h <- env$h
Cn.r <- env$Cn.r
theta1 <- unlist(param[idx.diff])
theta2 <- unlist(param[idx.drift])
n.theta1 <- length(theta1)
n.theta2 <- length(theta2)
n.theta <- n.theta1+n.theta2
if(is.CARMA(yuima)){
theta3 <- unlist(param[idx.xinit])
n.theta3 <- length(theta3)
n.theta <- n.theta1+n.theta2+n.theta3
}
d.size <- yuima@[email protected]
n <- length(yuima)[1]
if (is.CARMA(yuima)){
# 24/12
d.size <-1
# We build the two step procedure as described in
# if(length(yuima@model@[email protected])!=0){
prova<-as.numeric(param)
#names(prova)<-fullcoef[oo]
names(prova)<-names(param)
param<-prova[c(length(prova):1)]
time.obs<-env$time.obs
y<-as.numeric(env$X)
u<-env$h
p<-env$p
q<-env$q
# p<-yuima@model@info@p
ar.par <- yuima@model@[email protected]
name.ar<-paste0(ar.par, c(1:p))
# q <- yuima@model@info@q
ma.par <- yuima@model@[email protected]
name.ma<-paste0(ma.par, c(0:q))
if (length(yuima@model@[email protected])==0){
a<-param[name.ar]
# a_names<-names(param[c(1:p)])
# names(a)<-a_names
b<-param[name.ma]
# b_names<-names(param[c((p+1):(length(param)-p+1))])
# names(b)<-b_names
if(length(yuima@model@[email protected])!=0){
if(length(b)==1){
b<-1
} else{
indx_b0<-paste0(yuima@model@[email protected],"0",collapse="")
b[indx_b0]<-1
}
sigma<-tail(param,1)
}else {sigma<-1}
NoNeg.Noise<-FALSE
if(is.CARMA(yuima)){
if("mean.noise" %in% names(param)){
NoNeg.Noise<-TRUE
}
}
if(NoNeg.Noise==TRUE){
if (length(b)==p){
#mean.noise<-param[mean.noise]
# Be useful for carma driven by a no negative levy process
mean.y<-mean(y)
#mean.y<-mean.noise*tail(b,n=1)/tail(a,n=1)*sigma
#param[mean.noise]<-mean.y/(tail(b,n=1)/tail(a,n=1)*sigma)
}else{
mean.y<-0
}
y<-y-mean.y
}
# V_inf0<-matrix(diag(rep(1,p)),p,p)
V_inf0<-env$V_inf0
p<-env$p
q<-env$q
strLog<-yuima.carma.loglik1(y, u, a, b, sigma,time.obs,V_inf0,p,q)
}else{
# 01/01
# ar.par <- yuima@model@[email protected]
# name.ar<-paste0(ar.par, c(1:p))
a<-param[name.ar]
# ma.par <- yuima@model@[email protected]
# q <- yuima@model@info@q
name.ma<-paste0(ma.par, c(0:q))
b<-param[name.ma]
if(length(yuima@model@[email protected])!=0){
if(length(b)==1){
b<-1
} else{
indx_b0<-paste0(yuima@model@[email protected],"0",collapse="")
b[indx_b0]<-1
}
scale.par <- yuima@model@[email protected]
sigma <- param[scale.par]
} else{sigma <- 1}
loc.par <- yuima@model@[email protected]
mu <- param[loc.par]
NoNeg.Noise<-FALSE
if(is.CARMA(yuima)){
if("mean.noise" %in% names(param)){
NoNeg.Noise<-TRUE
}
}
# Lines 883:840 work if we have a no negative noise
if(is.CARMA(yuima)&&(NoNeg.Noise==TRUE)){
if (length(b)==p){
mean.noise<-param[mean.noise]
# Be useful for carma driven by levy process
# mean.y<-mean.noise*tail(b,n=1)/tail(a,n=1)*sigma
mean.y<-mean(y-mu)
}else{
mean.y<-0
}
y<-y-mean.y
}
y.start <- y-mu
#V_inf0<-matrix(diag(rep(1,p)),p,p)
V_inf0<-env$V_inf0
p<-env$p
q<-env$q
strLog<-yuima.carma.loglik1(y.start, u, a, b, sigma,time.obs,V_inf0,p,q)
}
QL<-strLog$loglikCdiag
# }else {
# yuima.warn("carma(p,q): the scale parameter is equal to 1. We will implemented as soon as possible")
# return(NULL)
# }
} else if (!rcpp) {
drift <- drift.term(yuima, param, env)
diff <- diffusion.term(yuima, param, env)
QL <- 0
pn <- 0
vec <- env$deltaX-h*drift[-n,]
K <- -0.5*d.size * log( (2*pi*h) )
dimB <- dim(diff[, , 1])
if(is.null(dimB)){ # one dimensional X
for(t in 1:(n-1)){
yB <- diff[, , t]^2
logdet <- log(yB)
pn <- Cn.r[t]*(K - 0.5*logdet-0.5*vec[t, ]^2/(h*yB))
QL <- QL+pn
}
} else { # multidimensional X
for(t in 1:(n-1)){
yB <- diff[, , t] %*% t(diff[, , t])
logdet <- log(det(yB))
if(is.infinite(logdet) ){ # should we return 1e10?
pn <- log(1)
yuima.warn("singular diffusion matrix")
return(1e10)
}else{
pn <- (K - 0.5*logdet +
((-1/(2*h))*t(vec[t, ])%*%solve(yB)%*%vec[t, ]))*Cn.r[t]
QL <- QL+pn
}
}
}
} else {
drift_name <- yuima@model@drift
diffusion_name <- yuima@model@diffusion
####data <- yuima@[email protected]
data <- matrix(0,length(yuima@[email protected][[1]]),d.size)
for(i in 1:d.size) data[,i] <- as.numeric(yuima@[email protected][[i]])
env$data <- data ##Kurisaki 5/29/2021
thetadim <- length(yuima@model@parameter@all)
noise_number <- yuima@[email protected]
assign(yuima@[email protected],env$time[-length(env$time)],envir = env) ##Kurisaki 5/29/2021
for(i in 1:d.size) assign(yuima@[email protected][i], data[-length(data[,1]),i],envir = env) ##Kurisaki 5/29/2021
for(i in 1:thetadim) assign(names(param)[i], param[[i]],envir = env) ##Kurisaki 5/29/2021
d_b <- NULL
for(i in 1:d.size){
if(length(eval(drift_name[[i]],envir = env))==(length(data[,1])-1)){ ##Kurisaki 5/29/2021
d_b[[i]] <- drift_name[[i]] #this part of model includes "x"(state.variable)
}
else{
if(is.na(c(drift_name[[i]][2]))){ #ex. yuima@model@drift=expression(0) (we hope "expression((0))")
drift_name[[i]] <- parse(text=paste(sprintf("(%s)", drift_name[[i]])))[[1]]
}
d_b[[i]] <- parse(text=paste("(",drift_name[[i]][2],")*rep(1,length(data[,1])-1)",sep=""))
#vectorization
}
}
v_a<-matrix(list(NULL),d.size,noise_number)
for(i in 1:d.size){
for(j in 1:noise_number){
if(length(eval(diffusion_name[[i]][[j]],envir = env))==(length(data[,1])-1)){ ##Kurisaki 5/29/2021
v_a[[i,j]] <- diffusion_name[[i]][[j]] #this part of model includes "x"(state.variable)
}
else{
if(is.na(c(diffusion_name[[i]][[j]][2]))){
diffusion_name[[i]][[j]] <- parse(text=paste(sprintf("(%s)", diffusion_name[[i]][[j]])))[[1]]
}
v_a[[i,j]] <- parse(text=paste("(",diffusion_name[[i]][[j]][2],")*rep(1,length(data[,1])-1)",sep=""))
#vectorization
}
}
}
#for(i in 1:d) assign(yuima@[email protected][i], data[-length(data[,1]),i])
dx_set <- as.matrix((data-rbind(numeric(d.size),as.matrix(data[-length(data[,1]),])))[-1,])
drift_set <- diffusion_set <- NULL
#for(i in 1:thetadim) assign(names(param)[i], param[[i]])
for(i in 1:d.size) drift_set <- cbind(drift_set,eval(d_b[[i]],envir = env)) ##Kurisaki 5/29/2021
for(i in 1:noise_number){
for(j in 1:d.size) diffusion_set <- cbind(diffusion_set,eval(v_a[[j,i]],envir = env)) ##Kurisaki 5/29/2021
}
QL <- (likndim(dx_set,drift_set,diffusion_set,env$h)*(-0.5) + (n-1)*(-0.5*d.size * log( (2*pi*env$h) )))
}
if(!is.finite(QL)){
yuima.warn("quasi likelihood is too small to calculate.")
return(1e10)
}
if(print==TRUE){
yuima.warn(sprintf("NEG-QL: %f, %s", -QL, paste(names(param),param,sep="=",collapse=", ")))
}
#cat(sprintf("\n%.5f ", -QL))
if(is.infinite(QL)) return(1e10)
return(as.numeric(-QL))
}
MatrixA<-function (a)
{
#Build Matrix A in the state space representation of Carma(p,q)
#given the autoregressive coefficient
pp = length(a)
af = cbind(rep(0, pp - 1), diag(pp - 1))
af = rbind(af, -a[pp:1])
return(af)
}
# yuima.Vinfinity<-function(elForVInf,v){
# # We find the infinity stationary variance-covariance matrix
# A<-elForVInf$A
# sigma<-elForVInf$sigma
# # #p<-dim(A)[1]
# # p<-elForVInf$p
# ATrans<-elForVInf$ATrans
# matrixV<-elForVInf$matrixV
# matrixV[upper.tri(matrixV,diag=TRUE)]<-v
# matrixV<-as.matrix(forceSymmetric(matrixV))
# #matrixV[lower.tri(matrixV)]<-matrixV[upper.tri(matrixV)]
# # l<-rbind(matrix(rep(0,p-1),p-1,1),1)
# # matrixV<-matrix(v,p,p)
#
# lTrans<-elForVInf$lTrans
# l<-elForVInf$l
#
#
# RigSid<-l%*%elForVInf$lTrans
# Matrixobj<-A%*%matrixV+matrixV%*%ATrans+sigma^2*RigSid
# obj<-sum(Matrixobj^2)
# obj
# }
#carma.kalman<-function(y, tt, p, q, a,bvector, sigma){
carma.kalman<-function(y, u, p, q, a,bvector, sigma, times.obs, V_inf0){
#new Code
A<-MatrixA(a)
expA<-expm(A*u,method="Pade",order=6, trySym=FALSE, do.sparseMsg = FALSE)
V_inf<-V0inf(a,p,sigma)
expAT<-t(expA)
Qmatr <- V_inf - expA %*% V_inf %*% expAT
statevar<-numeric(length=p)
SigMatr <- V_inf+0
sd_2<-0
Result<-numeric(length=2)
Kgain<-numeric(length=p)
dum_zc<-numeric(length=p)
Mat22int<-numeric(length=(p*p))
loglstar<- .Call("Cycle_Carma", y, statevar, expA, as.integer(length(y)),
as.integer(p), Qmatr, SigMatr, bvector, Result, Kgain,
dum_zc, Mat22int,
PACKAGE="yuima")
return(list(loglstar=loglstar[1]-0.5*log(2*pi)*times.obs,s2hat=loglstar[2]))
# # Old version
#
#
# V_inf0<-matrix(diag(rep(1,p)),p,p)
#
# A<-MatrixA(a)
# # u<-diff(tt)[1]
#
#
# # Amatx<-yuima.carma.eigen(A)
# # expA<-Amatx$vectors%*%expm(diag(Amatx$values*u),
# # method="Pade",
# # order=6,
# # trySym=TRUE,
# # do.sparseMsg = TRUE)%*%solve(Amatx$vectors)
#
# # if(!is.complex(Amatx$values)){
# # expA<-Amatx$vectors%*%diag(exp(Amatx$values*u))%*%solve(Amatx$vectors)
# # }else{
# expA<-expm(A*u,method="Pade",order=6, trySym=FALSE, do.sparseMsg = FALSE)
# # }
# #expA<-yuima.exp(A*u)
#
# v<-as.numeric(V_inf0[upper.tri(V_inf0,diag=TRUE)])
#
# ATrans<-t(A)
# matrixV<-matrix(0,p,p)
# #l.dummy<-c(rep(0,p-1),1)
# l<-rbind(matrix(rep(0,p-1),p-1,1),1)
# #l<-matrix(l.dummy,p,1)
# #lTrans<-matrix(l.dummy,1,p)
# lTrans<-t(l)
# elForVInf<-new.env()
# elForVInf$A<-A
# elForVInf$ATrans<-ATrans
# elForVInf$lTrans<-lTrans
# elForVInf$l<-l
# elForVInf$matrixV<-matrixV
# elForVInf$sigma<-sigma
# # elForVInf<-list(A=A,
# # ATrans=ATrans,
# # lTrans=lTrans,
# # l=l,
# # matrixV=matrixV,
# # sigma=sigma)
# #
# V_inf_vect<-nlm(yuima.Vinfinity, v,
# elForVInf = elForVInf)$estimate
# # V_inf_vect<-nlminb(start=v,objective=yuima.Vinfinity, elForVInf = elForVInf)$par
# # V_inf_vect<-optim(par=v,fn=yuima.Vinfinity,method="L-BFGS-B", elForVInf = elForVInf)$par
# V_inf<-matrix(0,p,p)
#
# V_inf[upper.tri(V_inf,diag=TRUE)]<-V_inf_vect
# V_inf<-forceSymmetric(V_inf)
#
# V_inf[abs(V_inf)<= 1.e-06]=0
#
# # A<-MatrixA(a)
# # expA<-expm(A*u,method="Pade",order=6, trySym=FALSE, do.sparseMsg = FALSE)
# #
# # V_inf<-V0inf(a,p,sigma)
# #
#
#
# expAT<-t(expA)
# #SIGMA_err<-V_inf-expA%*%V_inf%*%t(expA)
# SigMatr<-V_inf-expA%*%V_inf%*%expAT
# statevar<-matrix(rep(0, p),p,1)
# Qmatr<-SigMatr
#
# # set
# #statevar<-statevar0
#
# # SigMatr<-expA%*%V_inf%*%t(expA)+Qmatr
#
# #SigMatr<-Qmatr
# SigMatr<-V_inf
#
# zc<-matrix(bvector,1,p)
# loglstar <- 0
# loglstar1 <- 0
#
# # zcT<-matrix(bvector,p,1)
# zcT<-t(zc)
# for(t in 1:times.obs){
# # prediction
# statevar<-expA%*%statevar
# SigMatr<-expA%*%SigMatr%*%expAT+Qmatr
# # forecast
# Uobs<-y[t]-zc%*%statevar
# dum.zc<-zc%*%SigMatr
# sd_2<-dum.zc%*%zcT
# # sd_2<-zc%*%SigMatr%*%zcT
# Inv_sd_2<-1/sd_2
# #correction
# Kgain<-SigMatr%*%zcT%*%Inv_sd_2
# statevar<-statevar+Kgain%*%Uobs
# #SigMatr<-SigMatr-Kgain%*%zc%*%SigMatr
# SigMatr<-SigMatr-Kgain%*%dum.zc
# term_int<--0.5*(log(sd_2)+Uobs%*%Uobs%*%Inv_sd_2)
# loglstar<-loglstar+term_int
# }
# return(list(loglstar=(loglstar-0.5*log(2*pi)*times.obs),s2hat=sd_2))
}
V0inf<-function(a,p,sigma){
# This code is based on the paper A continuous-time ARMA process Tsai-Chan 2000
# we need to find the values along the diagonal
#l<-c(numeric(length=(p-1)),0.5)
# B_{p*p}V^{*}_{p*1}=-sigma^2*l/2
B<-matrix(0,nrow=p,ncol=p)
aa <- -rev(a)
# B1<-.Call("Coeffdiag_B", as.integer(p), aa, B,
# PACKAGE="yuima")
# B<-matrix(0,nrow=p,ncol=p)
for(i in 1:p){
# Condition on B
for(j in 1:p){
if ((2*j-i) %in% c(1:p)){
B[i,j]<-(-1)^(j-i)*aa[2*j-i]
}
if((2*j-i)==(p+1)){
B[i,j]<-(-1)^(j-i-1)
}
}
}
Vdiag <- -solve(B)[,p]*0.5*sigma^2
#V <- diag(Vdiag)
if(length(Vdiag)>1){
V <- diag(Vdiag)
}else{V <- as.matrix(Vdiag)}
# we insert the values outside the diagonal
for(i in 1:p){
for(j in (i+1):p){
if((i+j) %% 2 == 0){ # if even
V[i,j]=(-1)^((i-j)/2)*V[(i+j)/2,(i+j)/2]
V[j,i]=V[i,j]
}
}
}
return(V)
}
# CycleCarma<-function(y, statevar, expA, times.obs=integer(),
# p=integer(), Qmatr, SigMatr, zc, loglstar){
# # expAT=t(expA)
# # zcT=t(zc)
# # for(t in 1:times.obs){
# # t=1
# # # # prediction
# # statevar <- expA %*% statevar
# # SigMatr <- expA %*% SigMatr %*% t(expA) + Qmatr
# # # forecast
# # Uobs <- y[t] - zc %*% statevar # 1 - 1Xp px1
# # dum.zc <- zc %*% SigMatr # 1xp pxp
# # sd_2 <- dum.zc %*% t(zc) # 1xp px1
# # Inv_sd_2 <- 1/sd_2
# # #correction
# # Kgain <- SigMatr %*% t(zc) %*% Inv_sd_2 # pxp px1*1
# # statevar <- statevar+Kgain %*% Uobs # px1+px1
# # SigMatr <- SigMatr - Kgain %*% dum.zc # pxp-px1 1x+
# # term_int<- -0.5 * (log(sd_2)+ Uobs %*% Uobs %*% Inv_sd_2) # every entries are scalars
# # loglstar <- loglstar + term_int # every entries are scalars
# # }
# # expA=matrix(c(1:16),nrow=4,ncol=4)
# # SigMatr=matrix(c(1:16),nrow=4,ncol=4)+1
# # Qmatr=matrix(c(1:16),nrow=4,ncol=4)+2
# # vvvvv<-expA%*%SigMatr
# # ppppp<-expA%*%SigMatr%*%t(expA)+Qmatr
# rY=as.numeric(y)
# rStateVar=as.numeric(statevar)
# rExpA=as.numeric(expA)
# rtime_obs=times.obs
# p=p
# rQmatr=as.numeric(Qmatr)
# rSigMatr=as.numeric(SigMatr)
# rZc=as.numeric(zc)
# rLogstar=loglstar
# In_dum=0
# sd_2=0
# rMat21=numeric(length=p)
# rdum_zc=numeric(length=p)
# rMat22int=numeric(length=p*p)
# rMat22est=numeric(length=p*p)
# rKgain=numeric(length=p)
# for(t in 1:rtime_obs){
# # prediction
# for(i in 1:p){
# rMat21[(i-1)+1] = 0
# for(j in 1:p){
# # statevar <- expA %*% statevar: px1=pxp px1
# rMat21[(i-1)+1] = rMat21[(i-1)+1]+rExpA[(i-1)+(j-1)*p+1]*rStateVar[(j-1)+1]
# }
# rStateVar[(i-1)+1] = rMat21[(i-1)+1] # statevar <- expA %*% statevar
# }
#
# # SigMatr <- expA %*% SigMatr %*% expAT + Qmatr: pxp = pxp pxp pxp
# # First We compute rMat22int <- expA %*% SigMatr : pxp = pxp pxp
# for(i in 1:p){
# for(j in 1:p){
# rMat22int[(i-1)+(j-1)*p+1]=0
# for(h in 1:p){
# rMat22int[(i-1)+(j-1)*p+1]=rMat22int[(i-1)+(j-1)*p+1]+rExpA[(i-1)+(h-1)*p+1]*
# rSigMatr[(h-1)+(j-1)*p+1]
# }
# }
# }
# # Second We compute rMat22est <- rMat22int %*% t(expA) + Qmatr: pxp = pxp pxp + pxp
# for(i in 1:p){
# for(j in 1:p){
# rMat22est[(i-1)+(j-1)*p+1]=0
# for(h in 1:p){
# rMat22est[(i-1)+(j-1)*p+1]=rMat22est[(i-1)+(j-1)*p+1]+rMat22int[(i-1)+(h-1)*p+1]*rExpA[(j-1)+(h-1)*p+1]
#
# }
# rSigMatr[(i-1)+(j-1)*p+1]=rMat22est[(i-1)+(j-1)*p+1]+rQmatr[(i-1)+(j-1)*p+1]
# }
# }
# # # forecast
#
# # Uobs <- y[t] - zc %*% statevar # 1 - 1Xp px1
# rMat22est[1]=0
# for(i in c(1:p)){
# rMat22est[1]=rMat22est[1]+rZc[i]*rStateVar[i]
# }
# Uobs=rY[t]-rMat22est[1]
#
# # dum.zc <- zc %*% SigMatr # 1xp pxp
#
#
# for(i in c(1:p)){
# rdum_zc[i]=0
# for(j in c(1:p)){
# rdum_zc[i]=rdum_zc[i]+rZc[j]*rSigMatr[(i-1)*h+j-1+1]
# }
# }
# # sd_2 <- dum.zc %*% zcT # 1xp px1
# sd_2=0
# for(i in c(1:p)){
# sd_2=sd_2+rdum_zc[i]*rZc[i]
# }
# # #correction
# # Kgain <- SigMatr %*% zcT %*% 1/sd_2 # pxp px1*1
# for(i in c(1:p)){
# rMat21[i]=0
# for(j in c(1:p)){
# rMat21[i]=rMat21[i]+rSigMatr[(i-1)+(j-1)*p+1]*rZc[j]
# }
# rKgain[i]=rMat21[i]/sd_2
# }
#
#
# # statevar <- statevar+Kgain %*% Uobs # px1+px1
# for(i in c(1:p)){
# rStateVar[i] = rStateVar[i] + rKgain[i]*Uobs
# }
# # SigMatr <- SigMatr - Kgain %*% dum.zc # pxp-px1 1xp
# for(i in c(1:p)){
# for(j in c(1:p)){
# rSigMatr[(i-1)+(j-1)*p+1] =rSigMatr[(i-1)+(j-1)*p+1]-rKgain[i]*rdum_zc[j]
# }
# }
#
# term_int = -0.5 * (log(sd_2)+ Uobs * Uobs * 1/sd_2) # every entries are scalars
# loglstar = loglstar + term_int # every entries are scalars
#
#
# }
# Res<-matrix(c(loglstar,sd_2),nrow=2,ncol=1)
# return(Res)
# }
#yuima.PhamBreton.Inv<-function(gamma){
# p<-length(gamma)
# a<-gamma[p:1]
# if(p>2){
# x<-polynom()
# f0<-1*x^0
# f1<-x
# f2<-x*f1+gamma[1]*f0
# for(t in 2:(p-1)){
# f0<-f1
# f1<-f2
# f2<-x*f1+gamma[t]*f0
# }
# finpol<-f2+gamma[p]*f1
# a <- coef(finpol)[p:1]
# }
# return(a)
# }
#yuima.carma.loglik1<-function (y, tt, a, b, sigma)
yuima.carma.loglik1<-function (y, u, a, b, sigma,time.obs,V_inf0,p,q)
{
#This code compute the LogLik using kalman filter
# if(a_0!=0){we need to correct the Y_t for the mean}
# if(sigma!=1){we need to write}
#p <- as.integer(length(a))
# p <- length(a)
# bvector <- rep(0, p)
# q <- length(b)
bvector <- c(b, rep(0, p - q-1))
sigma<-sigma
y<-y
#xxalt<-carma.kalman(y, tt, p, q, a,bvector,sigma)
xxalt<-carma.kalman(y, u, p, q, a,bvector,sigma,time.obs,V_inf0)
list(loglikCdiag = xxalt$loglstar,s2hat=xxalt$s2hat)
}
# returns the vector of log-transitions instead of the final quasilog
quasiloglvec <- function(yuima, param, print=FALSE, env){
diff.par <- yuima@model@parameter@diffusion
drift.par <- yuima@model@parameter@drift
fullcoef <- NULL
if(length(diff.par)>0)
fullcoef <- diff.par
if(length(drift.par)>0)
fullcoef <- c(fullcoef, drift.par)
npar <- length(fullcoef)
nm <- names(param)
oo <- match(nm, fullcoef)
if(any(is.na(oo)))
yuima.stop("some named arguments in 'param' are not arguments to the supplied yuima model")
param <- param[order(oo)]
nm <- names(param)
idx.diff <- match(diff.par, nm)
idx.drift <- match(drift.par, nm)
h <- env$h
theta1 <- unlist(param[idx.diff])
theta2 <- unlist(param[idx.drift])
n.theta1 <- length(theta1)
n.theta2 <- length(theta2)
n.theta <- n.theta1+n.theta2
d.size <- yuima@[email protected]
n <- length(yuima)[1]
drift <- drift.term(yuima, param, env)
diff <- diffusion.term(yuima, param, env)
QL <- numeric(n-1) ## here is the difference
pn <- 0
vec <- env$deltaX-h*drift[-n,]
K <- -0.5*d.size * log( (2*pi*h) )
dimB <- dim(diff[, , 1])
if(is.null(dimB)){ # one dimensional X
for(t in 1:(n-1)){
yB <- diff[, , t]^2
logdet <- log(yB)
pn <- K - 0.5*logdet-0.5*vec[t, ]^2/(h*yB)
QL[t] <- pn
}
} else { # multidimensional X
for(t in 1:(n-1)){
yB <- diff[, , t] %*% t(diff[, , t])
logdet <- log(det(yB))
if(is.infinite(logdet) ){ # should we return 1e10?
pn <- log(1)
yuima.warn("singular diffusion matrix")
return(1e10)
}else{
pn <- K - 0.5*logdet +
((-1/(2*h))*t(vec[t, ])%*%solve(yB)%*%vec[t, ])
QL[t] <- pn
}
}
}
return(QL)
}
setMethod("summary", "yuima.qmle",
function (object, ...)
{
cmat <- cbind(Estimate = object@coef, `Std. Error` = sqrt(diag(object@vcov)))
m2logL <- 2 * object@min
Additional.Info <- list()
if(is(object@model,"yuima.carma")){
Additional.Info <-list(Stationarity = Diagnostic.Carma(object))
}
tmp <- new("summary.yuima.qmle", call = object@call, coef = cmat,
m2logL = m2logL,
model = object@model,
Additional.Info = Additional.Info
)
tmp
}
)
setMethod("show", "summary.yuima.qmle",
function (object)
{
cat("Quasi-Maximum likelihood estimation\n\nCall:\n")
print(object@call)
cat("\nCoefficients:\n")
print(coef(object))
cat("\n-2 log L:", object@m2logL, "\n")
if(length([email protected])>0){
if(is(object@model,"yuima.carma")){
Dummy<-paste0("\nCarma(",object@model@info@p,",",object@model@info@q,")",
collapse = "")
if([email protected]$Stationarity){
cat(Dummy,"model: Stationarity conditions are satisfied.\n")
}else{
cat(Dummy,"model: Stationarity conditions are not satisfied.\n")
}
}
}
}
)
setMethod("plot",signature(x="yuima.CP.qmle"),
function(x, ...){
t <- [email protected]
X <- [email protected]
points(x=t,y=X, ...)
}
)
setMethod("summary", "yuima.CP.qmle",
function (object, ...)
{
cmat <- cbind(Estimate = object@coef, `Std. Error` = sqrt(diag(object@vcov)))
m2logL <- 2 * object@min
x <- [email protected]
j <- [email protected]
t <- [email protected]
tmp <- new("summary.yuima.CP.qmle", call = object@call, coef = cmat,
m2logL = m2logL, NJ = length(t),
MeanJ = mean(j,na.rm=TRUE),
SdJ = sd(j,na.rm=TRUE),
MeanT = mean(diff(t),na.rm=TRUE),
X.values = x,
Jump.values = j,
Jump.times = t,
model = object@model,
threshold=object@threshold
)
tmp
}
)
setMethod("show", "summary.yuima.CP.qmle",
function (object)
{
cat("Quasi-Maximum likelihood estimation\n\nCall:\n")
print(object@call)
cat("\nCoefficients:\n")
print(coef(object))
cat("\n-2 log L:", object@m2logL, "\n")
cat(sprintf("\n\nNumber of estimated jumps: %d\n",object@NJ))
cat(sprintf("\nAverage inter-arrival times: %f\n",object@MeanT))
cat(sprintf("\nAverage jump size: %f\n",object@MeanJ))
cat(sprintf("\nStandard Dev. of jump size: %f\n",object@SdJ))
cat(sprintf("\nJump Threshold: %f\n",object@threshold))
cat("\nSummary statistics for jump times:\n")
print(summary([email protected]))
cat("\nSummary statistics for jump size:\n")
print(summary([email protected],na.rm=TRUE))
cat("\n")
}
)
# Utilities for estimation of levy in continuous arma model
setMethod("summary", "yuima.carma.qmle",
function (object, ...)
{
cmat <- cbind(Estimate = object@coef, `Std. Error` = sqrt(diag(object@vcov)))
m2logL <- 2 * object@min
data<-Re(coredata([email protected]))
data<- data[!is.na(data)]
Additional.Info <- list()
if(is(object@model,"yuima.carma")){
Additional.Info <-list(Stationarity = Diagnostic.Carma(object))
}
tmp <- new("summary.yuima.carma.qmle", call = object@call, coef = cmat,
m2logL = m2logL,
MeanI = mean(data),
SdI = sd(data),
logLI = [email protected],
TypeI = object@[email protected],
NumbI = length(data),
StatI = summary(data),
Additional.Info = Additional.Info,
model = object@model
)
tmp
}
)
setMethod("show", "summary.yuima.carma.qmle",
function (object)
{
cat("Two Stage Quasi-Maximum likelihood estimation\n\nCall:\n")
print(object@call)
cat("\nCoefficients:\n")
print(coef(object))
cat("\n-2 log L:", object@m2logL, "\n")
cat(sprintf("\n\nNumber of increments: %d\n",object@NumbI))
cat(sprintf("\nAverage of increments: %f\n",object@MeanI))
cat(sprintf("\nStandard Dev. of increments: %f\n",object@SdI))
if(!is.null(object@logLI)){
cat(sprintf("\n\n-2 log L of increments: %f\n",-2*object@logLI))
}
cat("\nSummary statistics for increments:\n")
print(object@StatI)
cat("\n")
if(length([email protected])>0){
if(is(object@model,"yuima.carma")){
Dummy<-paste0("\nCarma(",object@model@info@p,",",object@model@info@q,")",
collapse = "")
if([email protected]$Stationarity){
cat(Dummy,"model: Stationarity conditions are satisfied.\n")
}else{
cat(Dummy,"model: Stationarity conditions are not satisfied.\n")
}
}
}
}
)
# Plot Method for yuima.carma.qmle
setMethod("plot",signature(x="yuima.carma.qmle"),
function(x, ...){
Time<-index([email protected])
Incr.L<-coredata([email protected])
if(is.complex(Incr.L)){
yuima.warn("Complex increments. We plot only the real part")
Incr.L<-Re(Incr.L)
}
plot(x=Time,y=Incr.L, ...)
}
)
#Density code for compound poisson
#CPN
dCPN<-function(x,lambda,mu,sigma){
a<-min(mu-100*sigma,min(x)-1)
b<-max(mu+100*sigma,max(x)+1)
ChFunToDens.CPN <- function(n, a, b, lambda, mu, sigma) {
i <- 0:(n-1) # Indices
dx <- (b-a)/n # Step size, for the density
x <- a + i * dx # Grid, for the density
dt <- 2*pi / ( n * dx ) # Step size, frequency space
c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
d <- n/2 * dt # (center the interval on zero)
t <- c + i * dt # Grid, frequency space
charact.CPN<-function(t,lambda,mu,sigma){
normal.y<-exp(1i*t*mu-sigma^2*t^2/2)
y<-exp(lambda*(normal.y-1))
}
phi_t <- charact.CPN(t,lambda,mu,sigma)
X <- exp( -(0+1i) * i * dt * a ) * phi_t
Y <- fft(X)
density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
data.frame(
i = i,
t = t,
characteristic_function = phi_t,
x = x,
density = Re(density)
)
}
invFFT<-ChFunToDens.CPN(lambda=lambda,mu=mu,sigma=sigma,n=2^10,a=a,b=b)
dens<-approx(invFFT$x,invFFT$density,x)
return(dens$y)
}
# CExp
dCPExp<-function(x,lambda,rate){
a<-10^-6
b<-max(1/rate*10 +1/rate^2*10 ,max(x[!is.na(x)])+1)
ChFunToDens.CPExp <- function(n, a, b, lambda, rate) {
i <- 0:(n-1) # Indices
dx <- (b-a)/n # Step size, for the density
x <- a + i * dx # Grid, for the density
dt <- 2*pi / ( n * dx ) # Step size, frequency space
c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
d <- n/2 * dt # (center the interval on zero)
t <- c + i * dt # Grid, frequency space
charact.CPExp<-function(t,lambda,rate){
normal.y<-(rate/(1-1i*t))
# exp(1i*t*mu-sigma^2*t^2/2)
y<-exp(lambda*(normal.y-1))
}
phi_t <- charact.CPExp(t,lambda,rate)
X <- exp( -(0+1i) * i * dt * a ) * phi_t
Y <- fft(X)
density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
data.frame(
i = i,
t = t,
characteristic_function = phi_t,
x = x,
density = Re(density)
)
}
invFFT<-ChFunToDens.CPExp(lambda=lambda,rate=rate,n=2^10,a=a,b=b)
dens<-approx(invFFT$x[!is.na(invFFT$density)],invFFT$density[!is.na(invFFT$density)],x)
return(dens$y[!is.na(dens$y)])
}
# CGamma
dCPGam<-function(x,lambda,shape,scale){
a<-10^-6
b<-max(shape*scale*10 +shape*scale^2*10 ,max(x[!is.na(x)])+1)
ChFunToDens.CPGam <- function(n, a, b, lambda, shape,scale) {
i <- 0:(n-1) # Indices
dx <- (b-a)/n # Step size, for the density
x <- a + i * dx # Grid, for the density
dt <- 2*pi / ( n * dx ) # Step size, frequency space
c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
d <- n/2 * dt # (center the interval on zero)
t <- c + i * dt # Grid, frequency space
charact.CPGam<-function(t,lambda,shape,scale){
normal.y<-(1-1i*t*scale)^(-shape)
# exp(1i*t*mu-sigma^2*t^2/2)
y<-exp(lambda*(normal.y-1))
}
phi_t <- charact.CPGam(t,lambda,shape,scale)
X <- exp( -(0+1i) * i * dt * a ) * phi_t
Y <- fft(X)
density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
data.frame(
i = i,
t = t,
characteristic_function = phi_t,
x = x,
density = Re(density)
)
}
invFFT<-ChFunToDens.CPGam(lambda=lambda,shape=shape,scale=scale,n=2^10,a=a,b=b)
dens<-approx(invFFT$x[!is.na(invFFT$density)],invFFT$density[!is.na(invFFT$density)],x)
return(dens$y[!is.na(dens$y)])
}
minusloglik.Lev <- function(par,env){
if(env$measure.type=="code"){
if(env$measure=="rNIG"){
alpha<-par[1]
beta<-par[2]
delta<-par[3]
mu<-par[4]
f<-dNIG(env$data,alpha,beta,delta,mu)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}else{
if(env$measure=="rvgamma"){
lambda<-par[1]
alpha<-par[2]
beta<-par[3]
mu<-par[4]
f<-dvgamma(env$data,lambda,alpha,beta,mu)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}else{
if(env$measure=="rIG"){
delta<-par[1]
gamma<-par[2]
f<-dIG(env$data,delta,gamma)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}
}
}
}else{
if(env$measure=="dnorm"){
lambda<-par[1]
mu<-par[2]
sigma<-par[3]
f<-dCPN(env$data,lambda,mu,sigma)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}else{
if(env$measure=="dexp"){
lambda<-par[1]
rate<-par[2]
# -sum(log(dCPExp(env$data,lambda,rate)))
f<-dCPExp(env$data,lambda,rate)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}else{
if(env$measure=="dgamma"){
lambda<-par[1]
shape<-par[2]
scale<-par[3]
# -sum(log(dCPGam(env$data,lambda,shape,scale)))
f<-dCPGam(env$data,lambda,shape,scale)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
-sum(v1)
}
}
}
}
}
Lev.hessian<-function (params,env){
logLik.Lev <- function(params){
if(env$measure.type=="code"){
if(env$measure=="rNIG"){
alpha<-params[1]
beta<-params[2]
delta<-params[3]
mu<-params[4]
# return(sum(log(dNIG(env$data,alpha,beta,delta,mu))))
f<-dNIG(env$data,alpha,beta,delta,mu)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
return(sum(v1))
}else{
if(env$measure=="rvgamma"){
lambda<-params[1]
alpha<-params[2]
beta<-params[3]
mu<-params[4]
#return(sum(log(dvgamma(env$data,lambda,alpha,beta,mu))))
f<-dvgamma(env$data,lambda,alpha,beta,mu)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
return(sum(v1))
}else{
if(env$measure=="rIG"){
delta<-params[1]
gamma<-params[2]
f<-dIG(env$data,delta,gamma)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
return(sum(v1))
}else{
if(env$measure=="rgamma"){
shape<-params[1]
rate<-params[2]
f<-dgamma(env$data,shape,rate)
v<-log(as.numeric(na.omit(f)))
v1<-v[!is.infinite(v)]
return(sum(v1))
}
}
}
}
}else{
if(env$measure=="dnorm"){
lambda<-params[1]
mu<-params[2]
sigma<-params[3]
return(sum(log(dCPN(env$data,lambda,mu,sigma))))
}else{
if(env$measure=="dexp"){
lambda<-params[1]
rate<-params[2]
return(sum(log(dCPExp(env$data,lambda,rate))))
}else{
if(env$measure=="dgamma"){
lambda<-params[1]
shape<-params[2]
scale<-params[3]
return(sum(log(dCPGam(env$data,lambda,shape,scale))))
}
}
}
}
}
hessian<-tryCatch(optimHess(par=params, fn=logLik.Lev),
error=function(theta){matrix(NA,env$lengpar,env$lengpar)})
if(env$aggregation==FALSE){
if(env$measure.type=="CP"){
Matr.dum<-diag(c(1/env$dt, rep(1, (length(params)-1))))
}else{
if(env$measure=="rNIG"){
Matr.dum<-diag(c(1,1,1/env$dt,1/env$dt))
}else{
if(env$measure=="rvgamma"){
Matr.dum<-diag(c(1/env$dt,1,1,1/env$dt))
}else{
if(env$measure=="rIG"){
Matr.dum<-diag(c(1/env$dt,1))
}else{
if(env$measure=="rgamma"){
Matr.dum<-diag(c(1/env$dt,1))
}
}
}
}
}
cov<--Matr.dum%*%solve(hessian)%*%Matr.dum
}else{
cov<--solve(hessian)
}
return(cov)
}
yuima.Estimation.Lev<-function(Increment.lev,param0,
fixed.carma=fixed.carma,
lower.carma=lower.carma,
upper.carma=upper.carma,
measure=measure,
measure.type=measure.type,
dt=env$h,
aggregation=aggregation){
env<-new.env()
env$data<-Increment.lev
env$measure<-measure
env$measure.type<-measure.type
# Only one problem
env$dt<-dt
if(aggregation==FALSE){
if(measure.type=="code"){
if(env$measure=="rNIG"){
#Matr.dum<-diag(c(1,1,1/env$dt,1/env$dt))
param0[3]<-param0[3]*dt
param0[4]<-param0[4]*dt
}else{
if(env$measure=="rvgamma"){
#Matr.dum<-diag(c(1/env$dt,1,1,1/env$dt))
param0[1]<-param0[1]*dt
param0[4]<-param0[4]*dt
}else{
if(env$measure=="rIG"){
#Matr.dum<-diag(c(1/env$dt,1))
param0[1]<-param0[1]*dt
}else{
if(env$measure=="rgamma"){
param0[1]<-param0[1]*dt
}
}
}
}
}else{
param0[1]<-param0[1]*dt
}
}
# For NIG
if(measure.type=="code"){
if(measure=="rNIG"){
ui<-rbind(c(1, -1, 0, 0),c(1, 1, 0, 0),c(1, 0, 0, 0),c(0, 0, 1, 0))
ci<-c(0,0,0,10^(-6))
}else{
if(measure=="rvgamma"){
ui<-rbind(c(1,0, 0, 0),c(0, 1, 1, 0),c(0, 1,-1, 0),c(0, 1,0, 0))
ci<-c(10^-6,10^-6,10^(-6), 0)
}else{
if(measure=="rIG"){
ui<-rbind(c(1,0),c(0, 1))
ci<-c(10^-6,10^-6)
}else{
if(measure=="rgamma"){
ui<-rbind(c(1,0),c(0, 1))
ci<-c(10^-6,10^-6)
}
}
}
}
}else{
if(measure=="dnorm"){
ui<-rbind(c(1,0,0),c(0,0,1))
ci<-c(10^-6,10^-6)
}else{
if(measure=="dexp"){
ui<-rbind(c(1,0),c(0,1))
ci<-c(10^-6,10^-6)
}else{
if(measure=="dgamma"){
ui<-rbind(c(1,0,0),c(0,1,0),c(0,0,1))
ci<-c(10^-6,10^-6,10^-6)
}
}
}
}
if(!is.null(lower.carma)){
lower.con<-matrix(0,length(lower.carma),length(param0))
rownames(lower.con)<-names(lower.carma)
colnames(lower.con)<-names(param0)
numb.lower<-length(lower.carma)
lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
dummy.lower.names<-paste0(names(lower.carma),".lower")
rownames(lower.con)<-dummy.lower.names
names(lower.carma)<-dummy.lower.names
ui<-rbind(ui,lower.con)
ci<-c(ci,lower.carma)
#idx.lower.carma<-match(names(lower.carma),names(param0))
}
if(!is.null(upper.carma)){
upper.con<-matrix(0,length(upper.carma),length(param0))
rownames(upper.con)<-names(upper.carma)
colnames(upper.con)<-names(param0)
numb.upper<-length(upper.carma)
upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
dummy.upper.names<-paste0(names(upper.carma),".upper")
rownames(upper.con)<-dummy.upper.names
names(upper.carma)<-dummy.upper.names
ui<-rbind(ui,upper.con)
ci<-c(ci,-upper.carma)
}
if(!is.null(fixed.carma)){
names.fixed<-names(fixed.carma)
numb.fixed<-length(fixed.carma)
fixed.con<-matrix(0,length(fixed.carma),length(param0))
rownames(fixed.con)<-names(fixed.carma)
colnames(fixed.con)<-names(param0)
fixed.con.bis<-fixed.con
fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
rownames(fixed.con)<-dummy.fixed.names
rownames(fixed.con.bis)<-dummy.fixed.bis.names
names(fixed.carma)<-dummy.fixed.names
ui<-rbind(ui,fixed.con,fixed.con.bis)
ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
#ci<-c(ci,-fixed.carma,fixed.carma)
}
lengpar<-length(param0)
paramLev<-NA*c(1:length(lengpar))
env$lengpar<-lengpar
firs.prob<-tryCatch(constrOptim(theta=param0,
f=minusloglik.Lev,grad=NULL,ui=ui,ci=ci,env=env),
error=function(theta){NULL})
if(!is.null(firs.prob)){
paramLev<-firs.prob$par
names(paramLev)<-names(param0)
if(!is.null(fixed.carma)){
paramLev[names.fixed]<-fixed.carma
names(paramLev)<-names(param0)
}
}else{warning("the start value for levy measure is outside of the admissible region")}
env$aggregation<-aggregation
if(is.na(paramLev[1])){
covLev<-matrix(0,length(paramLev),length(paramLev))
}else{
covLev<-Lev.hessian(params=paramLev,env)
rownames(covLev)<-names(paramLev)
if(!is.null(fixed.carma)){
covLev[names.fixed,]<-matrix(0,numb.fixed,lengpar)
}
colnames(covLev)<-names(paramLev)
if(!is.null(fixed.carma)){
covLev[,names.fixed]<-matrix(0,lengpar,numb.fixed)
}
}
if(aggregation==FALSE){
if(measure.type=="code"){
if(env$measure=="rNIG"){
#Matr.dum<-diag(c(1,1,1/env$dt,1/env$dt))
paramLev[3]<-paramLev[3]/dt
paramLev[4]<-paramLev[4]/dt
}else{
if(env$measure=="rvgamma"){
#Matr.dum<-diag(c(1/env$dt,1,1,1/env$dt))
paramLev[1]<-paramLev[1]/dt
paramLev[4]<-paramLev[4]/dt
}else{
if(env$measure=="rIG"){
#Matr.dum<-diag(c(1/env$dt,1))
paramLev[1]<-paramLev[1]/dt
}else{
if(env$measure=="rgamma"){
paramLev[1]<-paramLev[1]/dt
}
}
}
}
}else{
paramLev[1]<-paramLev[1]/dt
}
}
results<-list(estLevpar=paramLev,covLev=covLev, value=firs.prob$value)
return(results)
}
# Normal Inverse Gaussian
# yuima.Estimation.NIG<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
#
# minusloglik.dNIG<-function(par,data){
# alpha<-par[1]
# beta<-par[2]
# delta<-par[3]
# mu<-par[4]
# -sum(log(dNIG(data,alpha,beta,delta,mu)))
# }
#
# data<-Increment.lev
#
# # Only one problem
#
#
# ui<-rbind(c(1, -1, 0, 0),c(1, 1, 0, 0),c(1, 0, 0, 0),c(0, 0, 1, 0))
# ci<-c(0,0,0,10^(-6))
#
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
#
#
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dNIG,grad=NULL,ui=ui,ci=ci,data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:length(lengpar))
#
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }else{warning("the start value for levy measure is outside of the admissible region")}
#
# NIG.hessian<-function (data,params){
# logLik.NIG <- function(params) {
#
# alpha<-params[1]
# beta<-params[2]
# delta<-params[3]
# mu<-params[4]
#
# return(sum(log(dNIG(data,alpha,beta,delta,mu))))
# }
# hessian<-optimHess(par=params, fn=logLik.NIG)
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA,length(paramLev),length(paramLev))
# }else{
# covLev<-NIG.hessian(data=as.numeric(data),params=paramLev)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# }
#
#
#
# # Variance Gaussian
#
# yuima.Estimation.VG<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
#
# minusloglik.dVG<-function(par,data){
# lambda<-par[1]
# alpha<-par[2]
# beta<-par[3]
# mu<-par[4]
# -sum(log(dvgamma(data,lambda,alpha,beta,mu)))
# }
#
# data<-Increment.lev
#
# ui<-rbind(c(1,0, 0, 0),c(0, 1, 1, 0),c(0, 1,-1, 0),c(0, 1,0, 0))
# ci<-c(10^-6,10^-6,10^(-6), 0)
#
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
#
#
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dVG,grad=NULL,ui=ui,ci=ci,data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:length(lengpar))
#
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }
#
#
# VG.hessian<-function (data,params){
# logLik.VG <- function(params) {
#
# lambda<-params[1]
# alpha<-params[2]
# beta<-params[3]
# mu<-params[4]
#
# return(sum(log(dvgamma(data,lambda,alpha,beta,mu))))
# }
# # hessian <- tsHessian(param = params, fun = logLik.VG)
# #hessian<-optimHess(par, fn, gr = NULL,data=data)
# hessian<-optimHess(par=params, fn=logLik.VG)
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA,length(paramLev),length(paramLev))
# }else{
# covLev<-VG.hessian(data=as.numeric(data),params=paramLev)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# }
#
# # Inverse Gaussian
#
# yuima.Estimation.IG<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
#
# minusloglik.dIG<-function(par,data){
# delta<-par[1]
# gamma<-par[2]
# f<-dIG(data,delta,gamma)
# v<-log(as.numeric(na.omit(f)))
# v1<-v[!is.infinite(v)]
# -sum(v1)
# }
#
# data<-Increment.lev
#
# ui<-rbind(c(1,0),c(0, 1))
# ci<-c(10^-6,10^-6)
#
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
#
#
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dIG,
# grad=NULL,
# ui=ui,
# ci=ci,
# data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:length(lengpar))
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }
#
# IG.hessian<-function (data,params){
# logLik.IG <- function(params) {
#
# delta<-params[1]
# gamma<-params[2]
# f<-dIG(data,delta,gamma)
# v<-log(as.numeric(na.omit(f)))
# v1<-v[!is.infinite(v)]
# return(sum(v1))
# }
# # hessian <- tsHessian(param = params, fun = logLik.VG)
# #hessian<-optimHess(par, fn, gr = NULL,data=data)
# hessian<-optimHess(par=params, fn=logLik.IG)
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA,length(paramLev),length(paramLev))
# }else{
# covLev<-IG.hessian(data=as.numeric(data),params=paramLev)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# }
#
# # Compound Poisson-Normal
#
# yuima.Estimation.CPN<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
# dCPN<-function(x,lambda,mu,sigma){
# a<-min(mu-100*sigma,min(x)-1)
# b<-max(mu+100*sigma,max(x)+1)
# ChFunToDens.CPN <- function(n, a, b, lambda, mu, sigma) {
# i <- 0:(n-1) # Indices
# dx <- (b-a)/n # Step size, for the density
# x <- a + i * dx # Grid, for the density
# dt <- 2*pi / ( n * dx ) # Step size, frequency space
# c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
# d <- n/2 * dt # (center the interval on zero)
# t <- c + i * dt # Grid, frequency space
# charact.CPN<-function(t,lambda,mu,sigma){
# normal.y<-exp(1i*t*mu-sigma^2*t^2/2)
# y<-exp(lambda*(normal.y-1))
# }
# phi_t <- charact.CPN(t,lambda,mu,sigma)
# X <- exp( -(0+1i) * i * dt * a ) * phi_t
# Y <- fft(X)
# density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
# data.frame(
# i = i,
# t = t,
# characteristic_function = phi_t,
# x = x,
# density = Re(density)
# )
# }
# invFFT<-ChFunToDens.CPN(lambda=lambda,mu=mu,sigma=sigma,n=2^12,a=a,b=b)
# dens<-approx(invFFT$x,invFFT$density,x)
# return(dens$y)
# }
#
# minusloglik.dCPN<-function(par,data){
# lambda<-par[1]
# mu<-par[2]
# sigma<-par[3]
# -sum(log(dCPN(data,lambda,mu,sigma)))
# }
#
# data<-Increment.lev
#
# ui<-rbind(c(1,0,0),c(0,0,1))
# ci<-c(10^-6,10^-6)
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dCPN,
# grad=NULL,
# ui=ui,
# ci=ci,
# data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:lengpar)
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }
#
# CPN.hessian<-function (data,params,lengpar){
# logLik.CPN <- function(params) {
#
# lambda<-params[1]
# mu<-params[2]
# sigma<-params[3]
# return(sum(log(dCPN(data,lambda,mu,sigma))))
# }
# # hessian <- tsHessian(param = params, fun = logLik.VG)
# #hessian<-optimHess(par, fn, gr = NULL,data=data)
# hessian<-tryCatch(optimHess(par=params, fn=logLik.CPN),
# error=function(theta){matrix(NA,lengpar,lengpar)})
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA, lengpar,lengpar)
# }else{
# covLev<-CPN.hessian(data=as.numeric(data),params=paramLev,lengpar=lengpar)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# }
#
# yuima.Estimation.CPExp<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
# dCPExp<-function(x,lambda,rate){
# a<-10^-6
# b<-max(1/rate*10 +1/rate^2*10 ,max(x[!is.na(x)])+1)
# ChFunToDens.CPExp <- function(n, a, b, lambda, rate) {
# i <- 0:(n-1) # Indices
# dx <- (b-a)/n # Step size, for the density
# x <- a + i * dx # Grid, for the density
# dt <- 2*pi / ( n * dx ) # Step size, frequency space
# c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
# d <- n/2 * dt # (center the interval on zero)
# t <- c + i * dt # Grid, frequency space
# charact.CPExp<-function(t,lambda,rate){
# normal.y<-(rate/(1-1i*t))
# # exp(1i*t*mu-sigma^2*t^2/2)
# y<-exp(lambda*(normal.y-1))
# }
# phi_t <- charact.CPExp(t,lambda,rate)
# X <- exp( -(0+1i) * i * dt * a ) * phi_t
# Y <- fft(X)
# density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
# data.frame(
# i = i,
# t = t,
# characteristic_function = phi_t,
# x = x,
# density = Re(density)
# )
# }
# invFFT<-ChFunToDens.CPExp(lambda=lambda,rate=rate,n=2^12,a=a,b=b)
# dens<-approx(invFFT$x[!is.na(invFFT$density)],invFFT$density[!is.na(invFFT$density)],x)
# return(dens$y[!is.na(dens$y)])
# }
#
# minusloglik.dCPExp<-function(par,data){
# lambda<-par[1]
# rate<-par[2]
# -sum(log(dCPExp(data,lambda,rate)))
# }
#
# data<-Increment.lev
#
# ui<-rbind(c(1,0),c(0,1))
# ci<-c(10^-6,10^-6)
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
#
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dCPExp,
# grad=NULL,
# ui=ui,
# ci=ci,
# data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:length(lengpar))
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }
#
#
# CPExp.hessian<-function (data,params){
# logLik.CPExp <- function(params) {
#
# lambda<-params[1]
# rate<-params[2]
#
# return(sum(log(dCPExp(data,lambda,rate))))
# }
# # hessian <- tsHessian(param = params, fun = logLik.VG)
# #hessian<-optimHess(par, fn, gr = NULL,data=data)
# hessian<-optimHess(par=params, fn=logLik.CPExp)
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA,length(paramLev),length(paramLev))
# }else{
# covLev<-CPExp.hessian(data=as.numeric(data),params=paramLev)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# }
#
# yuima.Estimation.CPGam<-function(Increment.lev,param0,
# fixed.carma=fixed.carma,
# lower.carma=lower.carma,
# upper.carma=upper.carma){
# dCPGam<-function(x,lambda,shape,scale){
# a<-10^-6
# b<-max(shape*scale*10 +shape*scale^2*10 ,max(x[!is.na(x)])+1)
# ChFunToDens.CPGam <- function(n, a, b, lambda, shape,scale) {
# i <- 0:(n-1) # Indices
# dx <- (b-a)/n # Step size, for the density
# x <- a + i * dx # Grid, for the density
# dt <- 2*pi / ( n * dx ) # Step size, frequency space
# c <- -n/2 * dt # Evaluate the characteristic function on [c,d]
# d <- n/2 * dt # (center the interval on zero)
# t <- c + i * dt # Grid, frequency space
# charact.CPGam<-function(t,lambda,shape,scale){
# normal.y<-(1-1i*t*scale)^(-shape)
# # exp(1i*t*mu-sigma^2*t^2/2)
# y<-exp(lambda*(normal.y-1))
# }
# phi_t <- charact.CPGam(t,lambda,shape,scale)
# X <- exp( -(0+1i) * i * dt * a ) * phi_t
# Y <- fft(X)
# density <- dt / (2*pi) * exp( - (0+1i) * c * x ) * Y
# data.frame(
# i = i,
# t = t,
# characteristic_function = phi_t,
# x = x,
# density = Re(density)
# )
# }
# invFFT<-ChFunToDens.CPGam(lambda=lambda,shape=shape,scale=scale,n=2^12,a=a,b=b)
# dens<-approx(invFFT$x[!is.na(invFFT$density)],invFFT$density[!is.na(invFFT$density)],x)
# return(dens$y[!is.na(dens$y)])
# }
#
# minusloglik.dCPGam<-function(par,data){
# lambda<-par[1]
# shape<-par[2]
# scale<-par[3]
# -sum(log(dCPGam(data,lambda,shape,scale)))
# }
#
# data<-Increment.lev
#
# ui<-rbind(c(1,0,0),c(0,1,0),c(0,1,0))
# ci<-c(10^-6,10^-6,10^-6)
#
# if(!is.null(lower.carma)){
# lower.con<-matrix(0,length(lower.carma),length(param0))
# rownames(lower.con)<-names(lower.carma)
# colnames(lower.con)<-names(param0)
# numb.lower<-length(lower.carma)
# lower.con[names(lower.carma),names(lower.carma)]<-1*diag(numb.lower)
# dummy.lower.names<-paste0(names(lower.carma),".lower")
# rownames(lower.con)<-dummy.lower.names
# names(lower.carma)<-dummy.lower.names
# ui<-rbind(ui,lower.con)
# ci<-c(ci,lower.carma)
# #idx.lower.carma<-match(names(lower.carma),names(param0))
# }
# if(!is.null(upper.carma)){
# upper.con<-matrix(0,length(upper.carma),length(param0))
# rownames(upper.con)<-names(upper.carma)
# colnames(upper.con)<-names(param0)
# numb.upper<-length(upper.carma)
# upper.con[names(upper.carma),names(upper.carma)]<--1*diag(numb.upper)
# dummy.upper.names<-paste0(names(upper.carma),".upper")
# rownames(upper.con)<-dummy.upper.names
# names(upper.carma)<-dummy.upper.names
# ui<-rbind(ui,upper.con)
# ci<-c(ci,-upper.carma)
# }
# if(!is.null(fixed.carma)){
# names.fixed<-names(fixed.carma)
# numb.fixed<-length(fixed.carma)
# fixed.con<-matrix(0,length(fixed.carma),length(param0))
# rownames(fixed.con)<-names(fixed.carma)
# colnames(fixed.con)<-names(param0)
# fixed.con.bis<-fixed.con
# fixed.con[names(fixed.carma),names(fixed.carma)]<--1*diag(numb.fixed)
# fixed.con.bis[names(fixed.carma),names(fixed.carma)]<-1*diag(numb.fixed)
# dummy.fixed.names<-paste0(names(fixed.carma),".fixed.u")
# dummy.fixed.bis.names<-paste0(names(fixed.carma),".fixed.l")
# rownames(fixed.con)<-dummy.fixed.names
# rownames(fixed.con.bis)<-dummy.fixed.bis.names
# names(fixed.carma)<-dummy.fixed.names
# ui<-rbind(ui,fixed.con,fixed.con.bis)
# ci<-c(ci,-fixed.carma-10^-6,fixed.carma-10^-6)
# #ci<-c(ci,-fixed.carma,fixed.carma)
# }
#
#
# firs.prob<-tryCatch(constrOptim(theta=param0,
# f=minusloglik.dCPGam,
# grad=NULL,
# ui=ui,
# ci=ci,
# data=data),
# error=function(theta){NULL})
#
# lengpar<-length(param0)
# paramLev<-NA*c(1:length(lengpar))
# if(!is.null(firs.prob)){
# paramLev<-firs.prob$par
# names(paramLev)<-names(param0)
# if(!is.null(fixed.carma)){
# paramLev[names.fixed]<-fixed.carma
# names(paramLev)<-names(param0)
# }
# }
#
#
# CPGam.hessian<-function (data,params){
# logLik.CPGam <- function(params) {
#
# lambda<-params[1]
# shape<-params[2]
# scale<-params[3]
#
# return(sum(log(dCPGam(data,lambda,shape,scale))))
# }
# # hessian <- tsHessian(param = params, fun = logLik.VG)
# #hessian<-optimHess(par, fn, gr = NULL,data=data)
# hessian<-optimHess(par=params, fn=logLik.CPGam)
# cov<--solve(hessian)
# return(cov)
# }
#
# if(is.na(paramLev)){
# covLev<-matrix(NA,length(paramLev),length(paramLev))
# }else{
# covLev<-CPGam.hessian(data=as.numeric(data),params=paramLev)
# rownames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[names.fixed,]<-matrix(NA,numb.fixed,lengpar)
# }
# colnames(covLev)<-names(paramLev)
# if(!is.null(fixed.carma)){
# covLev[,names.fixed]<-matrix(NA,lengpar,numb.fixed)
# }
# }
# results<-list(estLevpar=paramLev,covLev=covLev)
# return(results)
# } | /scratch/gouwar.j/cran-all/cranData/yuima/R/qmle.R |
########################################################################
# Stepwise estimation for ergodic Levy driven SDE
########################################################################
#qmleLevy<-function(yuima,start,lower,upper,joint = FALSE,third = FALSE,
# Est.Incr = c("NoIncr","Incr","IncrPar"),
# aggregation = TRUE)
qmleLevy<-function(yuima,start,lower,upper,joint = FALSE,third = FALSE,
Est.Incr = "NoIncr",
aggregation = TRUE)
{
oldyuima<-yuima #line1
myjumpname <- yuima@[email protected]
mymeasureparam <- yuima@model@parameter@measure
if(!(Est.Incr %in% c("NoIncr","Incr","IncrPar")))
stop("Argument'Est.Incr' must be one of \"NoIncr\",\"Incr\" or \"IncrPar\"")
call <- match.call()
truestart<-start
cat("\nStarting QGMLE for SDE ... \n")
parameter<-yuima@model@parameter@all
orig.mylaw<-yuima@model@measure
mylaw<-yuima@model@measure$df
numbLev<-length(yuima@[email protected])
if(missing(yuima))
yuima.stop("yuima object is missing.")
if(!is(yuima,"yuima"))
yuima.stop("This function is for yuima-class.")
if(length(yuima@model@parameter@jump)>0)
paracoef <- yuima@model@parameter@jump
if(length(yuima@model@parameter@drift)>0)
paracoef <- c(paracoef, yuima@model@parameter@drift)
if(Est.Incr == "IncrPar"){
start0<-start
lower0<-lower
upper0<-upper
lev.names<-yuima@model@parameter@measure
}
DRIFT <- yuima@model@drift
JUMP <- yuima@[email protected]
sdeModel<-yuima@model
if(length(sdeModel@parameter@measure)!=0){
nPar<-length(sdeModel@parameter@measure)
for(i in c(1:nPar)){
assign(x = sdeModel@parameter@measure[i],
value = start[[sdeModel@parameter@measure[i]]])
}
names1 <- names(start)
index <- which(names1 %in% sdeModel@parameter@measure)
start <- start[-index]
names1 <- names(lower)
index <- which(names1 %in% sdeModel@parameter@measure)
lower <- lower[-index]
names1 <- names(upper)
index <- which(names1 %in% sdeModel@parameter@measure)
upper <- upper[-index]
}
#if(class(sdeModel@measure$df)!="yuima.law"){
if(!inherits(sdeModel@measure$df, "yuima.law")){ # fixed by YK
code <- suppressWarnings(sub("^(.+?)\\(.+", "\\1", sdeModel@measure$df$expr, perl=TRUE))
candinoise<-c("rNIG","rvgamma","rnts","rbgamma")
if(is.na(match(code,candinoise))){
yuima.stop("This function works only for the standardized normal inverse Gaussian process, variance gamma process, bilateral gamma process, and normal tempered stable process now.")
}
if(length(sdeModel@xinit) == 1){
args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", sdeModel@measure$df$expr, perl=TRUE)), ","))
if(code == "rNIG"){
if(!((abs(eval(parse(text = paste("(",args[5] ,")+(", args[3], ")*(", args[4],
")/sqrt((", args[2], ")^2-(", args[3], ")^2)" )))) < 10^(-10))
&& (abs(eval(parse(text = paste("(",args[2], ")^2*(", args[4], ")/(sqrt((", args[2],
")^2-(", args[3], ")^2))^3"))) -1) < 10^(-10))))
{
yuima.stop("This function is only for standardized Levy noises.")
}
}
else if(code == "rvgamma"){
if(!((abs(eval(parse(text = paste("(", args[5], ")+2*(", args[2], ")*(", args[4], ")/((", args[3], ")^2-("
, args[4], ")^2)" )))) < 10^(-10))
&& (abs(eval(parse(text = paste("2*((", args[2], ")*(", args[3], ")^2+(", args[4], ")^2)", "/(("
, args[3], ")^2-(", args[4], ")^2)^2"))) - 1) < 10^(-10))))
{
yuima.stop("This function is only for standardized Levy noises")
}
}
else if((code == "rnts")){
if(!((abs(eval(parse(text = paste("(", args[6], ")-(", args[2], ")*(",
args[3], ")*(", args[4], ")^((", args[2],
")-1)*gamma(1-(", args[2], "))*(-1/(", args[2],"))*(", args[5],
")"
)))) < 10^(-10))
&&(abs(eval(parse(text = paste("(", args[3], ")*(", args[2],")*((", args[2], ")-1)*gamma(1-(", args[2], "))*(-1/(", args[2],"))*(", args[4], ")^((",args[2], ")-2)*(", args[5], ")^2-(",
args[2], ")*(", args[3], ")*(", args[4], ")^((", args[2], ")-1)*gamma(1-(", args[2], "))*(-1/(", args[2],"))"
))) - 1) < 10^(-10))))
{
yuima.stop("This function is only for standardized Levy processes.")
}
}else if(code == "rbgamma"){
if(!((abs(eval(parse(text = paste("(", args[2], ")/(", args[3],")-(", args[4],")/(", args[5], ")")))) < 10^(-10))
&& (abs(eval(parse(text = paste("(", args[2], ")/(", args[3], ")^2","+(", args[4],")/(", args[5],")^2"
))) - 1) < 10^(-10) )))
{
yuima.stop("This function is only for standardized Levy processes.")
}
}
}else{
warning("In this version, the standardized conditions on multidimensional noises can not be verified.
The expressions of mean and variance are given in help page.")
# The noise condition checker below does not work now (YU: 3/23).
# args <- suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", sdeModel@measure$df$expr, perl=TRUE))
# yuimaEnv <- new.env()
# yuimaEnv$mean <- switch(code,
# rNIG = function(x=1,alpha,beta,delta0,mu,Lambda){mu+as.vector(delta0/(sqrt(alpha^2-t(beta)%*%Lambda%*%beta)))*Lambda%*%beta},
# rnts = function(x=1,alpha,a,b,beta,mu,Lambda){mu+gamma(1-alpha)*a*b^(alpha-1)*Lambda%*%beta},
# rvgamma = function(x=1,lambda,alpha,beta,mu,Lambda){mu+as.vector(2*lambda/(alpha^2-t(beta)%*%Lambda%*%beta)^2)*beta}
# )
#
# yuimaEnv$covariance <- switch(code,
# rNIG = function(x=1,alpha,beta,delta0,mu,Lambda){as.vector(delta0/(sqrt(alpha^2-t(beta)%*%Lambda%*%beta))^3)*Lambda%*%beta%*%t(beta)%*%Lambda+as.vector(delta0/sqrt(alpha^2-t(beta)%*%Lambda%*%beta))*Lambda},
# rnts = function(x=1,alpha,a,b,beta,mu,Lambda){a*(1-alpha)*gamma(1-alpha)*b^(alpha-2)*Lambda%*%beta%*%t(beta)%*%Lambda+a*gamma(1-alpha)*b^(alpha-1)*Lambda},
# rvgamma = function(x=1,lambda,alpha,beta,mu,Lambda){as.vector(4*lambda/(alpha^2-t(beta)%*%Lambda%*%beta)^2)*Lambda%*%beta%*%t(beta)%*%Lambda+as.vector(2*lambda/alpha^2-t(beta)%*%Lambda%*%beta)*Lambda}
# )
# judgemean<-sum(eval(parse(text = paste("mean","(",args,")")),yuimaEnv)==numeric(length(sdeModel@xinit)))
# judgecovariance<-sum(eval(eval(parse(text = paste("covariance","(",args,")")),yuimaEnv)==diag(1,length(sdeModel@xinit))))
# if(!((judgemean==length(sdeModel@xinit))&&(judgecovariance==length(sdeModel@xinit)*length(sdeModel@xinit))))
# {
# yuima.stop("This function is only for standardized Levy processes.")
# }
}
}else{fullcoef<-NULL}
yuima@sampling@delta <- yuima@sampling@delta[1]
yuima@[email protected] <- as.integer(yuima@[email protected])
if(!joint){
DRIFT <- yuima@model@drift
DRPAR <- yuima@model@parameter@drift
if(length(yuima@model@parameter@jump)>0)
fullcoef <- yuima@model@parameter@jump
if(length(DRPAR)>0)
fullcoef <- c(fullcoef, DRPAR)
oo <- match(yuima@model@parameter@all, fullcoef)
yuima@model@parameter@all <- yuima@model@parameter@all[order(oo)]
oo <- match(names(start), fullcoef)
start <- start[order(oo)]
oo <- match(names(upper), fullcoef)
upper <- upper[order(oo)]
oo <- match(names(lower), fullcoef)
lower <- lower[order(oo)]
yuima@model@diffusion <- yuima@[email protected]
yuima@model@parameter@diffusion <- yuima@model@parameter@jump[1:length(yuima@model@parameter@jump)]
yuima@model@parameter@all <- yuima@model@parameter@diffusion
for(i in 1:length(yuima@model@drift)){
yuima@model@drift[i] <- expression((0))
}
yuima@[email protected] <- list()
yuima@model@parameter@drift <- character(0)
yuima@model@measure <- list()
yuima@[email protected] <- character(0)
yuima@[email protected] <- character(0)
yuima@model@parameter@jump <- character(0)
yuima@model@parameter@measure <- character(0)
diffstart <- start[1:length(yuima@model@parameter@diffusion)]
diffupper <- upper[1:length(yuima@model@parameter@diffusion)]
difflower <- lower[1:length(yuima@model@parameter@diffusion)]
fres <- qmle(yuima=yuima,start=diffstart,lower=difflower,upper=diffupper,rcpp = TRUE,joint = FALSE,method = "L-BFGS-B")
DiffHessian<- fres@details$hessian #182
DIPAR <- yuima@model@parameter@diffusion
DIFFUSION <- yuima@model@diffusion
yuima@model@parameter@all <- DRPAR
yuima@model@parameter@drift <- DRPAR
yuima@model@drift <- DRIFT
dristart <- start[-(1:length(yuima@model@parameter@diffusion))]
driupper <- upper[-(1:length(yuima@model@parameter@diffusion))]
drilower <- lower[-(1:length(yuima@model@parameter@diffusion))]
partcoef <- yuima@model@parameter@diffusion
ov <- match(yuima@model@parameter@drift,partcoef)
ovp <- which(!is.na(ov))
if(length(ovp)>0)
{yuima@model@parameter@drift <- yuima@model@parameter@drift[-ovp]}
yuima@model@parameter@all <- yuima@model@parameter@drift
ma <- match(names(fres@coef),partcoef)
sort <- fres@coef[order(ma)]
esti <- numeric(length(partcoef))
newdiff <- yuima@model@diffusion
newdri <- yuima@model@drift
for(i in 1:length(partcoef))
{
esti[i] <- as.character(fres@coef[[i]])
}
if(length(yuima@model@drift) == 1){
for(i in 1:length(partcoef))
{
newdri <- gsub(partcoef[i],esti[i],newdri)
yuima@model@drift[1] <- parse(text = newdri[1])
newdiff[[1]] <- gsub(partcoef[i],esti[i],newdiff[[1]])
yuima@model@diffusion[[1]] <- parse(text = newdiff[[1]])
}
}else{
for(i in 1:length(partcoef))
{
for(j in 1:length(yuima@model@drift))
{
newdri[j] <- gsub(partcoef[i],esti[i],newdri[j])
yuima@model@drift[j] <- parse(text = newdri[j])
}
for(k in 1:length(yuima@model@diffusion)){
for(l in 1:length(yuima@model@diffusion[[1]])){
newdiff[[k]][l] <- gsub(partcoef[i],esti[i],newdiff[[k]][l])
yuima@model@diffusion[[k]][l] <- parse(text = newdiff[[k]][l])
}
}
}
}
yuima@model@parameter@diffusion <- character(0)
sres<-qmle(yuima=yuima,start=dristart,lower=drilower,upper=driupper,rcpp = TRUE,method = "L-BFGS-B")
DriftHessian <- sres@details$hessian #239
if((length(ovp) == 0) && (third)){
yuima@model@diffusion <- DIFFUSION
yuima@model@drift <- DRIFT
yuima@model@parameter@diffusion <- DIPAR
yuima@model@parameter@all <- DIPAR
newdri <- yuima@model@drift
for(i in 1:length(sres@coef))
{
esti[i] <- as.character(sres@coef[[i]])
}
if(length(yuima@model@drift)==1){
for(i in 1:length(sres@coef))
{
newdri <- gsub(yuima@model@parameter@drift[i],esti[i],newdri)
yuima@model@drift[1] <- parse(text=newdri[1])
}
}else{
for(i in 1:length(sres@coef))
{
for(j in 1:length(yuima@model@drift))
{
newdri[j] <- gsub(yuima@model@parameter@drift[i],esti[i],newdri[j])
yuima@model@drift[j] <- parse(text = newdri[j])
}
}
}
yuima@model@parameter@drift <- character(0)
too <- match(names(fres@coef),names(diffstart))
diffstart <- diffstart[order(too)]
for(i in 1:length(diffstart)){
diffstart[[i]] <- fres@coef[[i]]
}
tres <- qmle(yuima=yuima,start=diffstart,lower=difflower,upper=diffupper,rcpp = TRUE,joint = FALSE,method = "L-BFGS-B")
res <- list(first = fres@coef, second = sres@coef, third = tres@coef)
}else if((length(ovp) > 0) || !(third)){
coef<-c(sres@coef,fres@coef)
mycoef<-unlist(truestart)
#mycoef1<-mycoef[names(coef)]
mycoef2<-mycoef[!names(mycoef)%in%names(coef)]
mycoef<-c(coef,mycoef2)
vcov0<-matrix(NA,nrow = length(coef),ncol=length(coef))
rownames(vcov0)<-names(coef)
colnames(vcov0)<-names(coef)
min0<- c(fres@min,sres@min)
details0<-list(sres@details,fres@details)
nobs0<-sres@nobs
res<-new("yuima.qmle", call = call, coef = coef, fullcoef = mycoef,
vcov = vcov0, min = min0, details = details0, minuslogl = minusquasilogl,
method = sres@method, nobs=nobs0, model=sdeModel)
# res <- list(first = fres@coef, second = sres@coef)}
if(length(oldyuima@[email protected])==1){
myGamhat <- matrix(0,length(coef),length(coef))
myGamhat[1:dim(DiffHessian)[1],1:dim(DiffHessian)[2]]<-DiffHessian
myGamhat[dim(DiffHessian)[1]+1:dim(DriftHessian)[1],dim(DiffHessian)[1]+1:dim(DriftHessian)[2]]<-DriftHessian#293
myGamhat<-myGamhat/oldyuima@sampling@Terminal
}else{
myGamhat <- matrix(0,length(coef),length(coef))
myGamhat[1:dim(DiffHessian)[1],1:dim(DiffHessian)[2]]<-DiffHessian*oldyuima@sampling@Terminal/oldyuima@sampling@n
myGamhat[dim(DiffHessian)[1]+1:dim(DriftHessian)[1],dim(DiffHessian)[1]+1:dim(DriftHessian)[2]]<-DriftHessian#293
myGamhat<-myGamhat/oldyuima@sampling@Terminal
}
}else{
yuima.stop("third estimation may be theoretical invalid under the presence of an overlapping parameter.")
}
}else{
if(third){
yuima.stop("third estimation does not make sense in joint estimation.")
}
if(length(yuima@model@parameter@jump)>0)
fullcoef <- yuima@model@parameter@jump
if(length(yuima@model@parameter@drift)>0)
fullcoef <- c(fullcoef, yuima@model@parameter@drift)
oo <- match(yuima@model@parameter@all, fullcoef)
yuima@model@parameter@all <- yuima@model@parameter@all[order(oo)]
yuima@model@parameter@all <- yuima@model@parameter@all[1:length(which(!is.na(oo)))]
yuima@model@diffusion <- yuima@[email protected]
yuima@[email protected] <- list()
yuima@model@parameter@diffusion <- yuima@model@parameter@jump[1:length(yuima@model@parameter@jump)]
yuima@model@measure <- list()
yuima@[email protected] <- character(0)
yuima@[email protected] <- character(0)
yuima@model@parameter@jump <- character(0)
yuima@model@parameter@measure <- character(0)
# jres<-qmle(yuima,start = start,lower = lower,upper = upper,rcpp = TRUE, joint = TRUE,method = "L-BFGS-B")
# res<-list(joint = jres@coef)
res<-qmle(yuima,start = start,lower = lower,upper = upper,rcpp = TRUE, joint = TRUE,
method = "L-BFGS-B")
}
if(Est.Incr == "NoIncr"){
return(res)
}
cat("\nStarting Estimation of Levy Increments ... \n")
data <- get.zoo.data(yuima)
s.size<-yuima@sampling@n
if(length(data)==1){
X<-as.numeric(data[[1]])
pX<-X[1:(s.size-1)]
inc<-double(s.size-1)
inc<-X[2:(s.size)]-pX
}else{
pX<- simplify2array(lapply(X = data,
FUN = as.numeric))
pX<-pX[-nrow(pX),]
inc<- simplify2array(lapply(X = data,
FUN = function(X){diff(as.numeric(X))}))
}
modeltime<-yuima@[email protected]
modelstate<-yuima@[email protected]
tmp.env<-new.env()
#if(joint){
coeffic<- coef(res)
# }else{
# coeffic<- res[[1]]
# if(length(res)>1){
# for(j in c(2:length(res))){
# coeffic<-c(coeffic,res[[j]])
# }
# }
#
#}
mp<-match(names(coeffic),parameter)
esort <- coeffic[order(mp)]
for(i in 1:length(coeffic))
{
assign(parameter[i],esort[[i]],envir=tmp.env)
}
# DRIFT <- yuima@model@drift
# JUMP <- yuima@[email protected]
if(length(yuima@[email protected])==1){
#parameter<-yuima@model@parameter@all
#resi<-double(s.size-1)
assign(modeltime,yuima@sampling@delta,envir=tmp.env)
h<-yuima@sampling@delta
assign(modelstate,pX,envir=tmp.env)
jump.term<-eval(JUMP[[1]],envir=tmp.env)
drif.term<-eval(DRIFT,envir=tmp.env)
if(length(jump.term)==1){
jump.term <- rep(jump.term, s.size-1)
}
if(length(drif.term)==1){
drif.term <- rep(drif.term, s.size-1)
} # vectorization (note. if an expression type object does not include state.variable, the length of the item after "eval" operation is 1.)
# for(s in 1:(s.size-1)){
# nova<-sqrt((jump.term)^2) # normalized variance
# resi[s]<-(1/(nova[s]))*(inc[s]-h*drif.term[s])
# }
nova<-sqrt((jump.term)^2)
resi<-(1/(nova[1:(s.size-1)]))*(inc[1:(s.size-1)]-h*drif.term[1:(s.size-1)])
if(length(oldyuima@[email protected])==1){
coefSigdiff<- 1/h*sum(resi^4) #389 resi
coefDriftSig <- 1/h*sum(resi^3)
}
if(aggregation){
Ter <- yuima@sampling@Terminal
ures <- numeric(floor(Ter))
for(l in 1:floor(Ter)){
ures[l] <- sum(resi[(floor((l-1)/h)):(floor(l/h)-1)])
}
res.incr<-ures
}else{
res.incr<-resi
}
}else{
h<-yuima@sampling@delta
Tbig<-dim(inc)[1]
assign(modeltime,h,envir=tmp.env)
numbofvar<- length(modelstate)
for(j in c(1:numbofvar)){
assign(modelstate[j],pX[,j],envir=tmp.env)
}
drif.term<-array(0,c(Tbig,numbofvar))
for(i in c(1:numbofvar))
drif.term [,i]<- eval(DRIFT[i],envir=tmp.env)
# Check using variable in the drift
# jump.term<-sapply(1:numbofvar,function(i){
# sapply(1:numbLev, function(j){
# eval(JUMP[[i]][j],envir=tmp.env)
# },simplify = TRUE)
# },simplify = TRUE)
jump.term<-array(0,c(numbofvar,numbLev,Tbig))
for(i in c(1:numbofvar)){
for(j in c(1:numbLev))
jump.term[i,j, ] <- eval(JUMP[[i]][j],envir=tmp.env)
}
# if(dim(jump.term)[1]==numbofvar){
# if(dim(jump.term)[2]==numbofvar){
# if(det(jump.term)==0){
# Invjump.term<-solve(t(jump.term)%*%jump.term)%*%t(jump.term)
# }else{
# Invjump.term<-solve(jump.term)
# }
# }else{
# Invjump.term<-solve(t(jump.term)%*%jump.term)%*%t(jump.term)
# }
# Invjump.term <- Invjump.term%o%rep(1,Tbig)
# }else{
#
# }
#
DeltaInc<-(inc-drif.term*h)
if(dim(jump.term)[1]==numbofvar){
if(dim(jump.term)[2]==numbofvar){
resi<-t(sapply(i:Tbig,function(i){
if(det(jump.term[,,i])==0){
step1<-t(jump.term[,,i])%*%jump.term[,,i]
if(det(step1)==0){
Invjump.term<-diag(rep(1,dim(step1)[1]))
}else{
Invjump.term<-solve(step1)%*%t(jump.term[,,i])
}
}else{
Invjump.term<-solve(jump.term[,,i])
}
Invjump.term%*% DeltaInc[i,]
}
)
)
}else{
resi<-t(sapply(i:Tbig,function(i){
step1<-t(jump.term[,,i])%*%jump.term[,,i]
if(det(step1)==0){
Invjump.term<-diag(rep(1,dim(step1)[1]))
}else{
Invjump.term<-solve(step1)%*%t(jump.term[,,i])
}
Invjump.term%*% DeltaInc[i,]
}
)
)
}
}
if(aggregation){
Ter <- min(floor(yuima@sampling@Terminal))
res.incr<-t(sapply(1:Ter,function(i) colSums(resi[(floor((i-1)/h)):(floor(i/h)-1),]) ))
}else{
res.incr<-resi
}
}
if(aggregation){
if(!is.matrix(res.incr)){
res.incr<- as.matrix(res.incr)
}
if(dim(res.incr)[2]==1){
colnames(res.incr)<[email protected]
}else{
colnames(res.incr)<-paste0([email protected],c(1:dim(res.incr)[2]))
}
Incr.Lev <- zooreg(data=res.incr)
Incr.Lev<- setData(original.data = Incr.Lev)
}else{
Incr.Lev <- zoo(res.incr,order.by=yuima@sampling@grid[[1]][-1])
Incr.Lev <- setData(original.data=Incr.Lev)
}
if(length(oldyuima@[email protected])==1){
mydiff<-oldyuima@[email protected][[1]]
mydiffDer <-deriv(mydiff,oldyuima@model@parameter@jump)
myenvdiff<- new.env()
if(length(oldyuima@model@parameter@jump)>=1){
for(i in c(1:length(oldyuima@model@parameter@jump))){
assign(value=coef[oldyuima@model@parameter@jump[i]],x=oldyuima@model@parameter@jump[i],envir=myenvdiff)
}
}
EvalPartDiff <- Vectorize(FUN= function(myenvdiff,mydiffDer, data){
assign(x=oldyuima@[email protected], value=data,envir = myenvdiff)
return(attr(eval(mydiffDer, envir=myenvdiff),"gradient"))},vectorize.args = "data")
DiffJumpCoeff<-EvalPartDiff(myenvdiff,mydiffDer, data=pX)
if(!is.matrix(DiffJumpCoeff)){
sigmadiffhat<- as.matrix(sum(DiffJumpCoeff^2/jump.term[1:(oldyuima@sampling@n-1)]^2)/(oldyuima@sampling@n))*coefSigdiff
DiffJumpCoeff<- t(DiffJumpCoeff)
}else{
sigmadiffhat<- matrix(0,dim(DiffJumpCoeff)[1],dim(DiffJumpCoeff)[1])
for(t in c(1:dim(DiffJumpCoeff)[2])){
sigmadiffhat <-sigmadiffhat+as.matrix(DiffJumpCoeff[,t])%*%DiffJumpCoeff[,t]/jump.term[t]^2
}
sigmadiffhat<- sigmadiffhat/(oldyuima@sampling@n)*coefSigdiff
}
mydrift<-oldyuima@model@drift[[1]]
mydriftDer <-deriv(mydrift,oldyuima@model@parameter@drift)
myenvdrift<- new.env()
if(length(oldyuima@model@parameter@drift)>=1){
for(i in c(1:length(oldyuima@model@parameter@drift))){
assign(value=coef[oldyuima@model@parameter@drift[i]],x=oldyuima@model@parameter@drift[i],envir=myenvdrift)
}
}
DriftDerCoeff<-EvalPartDiff(myenvdrift,mydriftDer, data=pX)
if(!is.matrix(DriftDerCoeff)){
# sigmadrifthat<- as.matrix(sum(DriftDerCoeff^2/jump.term[1:(oldyuima@sampling@n-1)]^2)/(oldyuima@sampling@n))*coefDriftSig
sigmadrifthat<- as.matrix(sum(DriftDerCoeff^2/jump.term[1:(oldyuima@sampling@n-1)]^2)/(oldyuima@sampling@n))
DriftDerCoeff <- t(DriftDerCoeff)
}else{
sigmadrifthat<- matrix(0,dim(DriftDerCoeff)[1],dim(DriftDerCoeff)[1])
for(t in c(1:dim(DriftDerCoeff)[2])){
sigmadrifthat <-sigmadrifthat+as.matrix(DriftDerCoeff[,t])%*%DriftDerCoeff[,t]/jump.term[t]^2
}
sigmadrifthat<- sigmadrifthat/(oldyuima@sampling@n)
}
sigmadriftdiff <- matrix(0, dim(sigmadrifthat)[2],dim(sigmadiffhat)[1])
for(t in c(1:dim(DriftDerCoeff)[2]))
sigmadriftdiff<-sigmadriftdiff+DriftDerCoeff[,t]%*%t(DiffJumpCoeff[,t])
#sigmadriftdiff<-sigmadriftdiff/oldyuima@sampling@n
sigmadriftdiff<-sigmadriftdiff/oldyuima@sampling@n*coefDriftSig
MatSigmaHat <- rbind(cbind(sigmadiffhat,t(sigmadriftdiff)),cbind(sigmadriftdiff,sigmadrifthat))
res@coef<-res@coef[c(oldyuima@model@parameter@jump,oldyuima@model@parameter@drift)]
res@vcov<-solve(t(myGamhat)%*%solve(MatSigmaHat)%*%myGamhat*oldyuima@sampling@Terminal)
}
if(Est.Incr == "Incr"){
if(length(oldyuima@[email protected])==1){
result<- new("yuima.qmleLevy.incr",Incr.Lev=Incr.Lev,
Data = yuima@data, yuima=res)
}else{
result<- new("yuima.qmleLevy.incr",Incr.Lev=Incr.Lev,
Data = yuima@data, yuima=res)
vcovLevyNoMeas <- function(myres, Gammahat0, sq=TRUE){
#myres <- res.VG2
DeltaX <- apply(myres@[email protected],2,diff)
myData<- myres@[email protected]
CmatExpr<- myres@[email protected]
ncolC <-length(myres@[email protected][[1]])
param <- myres@coef[myres@model@parameter@jump]
aexpr<- myres@model@drift
namedrift<-myres@model@parameter@drift
pardrif <- myres@coef[namedrift]
avect_exp<-myres@model@drift
Jac_Drift <- function(aexpr,namedrift,nobs=length(aexpr)){
lapply(X=c(1:nobs),FUN=function(X,namedrift,aexpr){
return(deriv(aexpr[X],namedrift))
},namedrift=namedrift,aexpr=aexpr)
}
Jac_DriftExp <- Jac_Drift(aexpr=avect_exp,namedrift)
FUNDum<-function(foo,myenv) sapply(foo, function(x,env) eval(x,envir = env), env= myenv)
h<-diff(time(myres@[email protected][[1]]))[1]
dummyf1n<- function(DeltaX, Cmat, h){
C2<-t(Cmat)%*%Cmat
dec <- chol(C2) # Eventualy Add a tryCatch
tmp <- t(DeltaX)%*%solve(C2)%*%DeltaX
logretval <- -h*sum(log(diag(dec))) - 0.5 * as.numeric(tmp)
return(logretval)
}
dummyInc<- function(DeltaX,Cmat, avect,h,sq=TRUE){
if(sq){
Incr <- solve(Cmat)%*%(DeltaX-avect*h)
}else{
C2<-t(Cmat)%*%Cmat
Incr <- solve(C2)%*%Cmat%*%(DeltaX-avect*h)
}
return(Incr)
}
dummyf2n<- function(DeltaX, avect, Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
tmp <- t(Incr)%*%solve(C2)%*%Incr
logretval <- - 0.5/h * as.numeric(tmp)
return(logretval)
}
dumGrad_f2n<-function(DeltaX, avect, Jac_a,Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
Grad<-t(Jac_a)%*%solve(C2)%*%Incr
return(Grad)
}
f1n_j<-function(x, myObsDelta, CmatExpr,h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef )
return(dummyf1n(DeltaX=myObsDelta, Cmat=Cmat, h=h))
}
Incr_Func <- function(x, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq=TRUE){
names(myData)<-myres@[email protected]
newenv <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenv )
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenv)
}
return(dummyInc(DeltaX= myObsDelta,Cmat, avect,h,sq=sq))
}
f2n_j <- function(x, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
}
return(dummyf2n(DeltaX=myObsDelta, avect=avect, Cmat=Cmat, h=h))
}
Gradf2n_j <-function(x, parDiff, myObsDelta, CmatExpr,
avect_exp, Jac_DriftExp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
Jac_a<- matrix(0,dd,length(x))
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
Jac_a[j,]<-attr(eval(Jac_DriftExp[[j]],envir=newenvDriftCoef),"gradient")
}
return(dumGrad_f2n(DeltaX=myObsDelta, avect=avect, Jac_a=Jac_a, Cmat=Cmat, h=h))
}
# f1n_j(x=param, myObsDelta=DeltaX[1,], CmatExpr=CmatExpr, h=h)
#i=1
# debug(dummyInc)
# Incr_Func(x=c(param,pardrif), myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, sq=TRUE)
# f1n_j(x=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,], myres=myres)
# f2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, avect_exp=avect_exp,
# h=h, myData=myData[i,], myres=myres)
# Gradf2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
# avect_exp, Jac_DriftExp, h, myData[i,], myres)
del <- 10^-3
dummy <- t(rep(1,length(param)))
myeta<- as.matrix(param)%*%dummy
dummyG <- t(rep(1,length(c(param,pardrif))))
globalmyeta <- as.matrix(c(param,pardrif))%*%dummyG
Incr <- del*diag(rep(1,dim(myeta)[1]))
GlobIncr <- del*diag(rep(1,dim(globalmyeta)[1]))
Sigma_gamma <- matrix(0 ,length(param),length(param))
Sigma_alpha <- matrix(0 ,length(pardrif),length(pardrif))
Sigma_algam <- matrix(0 ,length(pardrif),length(param))
histDeltaf1n_j_delta <-matrix(0 , dim(DeltaX)[1],length(param))
histDeltaf2n_j_delta <- matrix(0 , dim(DeltaX)[1],length(pardrif))
# histb_incr <- array(0, c(dim(DeltaX)[2],dim(DeltaX)[1],length(c(param,pardrif))))
for(i in c(1:dim(DeltaX)[1])){
Deltaf1n_j_delta<-sapply(X=1:dim(myeta)[1],
FUN = function(X,myObsDelta, CmatExpr, h, myData, myres,del){
par1<-myeta[,X]+Incr[,X]
#par[oldyuima@model@[email protected]]<-1
f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
par2<-myeta[,X]-Incr[,X]
f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
return((f1-f2)/(2*del))
},
myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,],
myres=myres, del=del)
# DeltaInc <- sapply(X=1:dim(GlobIncr)[1],
# FUN = function(X, myObsDelta, CmatExpr,avect_exp, h,
# myData, myres, del, sq){
# par1<-globalmyeta[,X]+GlobIncr[,X]
#
# # f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
# f1<-Incr_Func(x=par1, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
# par2<-globalmyeta[,X]-GlobIncr[,X]
# f2<-Incr_Func(x=par2, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
# #f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
# return((f1-f2)/(2*del))
# },
# myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, del=del,sq=sq
# )
#
# histDeltaf1n_j_delta[i, ]<-Deltaf1n_j_delta
#histb_incr[ , i, ] <- DeltaInc
Sigma_gamma <- Sigma_gamma + as.matrix(Deltaf1n_j_delta)%*%t(Deltaf1n_j_delta)
Deltaf2n_j_delta<- Gradf2n_j(x=pardrif, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
avect_exp, Jac_DriftExp, h, myData[i,], myres)
histDeltaf2n_j_delta[i,]<-Deltaf2n_j_delta
Sigma_alpha <- Sigma_alpha + Deltaf2n_j_delta%*%t(Deltaf2n_j_delta)
Sigma_algam <- Sigma_algam + Deltaf2n_j_delta%*%t(Deltaf1n_j_delta)
#cat("\n",i)
}
Tn<-tail(time(myres@[email protected]),1L)
Sigma_gamma0<-Sigma_gamma/Tn
Sigma_alpha0 <- Sigma_alpha/Tn
Sigma_algam0 <- Sigma_algam/Tn
Sigma0 <- cbind(rbind(Sigma_gamma0,Sigma_algam0),rbind(t(Sigma_algam0),Sigma_alpha0))
InvGammaHAT <- solve(Gammahat0)
vcov <- InvGammaHAT %*% Sigma0 %*% InvGammaHAT/Tn
myres@vcov<- vcov
return(myres)
}
if(length(result@[email protected])==length(result@[email protected][[1]])){
sq<-TRUE
}else{
sq<-FALSE
}
result<-vcovLevyNoMeas(myres=result, Gammahat0=myGamhat, sq=sq)
}
return(result)
}
cat("\nEstimation Levy parameters ... \n")
#if(class(mylaw)=="yuima.law"){
if(inherits(mylaw, "yuima.law")){ # YK, Mar. 22, 2022
if(aggregation){
minusloglik <- function(para){
para[length(para)+1]<-1
names(para)[length(para)]<-yuima@[email protected]
-sum(dens(object=mylaw, x=res.incr, param = para, log = TRUE),
na.rm = T)
}
}else{
minusloglik <- function(para){
para[length(para)+1] <- yuima@sampling@delta
names(para)[length(para)]<-yuima@[email protected]
-sum(dens(object=mylaw, x=res.incr, param = para, log = TRUE),
na.rm = T)
}
}
para <- start0[lev.names]
lowerjump <- lower0[lev.names]
upperjump <- upper0[lev.names]
esti <- optim(fn = minusloglik, lower = lowerjump, upper = upperjump,
par = para, method = "L-BFGS-B")
HessianEta <- optimHess(par=esti$par, fn=minusloglik)
res@coef<-c(res@coef,esti$par)
res@fullcoef[names(para)]<-esti$par
if(length(oldyuima@[email protected])==1 & is(oldyuima@model@measure$df, "yuima.law")){
if(!aggregation){
Ter <- yuima@sampling@Terminal
ures <- numeric(floor(Ter))
for(l in 1:floor(Ter)){
ures[l] <- sum(resi[(floor((l-1)/h)):(floor(l/h)-1)])
}
}
mypar<-res@coef[oldyuima@model@parameter@measure]
mypar[oldyuima@model@[email protected]]<-1
fdataeta<-dens(object=oldyuima@model@measure$df,x=ures,param=mypar)# f(eps, eta)
del <- 10^-3
fdatadeltaeta<- dens(object=oldyuima@model@measure$df,x=ures+del,param=mypar) # f(eps + delta, eta)
dummy <- t(rep(1,length(mypar[oldyuima@model@[email protected]])))
myeta<- as.matrix(mypar[oldyuima@model@[email protected]])%*%dummy
myetapert <- myeta+del*diag(rep(1,dim(myeta)[1]))
fdataetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[oldyuima@model@[email protected]]<-1
dens(object=oldyuima@model@measure$df,x=ures,param=par)
}
)# f(eps, eta+delta)
fdatadeltaetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[oldyuima@model@[email protected]]<-1
dens(object=oldyuima@model@measure$df,x=ures+del,param=par)
}
) # f(eps +deta, eta+delta)
term1<-1/(fdataeta)
term2 <- fdatadeltaeta*term1%*%dummy
term2 <- fdatadeltaetadelta-term2*fdataetadelta
mixpartial<-t((as.matrix(term1)%*%dummy)/del^2*term2/oldyuima@sampling@Terminal)
# construction of b_i
# DiffJumpCoeff, DriftDerCoeff, jump.term length(resi)
# step1 <- t(DiffJumpCoeff)%*%DiffJumpCoeff
DerMeta <- 1/del*(fdataetadelta - as.matrix(fdataeta)%*%rep(1,dim(fdataetadelta)[2]))*(as.matrix(term1)%*%rep(1,dim(fdataetadelta)[2]))
SigmaEta <- t(DerMeta)%*%DerMeta/oldyuima@sampling@Terminal
#SigmaEtaAlpha<- 1/oldyuima@sampling@n*DriftDerCoeff%*%(t(DiffJumpCoeff)/(as.matrix(jump.term[-length(jump.term)]^2)%*%rep(1,dim(DiffJumpCoeff)[1])) )
SigmaEtaAlpha<- 1/oldyuima@sampling@n*DriftDerCoeff%*%(t(DiffJumpCoeff)/(as.matrix(jump.term^2)%*%rep(1,dim(DiffJumpCoeff)[1])) )
SigmaEtaAlpha <- SigmaEtaAlpha*sum(resi^3)/oldyuima@sampling@delta
b_i <- matrix(0,floor(Ter),length(c(oldyuima@model@parameter@drift, oldyuima@model@parameter@jump)))
Coef1 <- matrix(0,floor(Ter),length( oldyuima@model@parameter@jump))
Coef2 <- matrix(0,floor(Ter),length( oldyuima@model@parameter@drift))
for(l in 1:floor(Ter)){
pos <- (floor((l-1)/h)):(floor(l/h)-1)
if(length(oldyuima@model@parameter@jump)==1){
b_i[l,1:length(oldyuima@model@parameter@jump)] <- sum(-DiffJumpCoeff[,pos]*resi[pos]/jump.term[pos])
Coef1[l,]<-sum(DiffJumpCoeff[,pos]/jump.term[pos]*(resi[pos]^2-h))
}else{
interm <- as.matrix(resi[pos]/jump.term[pos])
b_i[l,1:length(oldyuima@model@parameter@jump)] <- -t(DiffJumpCoeff[,pos]%*%interm)
interm2 <- as.matrix((resi[pos]^2-h)/jump.term[pos])
Coef1[l,] <-DiffJumpCoeff[,pos]%*%interm2
}
if(length(oldyuima@model@parameter@drift)==1){
b_i[l,1:length(oldyuima@model@parameter@drift)+length(oldyuima@model@parameter@jump)] <- -sum(h* DriftDerCoeff[,pos]%*%jump.term[pos])
Coef2[l,]<-sum(DriftDerCoeff[,pos]/jump.term[pos]*(resi[pos]))
}else{
b_i[l,1:length(oldyuima@model@parameter@drift)+length(oldyuima@model@parameter@jump)] <--h* DriftDerCoeff[,pos]%*%jump.term[pos]
interm3 <- as.matrix((resi[pos])/jump.term[pos])
Coef2[l,] <-DriftDerCoeff[,pos]%*%interm3
}
}
MatrUnder <- mixpartial%*%b_i
I_n <- cbind(rbind(myGamhat,MatrUnder),rbind(matrix(0, dim(myGamhat)[1],dim(HessianEta)[2]),HessianEta/oldyuima@sampling@Terminal))
SigmaGammaEta <- t(DerMeta)%*%Coef1/Ter
SigmaAlphaEta <- t(DerMeta)%*%Coef2/Ter
# dim(fdataetadelta), length(fdataeta), length(term1)
dum <- cbind(SigmaGammaEta , SigmaAlphaEta)
MatSigmaHat1<- rbind(cbind(MatSigmaHat,t(dum)),cbind(dum,SigmaEta))
InvIn<- solve(I_n)
res@vcov <-InvIn%*%MatSigmaHat1%*%t(InvIn)/Ter
colnames(res@vcov)<-names(res@fullcoef)
#res@vcov<-rbind(res@vcov,matrix(NA,nrow=length(esti$par),ncol=dim(res@vcov)[2]))
rownames(res@vcov)<-names(res@fullcoef)
res@min<-c(res@min,esti$value)
res@nobs<-c(res@nobs,length([email protected][[1]]))
result<- new("yuima.qmleLevy.incr",Incr.Lev=Incr.Lev,
minusloglLevy = minusloglik,logL.Incr=-esti$value,
Data = yuima@data, yuima=res, Levydetails=esti)
}else{
Tn<-yuima@sampling@Terminal
HessianEta_divTn <- HessianEta/Tn
if(!aggregation){
Ter <- min(floor(yuima@sampling@Terminal))
ures<-t(sapply(1:Ter,function(i) colSums(resi[(floor((i-1)/h)):(floor(i/h)-1),]) ))
#yuima.stop("da fare")
vcovLevy1 <- function(myres, HessianEta_divTn, Gammahat0, ures, sq=TRUE){
#myres <- res.VG2
DeltaX <- apply(myres@[email protected],2,diff)
myData<- myres@[email protected]
CmatExpr<- myres@[email protected]
ncolC <-length(myres@[email protected][[1]])
param <- myres@coef[myres@model@parameter@jump]
aexpr<- myres@model@drift
namedrift<-myres@model@parameter@drift
pardrif <- myres@coef[namedrift]
avect_exp<-myres@model@drift
Jac_Drift <- function(aexpr,namedrift,nobs=length(aexpr)){
lapply(X=c(1:nobs),FUN=function(X,namedrift,aexpr){
return(deriv(aexpr[X],namedrift))
},namedrift=namedrift,aexpr=aexpr)
}
Jac_DriftExp <- Jac_Drift(aexpr=avect_exp,namedrift)
FUNDum<-function(foo,myenv) sapply(foo, function(x,env) eval(x,envir = env), env= myenv)
h<-diff(time(myres@[email protected][[1]]))[1]
dummyf1n<- function(DeltaX, Cmat, h){
C2<-t(Cmat)%*%Cmat
dec <- chol(C2) # Eventualy Add a tryCatch
tmp <- t(DeltaX)%*%solve(C2)%*%DeltaX
logretval <- -h*sum(log(diag(dec))) - 0.5 * as.numeric(tmp)
return(logretval)
}
dummyInc<- function(DeltaX,Cmat, avect,h,sq=TRUE){
if(sq){
Incr <- solve(Cmat)%*%(DeltaX-avect*h)
}else{
C2<-t(Cmat)%*%Cmat
Incr <- solve(C2)%*%Cmat%*%(DeltaX-avect*h)
}
return(Incr)
}
dummyf2n<- function(DeltaX, avect, Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
tmp <- t(Incr)%*%solve(C2)%*%Incr
logretval <- - 0.5/h * as.numeric(tmp)
return(logretval)
}
dumGrad_f2n<-function(DeltaX, avect, Jac_a,Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
Grad<-t(Jac_a)%*%solve(C2)%*%Incr
return(Grad)
}
f1n_j<-function(x, myObsDelta, CmatExpr,h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef )
return(dummyf1n(DeltaX=myObsDelta, Cmat=Cmat, h=h))
}
Incr_Func <- function(x, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq=TRUE){
names(myData)<-myres@[email protected]
newenv <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenv )
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenv)
}
return(dummyInc(DeltaX= myObsDelta,Cmat, avect,h,sq=sq))
}
f2n_j <- function(x, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
}
return(dummyf2n(DeltaX=myObsDelta, avect=avect, Cmat=Cmat, h=h))
}
Gradf2n_j <-function(x, parDiff, myObsDelta, CmatExpr,
avect_exp, Jac_DriftExp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
Jac_a<- matrix(0,dd,length(x))
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
Jac_a[j,]<-attr(eval(Jac_DriftExp[[j]],envir=newenvDriftCoef),"gradient")
}
return(dumGrad_f2n(DeltaX=myObsDelta, avect=avect, Jac_a=Jac_a, Cmat=Cmat, h=h))
}
# f1n_j(x=param, myObsDelta=DeltaX[1,], CmatExpr=CmatExpr, h=h)
#i=1
# debug(dummyInc)
# Incr_Func(x=c(param,pardrif), myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, sq=TRUE)
# f1n_j(x=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,], myres=myres)
# f2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, avect_exp=avect_exp,
# h=h, myData=myData[i,], myres=myres)
# Gradf2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
# avect_exp, Jac_DriftExp, h, myData[i,], myres)
del <- 10^-3
dummy <- t(rep(1,length(param)))
myeta<- as.matrix(param)%*%dummy
dummyG <- t(rep(1,length(c(param,pardrif))))
globalmyeta <- as.matrix(c(param,pardrif))%*%dummyG
Incr <- del*diag(rep(1,dim(myeta)[1]))
GlobIncr <- del*diag(rep(1,dim(globalmyeta)[1]))
Sigma_gamma <- matrix(0 ,length(param),length(param))
Sigma_alpha <- matrix(0 ,length(pardrif),length(pardrif))
Sigma_algam <- matrix(0 ,length(pardrif),length(param))
histDeltaf1n_j_delta <-matrix(0 , dim(DeltaX)[1],length(param))
histDeltaf2n_j_delta <- matrix(0 , dim(DeltaX)[1],length(pardrif))
histb_incr <- array(0, c(dim(DeltaX)[2],dim(DeltaX)[1],length(c(param,pardrif))))
for(i in c(1:dim(DeltaX)[1])){
Deltaf1n_j_delta<-sapply(X=1:dim(myeta)[1],
FUN = function(X,myObsDelta, CmatExpr, h, myData, myres,del){
par1<-myeta[,X]+Incr[,X]
#par[oldyuima@model@[email protected]]<-1
f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
par2<-myeta[,X]-Incr[,X]
f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
return((f1-f2)/(2*del))
},
myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,],
myres=myres, del=del)
DeltaInc <- sapply(X=1:dim(GlobIncr)[1],
FUN = function(X, myObsDelta, CmatExpr,avect_exp, h,
myData, myres, del, sq){
par1<-globalmyeta[,X]+GlobIncr[,X]
# f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
f1<-Incr_Func(x=par1, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
par2<-globalmyeta[,X]-GlobIncr[,X]
f2<-Incr_Func(x=par2, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
#f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
return((f1-f2)/(2*del))
},
myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, del=del,sq=sq
)
histDeltaf1n_j_delta[i, ]<-Deltaf1n_j_delta
histb_incr[ , i, ] <- DeltaInc
Sigma_gamma <- Sigma_gamma + as.matrix(Deltaf1n_j_delta)%*%t(Deltaf1n_j_delta)
Deltaf2n_j_delta<- Gradf2n_j(x=pardrif, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
avect_exp, Jac_DriftExp, h, myData[i,], myres)
histDeltaf2n_j_delta[i,]<-Deltaf2n_j_delta
Sigma_alpha <- Sigma_alpha + Deltaf2n_j_delta%*%t(Deltaf2n_j_delta)
Sigma_algam <- Sigma_algam + Deltaf2n_j_delta%*%t(Deltaf1n_j_delta)
#cat("\n",i)
}
Tn<-tail(time(myres@[email protected]),1L)
Sigma_gamma0<-Sigma_gamma/Tn
Sigma_alpha0 <- Sigma_alpha/Tn
Sigma_algam0 <- Sigma_algam/Tn
Sigma0 <- cbind(rbind(Sigma_gamma0,Sigma_algam0),rbind(t(Sigma_algam0),Sigma_alpha0))
myLogDens <- function(x, myObsDelta, CmatExpr,h,myData, myres, pos){
return(f1n_j(x, myObsDelta[pos, ], CmatExpr,h,myData=myData[pos,], myres=myres))
}
myLogDens2<- function(x, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres, pos){
return(f2n_j(x, parDiff, myObsDelta[pos, ], CmatExpr, avect_exp, h, myData[pos,], myres))
#return(f1n_j(x, myObsDelta[pos, ], CmatExpr,h,myData=myData[pos,], myres=myres))
}
VectLogDens<-Vectorize(FUN=myLogDens,vectorize.args = "pos")
#VectLogDens2<-Vectorize(FUN=myLogDens2,vectorize.args = "pos")
mylogLik<- function(par,DeltaX,CmatExpr, myData, myres, h, Tn, nobs=dim(DeltaX)[1]){
term<-VectLogDens(x=par,
myObsDelta=DeltaX, CmatExpr=CmatExpr,h=h, myData=myData, myres=myres,pos=c(1:nobs))
return(sum(term)/Tn)
}
mylogLik2<- function(par, parDiff, myObsDelta, CmatExpr, avect_exp, h, Tn, myData, myres, nobs=dim(DeltaX)[1]){
# term<-VectLogDens2(x=par, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres,
# pos=c(1:nobs))
term<-0
for(j in c(1:nobs)){
term<-term+f2n_j(x=par, parDiff, myObsDelta[j, ], CmatExpr, avect_exp, h, myData[j,], myres)
}
return(term/Tn)
}
GradlogLik2<-function(par, parDiff, myObsDelta, CmatExpr, avect_exp, h, Tn, myData, myres, nobs=dim(DeltaX)[1]){
term<-matrix(0, length(par),1)
for(j in c(1:nobs)){
term <- term + Gradf2n_j(x=par, parDiff, myObsDelta=myObsDelta[j, ], CmatExpr,
avect_exp, Jac_DriftExp, h, myData=myData[j,], myres)
}
return(as.numeric(term)/Tn)
}
# Gammahat <- optimHess(par=param,fn=mylogLik,
# DeltaX=DeltaX,CmatExpr=CmatExpr,
# h=h, Tn=Tn, nobs=dim(DeltaX)[1],
# myData=myData, myres=myres)
# GammaAlfahat <- optimHess(par=pardrif,fn=mylogLik2,gr=GradlogLik2,
# parDiff=param, myObsDelta=DeltaX,
# CmatExpr=CmatExpr, avect_exp=avect_exp, h=h,
# Tn=Tn, nobs=dim(DeltaX)[1], myData=myData, myres=myres)
#
# Gammahat0 <- cbind(rbind(Gammahat,matrix(0,dim(GammaAlfahat)[1],dim(Gammahat)[2])),
# rbind(matrix(0,dim(Gammahat)[2],dim(GammaAlfahat)[1]),GammaAlfahat))
uhistDeltaf1n_j<- matrix(0,floor(Tn),length(param))
uhistDeltaf2n_j<- matrix(0,floor(Tn),length(pardrif))
aaa<-length(c(param,pardrif))
uhistb_incr <- array(0, c(dim(DeltaX)[2],floor(Tn),aaa))
for(l in 1:floor(Tn)){
#ures[l] <- sum(resi[(floor((l-1)/h)):(floor(l/h)-1)])
uhistDeltaf1n_j[l, ] <- colSums(histDeltaf1n_j_delta[(floor((l-1)/h)):(floor(l/h)-1),])
uhistDeltaf2n_j[l, ] <- colSums(histDeltaf2n_j_delta[(floor((l-1)/h)):(floor(l/h)-1),])
for(i in c(1:aaa)){
dumIn <- histb_incr[,(floor((l-1)/h)):(floor(l/h)-1),i]
uhistb_incr[,l,i]<- rowSums(dumIn)
}
}
nMeaspar<-myres@model@parameter@measure
mypar_meas0 <- myres@coef[nMeaspar]#res@coef[oldyuima@model@parameter@measure]
mypar_meas <- c(mypar_meas0,1)
# ures<[email protected]@original.data
names(mypar_meas)<-c(nMeaspar,myres@model@[email protected])
fdataeta<-dens(object=myres@model@measure$df,x=ures,param=mypar_meas)# f(eps, eta)
del <- 10^-3
dummy <- t(rep(1,length(mypar_meas[myres@model@[email protected]])))
myeta<- as.matrix(mypar_meas[myres@model@[email protected]])%*%dummy
myetapert <- myeta+del*diag(rep(1,dim(myeta)[1]))
fdataetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[myres@model@[email protected]]<-1
dens(object=myres@model@measure$df,x=ures,param=par)
}
)# f(eps, eta+delta)
term1<-1/(fdataeta)
dummyvecdel<-numeric(length=dim(ures)[2])
fdatadeltaeta<- matrix(0,floor(Tn),dim(ures)[2])
mixpartial <-array(0,c(length(nMeaspar),floor(Tn),dim(ures)[2]))
for(j in c(1:dim(ures)[2])){
dummyvecdel[j]<-del
fdatadeltaeta[,j]<- dens(object=myres@model@measure$df,x=ures+rep(1,dim(ures)[1])%*%t(dummyvecdel),param=mypar_meas)
fdatadeltaetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[myres@model@[email protected]]<-1
dens(object=myres@model@measure$df,x=ures+rep(1,dim(ures)[1])%*%t(dummyvecdel),param=par)
}
) # f(eps +deta, eta+delta)
dummyvecdel<-numeric(length=dim(ures)[2])
term2 <- fdatadeltaeta[,j]*term1%*%dummy
term2 <- fdatadeltaetadelta-term2*fdataetadelta
mixpartial[,,j]<-t((as.matrix(term1)%*%dummy)/del^2*term2/Tn)
}
DerMeta <- 1/del*(fdataetadelta - as.matrix(fdataeta)%*%rep(1,dim(fdataetadelta)[2]))*(as.matrix(term1)%*%rep(1,dim(fdataetadelta)[2]))
SigmaEta <- t(DerMeta)%*%DerMeta/Tn
minusloglik <- function(para){
para[length(para)+1]<-1
names(para)[length(para)]<-myres@[email protected]
-sum(dens(object=myres@model@measure$df, x=ures, param = para, log = TRUE),
na.rm = T)/Tn
}
# HessianEta_divTn <- optimHess(par=mypar_meas0, fn=minusloglik)
Gammaeta_theta <- matrix(0,length(mypar_meas0), aaa)
for(t in c(1:floor(Tn))){
Gammaeta_theta<-Gammaeta_theta+mixpartial[,t,]%*%uhistb_incr[,t,]
}
GammaEta_Theta<- -1/Tn*Gammaeta_theta
Sigma_eta_theta<-t(DerMeta)%*%cbind(uhistDeltaf1n_j,uhistDeltaf2n_j)/Tn
Sigma <- cbind(rbind(Sigma0,Sigma_eta_theta),rbind(t(Sigma_eta_theta),SigmaEta))
GammaHAT<-cbind(rbind(Gammahat0, GammaEta_Theta), rbind(t(GammaEta_Theta), HessianEta_divTn))
InvGammaHAT <- solve(GammaHAT)
vcov <- InvGammaHAT %*% Sigma %*% InvGammaHAT/Tn
myres@vcov<- vcov
return(myres)
}
}
res@vcov<-cbind(res@vcov,matrix(NA,ncol=length(esti$par),nrow=dim(res@vcov)[1]))
res@vcov<-rbind(res@vcov,matrix(NA,nrow=length(esti$par),ncol=dim(res@vcov)[2]))
colnames(res@vcov)<-names(res@fullcoef)
#res@vcov<-rbind(res@vcov,matrix(NA,nrow=length(esti$par),ncol=dim(res@vcov)[2]))
rownames(res@vcov)<-names(res@fullcoef)
res@min<-c(res@min,esti$value)
res@nobs<-c(res@nobs,length([email protected][[1]]))
result<- new("yuima.qmleLevy.incr",Incr.Lev=Incr.Lev,
minusloglLevy = minusloglik,logL.Incr=-esti$value,
Data = yuima@data, yuima=res, Levydetails=esti)
if(length(result@[email protected])==length(result@[email protected][[1]])){
sq<-TRUE
}else{
sq<-FALSE
}
if(!aggregation){
result <- vcovLevy1(myres = result, HessianEta_divTn = HessianEta_divTn,
Gammahat0 = myGamhat, ures = ures, sq=TRUE)
}else{
vcovLevy <- function(myres, HessianEta_divTn, Gammahat0, sq=TRUE){
#myres <- res.VG2
DeltaX <- apply(myres@[email protected],2,diff)
myData<- myres@[email protected]
CmatExpr<- myres@[email protected]
ncolC <-length(myres@[email protected][[1]])
param <- myres@coef[myres@model@parameter@jump]
aexpr<- myres@model@drift
namedrift<-myres@model@parameter@drift
pardrif <- myres@coef[namedrift]
avect_exp<-myres@model@drift
Jac_Drift <- function(aexpr,namedrift,nobs=length(aexpr)){
lapply(X=c(1:nobs),FUN=function(X,namedrift,aexpr){
return(deriv(aexpr[X],namedrift))
},namedrift=namedrift,aexpr=aexpr)
}
Jac_DriftExp <- Jac_Drift(aexpr=avect_exp,namedrift)
FUNDum<-function(foo,myenv) sapply(foo, function(x,env) eval(x,envir = env), env= myenv)
h<-diff(time(myres@[email protected][[1]]))[1]
dummyf1n<- function(DeltaX, Cmat, h){
C2<-t(Cmat)%*%Cmat
dec <- chol(C2) # Eventualy Add a tryCatch
tmp <- t(DeltaX)%*%solve(C2)%*%DeltaX
logretval <- -h*sum(log(diag(dec))) - 0.5 * as.numeric(tmp)
return(logretval)
}
dummyInc<- function(DeltaX,Cmat, avect,h,sq=TRUE){
if(sq){
Incr <- solve(Cmat)%*%(DeltaX-avect*h)
}else{
C2<-t(Cmat)%*%Cmat
Incr <- solve(C2)%*%Cmat%*%(DeltaX-avect*h)
}
return(Incr)
}
dummyf2n<- function(DeltaX, avect, Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
tmp <- t(Incr)%*%solve(C2)%*%Incr
logretval <- - 0.5/h * as.numeric(tmp)
return(logretval)
}
dumGrad_f2n<-function(DeltaX, avect, Jac_a,Cmat, h){
C2 <- t(Cmat)%*%Cmat
Incr <- DeltaX-h*avect
Grad<-t(Jac_a)%*%solve(C2)%*%Incr
return(Grad)
}
f1n_j<-function(x, myObsDelta, CmatExpr,h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef )
return(dummyf1n(DeltaX=myObsDelta, Cmat=Cmat, h=h))
}
Incr_Func <- function(x, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq=TRUE){
names(myData)<-myres@[email protected]
newenv <- list2env(as.list(c(x,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenv )
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenv)
}
return(dummyInc(DeltaX= myObsDelta,Cmat, avect,h,sq=sq))
}
f2n_j <- function(x, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
}
return(dummyf2n(DeltaX=myObsDelta, avect=avect, Cmat=Cmat, h=h))
}
Gradf2n_j <-function(x, parDiff, myObsDelta, CmatExpr,
avect_exp, Jac_DriftExp, h, myData, myres){
names(myData)<-myres@[email protected]
newenvJumpCoef <- list2env(as.list(c(parDiff,myData)))
Cmat<-sapply(X=CmatExpr, FUN=FUNDum, myenv=newenvJumpCoef)
newenvDriftCoef <- list2env(as.list(c(x,myData)))
dd<-length(avect_exp)
avect<-numeric(length=dd)
Jac_a<- matrix(0,dd,length(x))
for(j in c(1:dd)){
avect[j]<-eval(avect_exp[j],envir=newenvDriftCoef)
Jac_a[j,]<-attr(eval(Jac_DriftExp[[j]],envir=newenvDriftCoef),"gradient")
}
return(dumGrad_f2n(DeltaX=myObsDelta, avect=avect, Jac_a=Jac_a, Cmat=Cmat, h=h))
}
# f1n_j(x=param, myObsDelta=DeltaX[1,], CmatExpr=CmatExpr, h=h)
#i=1
# debug(dummyInc)
# Incr_Func(x=c(param,pardrif), myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, sq=TRUE)
# f1n_j(x=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,], myres=myres)
# f2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, avect_exp=avect_exp,
# h=h, myData=myData[i,], myres=myres)
# Gradf2n_j(x, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
# avect_exp, Jac_DriftExp, h, myData[i,], myres)
del <- 10^-3
dummy <- t(rep(1,length(param)))
myeta<- as.matrix(param)%*%dummy
dummyG <- t(rep(1,length(c(param,pardrif))))
globalmyeta <- as.matrix(c(param,pardrif))%*%dummyG
Incr <- del*diag(rep(1,dim(myeta)[1]))
GlobIncr <- del*diag(rep(1,dim(globalmyeta)[1]))
Sigma_gamma <- matrix(0 ,length(param),length(param))
Sigma_alpha <- matrix(0 ,length(pardrif),length(pardrif))
Sigma_algam <- matrix(0 ,length(pardrif),length(param))
histDeltaf1n_j_delta <-matrix(0 , dim(DeltaX)[1],length(param))
histDeltaf2n_j_delta <- matrix(0 , dim(DeltaX)[1],length(pardrif))
histb_incr <- array(0, c(dim(DeltaX)[2],dim(DeltaX)[1],length(c(param,pardrif))))
for(i in c(1:dim(DeltaX)[1])){
Deltaf1n_j_delta<-sapply(X=1:dim(myeta)[1],
FUN = function(X,myObsDelta, CmatExpr, h, myData, myres,del){
par1<-myeta[,X]+Incr[,X]
#par[oldyuima@model@[email protected]]<-1
f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
par2<-myeta[,X]-Incr[,X]
f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
return((f1-f2)/(2*del))
},
myObsDelta=DeltaX[i,], CmatExpr=CmatExpr, h=h, myData=myData[i,],
myres=myres, del=del)
DeltaInc <- sapply(X=1:dim(GlobIncr)[1],
FUN = function(X, myObsDelta, CmatExpr,avect_exp, h,
myData, myres, del, sq){
par1<-globalmyeta[,X]+GlobIncr[,X]
# f1<-f1n_j(x=par1, myObsDelta, CmatExpr, h, myData, myres)
f1<-Incr_Func(x=par1, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
par2<-globalmyeta[,X]-GlobIncr[,X]
f2<-Incr_Func(x=par2, myObsDelta, CmatExpr,avect_exp, h, myData, myres, sq)
#f2<-f1n_j(x=par2, myObsDelta, CmatExpr, h, myData, myres)
return((f1-f2)/(2*del))
},
myObsDelta=DeltaX[i,], CmatExpr,avect_exp, h, myData=myData[i,], myres, del=del,sq=sq
)
histDeltaf1n_j_delta[i, ]<-Deltaf1n_j_delta
histb_incr[ , i, ] <- DeltaInc
Sigma_gamma <- Sigma_gamma + as.matrix(Deltaf1n_j_delta)%*%t(Deltaf1n_j_delta)
Deltaf2n_j_delta<- Gradf2n_j(x=pardrif, parDiff=param, myObsDelta=DeltaX[i,], CmatExpr,
avect_exp, Jac_DriftExp, h, myData[i,], myres)
histDeltaf2n_j_delta[i,]<-Deltaf2n_j_delta
Sigma_alpha <- Sigma_alpha + Deltaf2n_j_delta%*%t(Deltaf2n_j_delta)
Sigma_algam <- Sigma_algam + Deltaf2n_j_delta%*%t(Deltaf1n_j_delta)
#cat("\n",i)
}
Tn<-tail(time(myres@[email protected]),1L)
Sigma_gamma0<-Sigma_gamma/Tn
Sigma_alpha0 <- Sigma_alpha/Tn
Sigma_algam0 <- Sigma_algam/Tn
Sigma0 <- cbind(rbind(Sigma_gamma0,Sigma_algam0),rbind(t(Sigma_algam0),Sigma_alpha0))
myLogDens <- function(x, myObsDelta, CmatExpr,h,myData, myres, pos){
return(f1n_j(x, myObsDelta[pos, ], CmatExpr,h,myData=myData[pos,], myres=myres))
}
myLogDens2<- function(x, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres, pos){
return(f2n_j(x, parDiff, myObsDelta[pos, ], CmatExpr, avect_exp, h, myData[pos,], myres))
#return(f1n_j(x, myObsDelta[pos, ], CmatExpr,h,myData=myData[pos,], myres=myres))
}
VectLogDens<-Vectorize(FUN=myLogDens,vectorize.args = "pos")
#VectLogDens2<-Vectorize(FUN=myLogDens2,vectorize.args = "pos")
mylogLik<- function(par,DeltaX,CmatExpr, myData, myres, h, Tn, nobs=dim(DeltaX)[1]){
term<-VectLogDens(x=par,
myObsDelta=DeltaX, CmatExpr=CmatExpr,h=h, myData=myData, myres=myres,pos=c(1:nobs))
return(sum(term)/Tn)
}
mylogLik2<- function(par, parDiff, myObsDelta, CmatExpr, avect_exp, h, Tn, myData, myres, nobs=dim(DeltaX)[1]){
# term<-VectLogDens2(x=par, parDiff, myObsDelta, CmatExpr, avect_exp, h, myData, myres,
# pos=c(1:nobs))
term<-0
for(j in c(1:nobs)){
term<-term+f2n_j(x=par, parDiff, myObsDelta[j, ], CmatExpr, avect_exp, h, myData[j,], myres)
}
return(term/Tn)
}
GradlogLik2<-function(par, parDiff, myObsDelta, CmatExpr, avect_exp, h, Tn, myData, myres, nobs=dim(DeltaX)[1]){
term<-matrix(0, length(par),1)
for(j in c(1:nobs)){
term <- term + Gradf2n_j(x=par, parDiff, myObsDelta=myObsDelta[j, ], CmatExpr,
avect_exp, Jac_DriftExp, h, myData=myData[j,], myres)
}
return(as.numeric(term)/Tn)
}
# Gammahat <- optimHess(par=param,fn=mylogLik,
# DeltaX=DeltaX,CmatExpr=CmatExpr,
# h=h, Tn=Tn, nobs=dim(DeltaX)[1],
# myData=myData, myres=myres)
# GammaAlfahat <- optimHess(par=pardrif,fn=mylogLik2,gr=GradlogLik2,
# parDiff=param, myObsDelta=DeltaX,
# CmatExpr=CmatExpr, avect_exp=avect_exp, h=h,
# Tn=Tn, nobs=dim(DeltaX)[1], myData=myData, myres=myres)
#
# Gammahat0 <- cbind(rbind(Gammahat,matrix(0,dim(GammaAlfahat)[1],dim(Gammahat)[2])),
# rbind(matrix(0,dim(Gammahat)[2],dim(GammaAlfahat)[1]),GammaAlfahat))
uhistDeltaf1n_j<- matrix(0,floor(Tn),length(param))
uhistDeltaf2n_j<- matrix(0,floor(Tn),length(pardrif))
aaa<-length(c(param,pardrif))
uhistb_incr <- array(0, c(dim(DeltaX)[2],floor(Tn),aaa))
for(l in 1:floor(Tn)){
#ures[l] <- sum(resi[(floor((l-1)/h)):(floor(l/h)-1)])
uhistDeltaf1n_j[l, ] <- colSums(histDeltaf1n_j_delta[(floor((l-1)/h)):(floor(l/h)-1),])
uhistDeltaf2n_j[l, ] <- colSums(histDeltaf2n_j_delta[(floor((l-1)/h)):(floor(l/h)-1),])
for(i in c(1:aaa)){
dumIn <- histb_incr[,(floor((l-1)/h)):(floor(l/h)-1),i]
uhistb_incr[,l,i]<- rowSums(dumIn)
}
}
nMeaspar<-myres@model@parameter@measure
mypar_meas0 <- myres@coef[nMeaspar]#res@coef[oldyuima@model@parameter@measure]
mypar_meas <- c(mypar_meas0,1)
ures<[email protected]@original.data
names(mypar_meas)<-c(nMeaspar,myres@model@[email protected])
fdataeta<-dens(object=myres@model@measure$df,x=ures,param=mypar_meas)# f(eps, eta)
del <- 10^-3
dummy <- t(rep(1,length(mypar_meas[myres@model@[email protected]])))
myeta<- as.matrix(mypar_meas[myres@model@[email protected]])%*%dummy
myetapert <- myeta+del*diag(rep(1,dim(myeta)[1]))
fdataetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[myres@model@[email protected]]<-1
dens(object=myres@model@measure$df,x=ures,param=par)
}
)# f(eps, eta+delta)
term1<-1/(fdataeta)
dummyvecdel<-numeric(length=dim(ures)[2])
fdatadeltaeta<- matrix(0,floor(Tn),dim(ures)[2])
mixpartial <-array(0,c(length(nMeaspar),floor(Tn),dim(ures)[2]))
for(j in c(1:dim(ures)[2])){
dummyvecdel[j]<-del
fdatadeltaeta[,j]<- dens(object=myres@model@measure$df,x=ures+rep(1,dim(ures)[1])%*%t(dummyvecdel),param=mypar_meas)
fdatadeltaetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
par[myres@model@[email protected]]<-1
dens(object=myres@model@measure$df,x=ures+rep(1,dim(ures)[1])%*%t(dummyvecdel),param=par)
}
) # f(eps +deta, eta+delta)
dummyvecdel<-numeric(length=dim(ures)[2])
term2 <- fdatadeltaeta[,j]*term1%*%dummy
term2 <- fdatadeltaetadelta-term2*fdataetadelta
mixpartial[,,j]<-t((as.matrix(term1)%*%dummy)/del^2*term2/Tn)
}
DerMeta <- 1/del*(fdataetadelta - as.matrix(fdataeta)%*%rep(1,dim(fdataetadelta)[2]))*(as.matrix(term1)%*%rep(1,dim(fdataetadelta)[2]))
SigmaEta <- t(DerMeta)%*%DerMeta/Tn
minusloglik <- function(para){
para[length(para)+1]<-1
names(para)[length(para)]<-myres@[email protected]
-sum(dens(object=myres@model@measure$df, x=ures, param = para, log = TRUE),
na.rm = T)/Tn
}
# HessianEta_divTn <- optimHess(par=mypar_meas0, fn=minusloglik)
Gammaeta_theta <- matrix(0,length(mypar_meas0), aaa)
for(t in c(1:floor(Tn))){
Gammaeta_theta<-Gammaeta_theta+mixpartial[,t,]%*%uhistb_incr[,t,]
}
GammaEta_Theta<- -1/Tn*Gammaeta_theta
Sigma_eta_theta<-t(DerMeta)%*%cbind(uhistDeltaf1n_j,uhistDeltaf2n_j)/Tn
Sigma <- cbind(rbind(Sigma0,Sigma_eta_theta),rbind(t(Sigma_eta_theta),SigmaEta))
GammaHAT<-cbind(rbind(Gammahat0, GammaEta_Theta), rbind(t(GammaEta_Theta), HessianEta_divTn))
InvGammaHAT <- solve(GammaHAT)
vcov <- InvGammaHAT %*% Sigma %*% InvGammaHAT/Tn
myres@vcov<- vcov
return(myres)
}
result <- vcovLevy(result,HessianEta_divTn,myGamhat,sq=sq)
}
#result <- vcovLevy(result,HessianEta_divTn,myGamhat,sq=sq)
# colnames(result@vcov)<-names(res@fullcoef)
# rownames(result@vcov)<-names(res@fullcoef)
}
# colnames(res@vcov)<-names(res@fullcoef)
# #res@vcov<-rbind(res@vcov,matrix(NA,nrow=length(esti$par),ncol=dim(res@vcov)[2]))
# rownames(res@vcov)<-names(res@fullcoef)
# res@min<-c(res@min,esti$value)
# res@nobs<-c(res@nobs,length([email protected][[1]]))
return(result)
}else{
dist <- substr(as.character(orig.mylaw$df$expr), 2, 10^3)
startjump <- start0[lev.names]
lowerjump <- lower0[lev.names]
upperjump <- upper0[lev.names]
if(length(startjump) == -1){
logdens <- function(para){
exlogdens <- parse(text = sprintf("log(d%s)", dist))
assign(myjumpname, ures, envir = tmp.env)
assign(mymeasureparam, para, envir = tmp.env)
sum(eval(exlogdens, envir = tmp.env))
}
mydens <-function(para){
exdens <- parse(text = sprintf("d%s", dist))
assign(myjumpname, ures, envir = tmp.env)
assign(mymeasureparam, para, envir = tmp.env)
eval(exdens, envir = tmp.env)
}
intervaljump <- c(lowerjump[[1]], upperjump[[1]])
esti <- optimize(logdens, interval = intervaljump, maximum = TRUE)
return(list(sde=esort, meas=esti$maximum))
}else{
logdens <- function(para){
exlogdens <- parse(text = sprintf("log(d%s)", dist))
assign(oldyuima@[email protected], ures, envir = tmp.env)
for(i in c(1:length(oldyuima@model@parameter@measure)))
assign(oldyuima@model@parameter@measure[i], para[[oldyuima@model@parameter@measure[i]]], envir = tmp.env)
-sum(eval(exlogdens, envir = tmp.env),na.rm = T)
}
mydens1 <-function(par,x){
exdens <- parse(text = sprintf("d%s", dist))
assign(myjumpname, x, envir = tmp.env)
assign(mymeasureparam, par, envir = tmp.env)
eval(exdens, envir = tmp.env)
}
esti <- optim(fn=logdens, lower = lowerjump, upper = upperjump, par = startjump,
method = "L-BFGS-B")
HessianEta <- optimHess(par=esti$par, fn=logdens)
res@coef<-c(res@coef,esti$par)
res@fullcoef[lev.names]<-esti$par
if(length(oldyuima@[email protected])==1){
if(!aggregation){
Ter <- yuima@sampling@Terminal
ures <- numeric(floor(Ter))
for(l in 1:floor(Ter)){
ures[l] <- sum(resi[(floor((l-1)/h)):(floor(l/h)-1)])
}
}
mypar<-res@coef[oldyuima@model@parameter@measure]
#mypar[oldyuima@model@[email protected]]<-1
fdataeta<-mydens1(par=mypar,x=ures)# f(eps, eta)
del <- 10^-3
fdatadeltaeta<- mydens1(par=mypar,x=ures+del) # f(eps + delta, eta)
dummy <- t(rep(1,length(mypar[lev.names])))
myeta<- as.matrix(mypar[lev.names])%*%dummy
myetapert <- myeta+del*diag(rep(1,dim(myeta)[1]))
fdataetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
#par[oldyuima@model@[email protected]]<-1
mydens1(par=par,x=ures)
}
)# f(eps, eta+delta)
fdatadeltaetadelta<-sapply(X=1:dim(myeta)[1],FUN = function(X){
par<-myetapert[,X]
#par[oldyuima@model@[email protected]]<-1
mydens1(par=par,x=ures+del)
}
) # f(eps +deta, eta+delta)
term1<-1/(fdataeta)
term2 <- fdatadeltaeta*term1%*%dummy
term2 <- fdatadeltaetadelta-term2*fdataetadelta
mixpartial<-t((as.matrix(term1)%*%dummy)/del^2*term2/oldyuima@sampling@Terminal)
# construction of b_i
# DiffJumpCoeff, DriftDerCoeff, jump.term length(resi)
# step1 <- t(DiffJumpCoeff)%*%DiffJumpCoeff
DerMeta <- 1/del*(fdataetadelta - as.matrix(fdataeta)%*%rep(1,dim(fdataetadelta)[2]))*(as.matrix(term1)%*%rep(1,dim(fdataetadelta)[2]))
SigmaEta <- t(DerMeta)%*%DerMeta/oldyuima@sampling@Terminal
#SigmaEtaAlpha<- 1/oldyuima@sampling@n*DriftDerCoeff%*%(t(DiffJumpCoeff)/(as.matrix(jump.term[-length(jump.term)]^2)%*%rep(1,dim(DiffJumpCoeff)[1])) )
SigmaEtaAlpha<- 1/oldyuima@sampling@n*DriftDerCoeff%*%(t(DiffJumpCoeff)/(as.matrix(jump.term^2)%*%rep(1,dim(DiffJumpCoeff)[1])) )
SigmaEtaAlpha <- SigmaEtaAlpha*sum(resi^3)/oldyuima@sampling@delta
b_i <- matrix(0,floor(Ter),length(c(oldyuima@model@parameter@drift, oldyuima@model@parameter@jump)))
Coef1 <- matrix(0,floor(Ter),length( oldyuima@model@parameter@jump))
Coef2 <- matrix(0,floor(Ter),length( oldyuima@model@parameter@drift))
for(l in 1:floor(Ter)){
pos <- (floor((l-1)/h)):(floor(l/h)-1)
if(length(oldyuima@model@parameter@jump)==1){
b_i[l,1:length(oldyuima@model@parameter@jump)] <- sum(-DiffJumpCoeff[,pos]*resi[pos]/jump.term[pos])
Coef1[l,]<-sum(DiffJumpCoeff[,pos]/jump.term[pos]*(resi[pos]^2-h))
}else{
interm <- as.matrix(resi[pos]/jump.term[pos])
b_i[l,1:length(oldyuima@model@parameter@jump)] <- -t(DiffJumpCoeff[,pos]%*%interm)
interm2 <- as.matrix((resi[pos]^2-h)/jump.term[pos])
Coef1[l,] <-DiffJumpCoeff[,pos]%*%interm2
}
if(length(oldyuima@model@parameter@drift)==1){
b_i[l,1:length(oldyuima@model@parameter@drift)+length(oldyuima@model@parameter@jump)] <- -sum(h* DriftDerCoeff[,pos]%*%jump.term[pos])
Coef2[l,]<-sum(DriftDerCoeff[,pos]/jump.term[pos]*(resi[pos]))
}else{
b_i[l,1:length(oldyuima@model@parameter@drift)+length(oldyuima@model@parameter@jump)] <--h* DriftDerCoeff[,pos]%*%jump.term[pos]
interm3 <- as.matrix((resi[pos])/jump.term[pos])
Coef2[l,] <-DriftDerCoeff[,pos]%*%interm3
}
}
MatrUnder <- mixpartial%*%b_i
I_n <- cbind(rbind(myGamhat,MatrUnder),rbind(matrix(0, dim(myGamhat)[1],dim(HessianEta)[2]),HessianEta/oldyuima@sampling@Terminal))
SigmaGammaEta <- t(DerMeta)%*%Coef1/Ter
SigmaAlphaEta <- t(DerMeta)%*%Coef2/Ter
# dim(fdataetadelta), length(fdataeta), length(term1)
dum <- cbind(SigmaGammaEta , SigmaAlphaEta)
MatSigmaHat1<- rbind(cbind(MatSigmaHat,t(dum)),cbind(dum,SigmaEta))
InvIn<- solve(I_n)
res@vcov <-InvIn%*%MatSigmaHat1%*%t(InvIn)/Ter
}else{
res@vcov<-cbind(res@vcov,matrix(NA,ncol=length(esti$par),nrow=dim(res@vcov)[1]))
}
colnames(res@vcov)<-names(res@fullcoef)
#res@vcov<-rbind(res@vcov,matrix(NA,nrow=length(esti$par),ncol=dim(res@vcov)[2]))
rownames(res@vcov)<-names(res@fullcoef)
res@min<-c(res@min,esti$value)
res@nobs<-c(res@nobs,length([email protected][[1]]))
result<- new("yuima.qmleLevy.incr",Incr.Lev=Incr.Lev,
minusloglLevy = logdens,logL.Incr=-esti$value,
Data = yuima@data, yuima=res, Levydetails=esti)
return(result)
#return(list(sde=esort, meas=esti$par))
}
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/qmleLevy.R |
##################################################################
###### "Random number generators and
###### related density functions for yuima packages"
##################################################################
## Bilateral gamma
rbgamma <- function(x,delta.plus,gamma.plus,delta.minus,gamma.minus){
if( delta.plus <= 0 )
stop("delta.plus must be positive.")
if( gamma.plus <= 0 )
stop("gamma.plus must be positive.")
if( delta.minus <= 0 )
stop("delta.minus must be positive.")
if( gamma.minus <= 0 )
stop("gamma.minus must be positive.")
X <- rgamma(x,delta.plus,gamma.plus) - rgamma(x,delta.minus,gamma.minus)
return(X)
}
# "dbgamma" by YU.
dbgamma<-function(x,delta.plus,gamma.plus,delta.minus,gamma.minus){
## Error check
if(length(delta.plus)!=1||length(gamma.plus)!=1||length(delta.minus)!=1||length(gamma.minus)!=1)
stop("All of the parameters are numeric.")
if(delta.plus<=0||gamma.plus<=0||delta.minus<=0||gamma.minus<=0)
stop("All of the parameters are positive.")
leng <- length(x)
dens <- numeric(leng)
for(i in 1:leng){
if(x[i]>=0){
## On the positive line
funcp<-function(x,y){y^{delta.minus-1}*(x+y/(gamma.plus+gamma.minus))^{delta.plus-1}*exp(-y)}
intp<-function(x){integrate(funcp,lower=0,upper=Inf,x=x)$value}
intvecp<-function(x)sapply(x,intp)
dens[i]<-gamma.plus^delta.plus*gamma.minus^delta.minus/((gamma.plus+gamma.minus)^delta.minus*gamma(delta.plus)*gamma(delta.minus))*exp(-gamma.plus*x[i])*intvecp(x[i])
}else{
## On the negative line
funcm<-function(x,y){y^{delta.plus-1}*(-x+y/(gamma.plus+gamma.minus))^{delta.minus-1}*exp(-y)}
intm<-function(x){integrate(funcm,lower=0,upper=Inf,x=x)$value}
intvecm<-function(x)sapply(x,intm)
dens[i]<-gamma.plus^delta.plus*gamma.minus^delta.minus/((gamma.plus+gamma.minus)^delta.plus*gamma(delta.minus)*gamma(delta.plus))*exp(gamma.minus*x[i])*intvecm(x[i])
}
}
dens
}
## Generalized inverse Gaussian
rGIG<-function(x,lambda,delta,gamma){
if((delta < 0)||(gamma<0)){
stop("delta and gamma must be nonnegative.")
}
if((delta == 0) && (lambda <= 0)){
stop("delta must be positive when lambda is nonpositive.")
}
if((gamma == 0) && (lambda >= 0)){
stop("gamma must be positive when lambda is nonnegative.")
}
if(lambda >= 0){
if(delta == 0){ ## gamma case
X<-rgamma(x, lambda, 1/(2*gamma^2))
}
if(gamma == 0){ ## inverse gamma case
X<-delta^2/(2*rgamma(x,-lambda,1))
}
if(delta != 0){
X<-.C("rGIG", as.integer(x), as.double(lambda), as.double(delta^2), as.double(gamma^2),
rn = double(length = x))$rn}
}
else{
Y<-.C("rGIG", as.integer(x), as.double(-lambda), as.double(gamma^2), as.double(delta^2),
rn = double(length = x))$rn
X<-1/Y
}
return(X)
}
dGIG<-function(x,lambda,delta,gamma){
if(x <= 0)
stop("x must be positive")
if((delta < 0)||(gamma<0)){
stop("delta and gamma must be nonnegative.")
}
if((delta == 0) && (lambda <= 0)){
stop("delta must be positive when lambda is nonpositive.")
}
if((gamma == 0) && (lambda >= 0)){
stop("gamma must be positive when lambda is nonnegative.")
}
if(delta == 0){ ## gamma case
dens <- dgamma(x,lambda,1/(2*gamma^2))
}
if(gamma == 0){ ## inverse gamma case
dens <- x^(lambda-1)*exp(-1/2*delta/x)/(gamma(-lambda)*2^(-lambda)*delta^(2*lambda))
}else{
dens <- 1/2*(gamma/delta)^lambda*1/besselK(gamma*delta,lambda)*x^(lambda-1)*exp(-1/2*(delta^2/x+gamma^2*x))
}
return(dens)
}
## Generalized hyperbolic
rGH<-function(x,lambda,alpha,beta,delta,mu,Lambda){
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(missing(Lambda))
Lambda <- NA
if( alpha < 0 ){
stop("alpha must be nonnegative.")
}
## variance gamma case
if(delta == 0){
X<-rvgamma(x,lambda,alpha,beta,mu,lambda)
}
## univariate case
if(any(is.na(Lambda))){
if(length(mu)!=1 || length(beta)!=1){
stop("Error: wrong input dimension.")
}
tmp <- as.numeric(alpha^2 - beta^2)
if( tmp < 0 ){
stop("alpha^2 - beta^2 must be nonnegative value.")
}
if(((alpha == 0)||(tmp == 0)) && (lambda >= 0)){
stop("alpha and alpha^2 - beta^2 must be positive when lambda is nonnegative.")
}
Z<-rGIG(x,lambda,delta,sqrt(tmp))
N<-rnorm(x,0,1)
X<-mu + beta*Z + sqrt(Z)*N
}
else{## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp <- as.numeric(alpha^2 - t(beta) %*% Lambda %*% beta)
if(tmp < 0){
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be nonnegative")
}
if(((alpha == 0)||(tmp == 0))&&(lambda >=0)){
stop("alpha and alpha^2 - t(beta) %*% Lambda %*% beta must be positive when lambda is nonnegative.")
}
Z<-rGIG(x,lambda,delta,sqrt(tmp))
N<-rnorm(x*length(beta),0,1)
sqrt.L <- svd(Lambda)
sqrt.L <- sqrt.L$u %*% diag(sqrt(sqrt.L$d)) %*% t(sqrt.L$v)
X <- mu + t(matrix(rep(Z,length(beta)),x,length(beta))) * matrix(rep(Lambda %*% beta,x),length(beta),x)+t(matrix(rep(sqrt(Z),length(beta)),x,length(beta))) * (sqrt.L %*% t(matrix(N,x,length(beta))))
}
return(X)
}
dGH<-function(x,lambda,alpha,beta,delta,mu,Lambda){
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(missing(Lambda))
Lambda <- NA
if( alpha < 0 ){
stop("alpha must be nonnegative.")
}
## variance gamma case
if(delta == 0){
X<-dvgamma(x,lambda,alpha,beta,mu,Lambda)
}
## univariate case
if(any(is.na(Lambda))){
if(length(mu)!=1 || length(beta)!=1){
stop("Error: wrong input dimension.")
}
tmp <- as.numeric(alpha^2 - beta^2)
if( tmp < 0 ){
stop("alpha^2 - beta^2 must be nonnegative value.")
}
if(((alpha == 0)||(tmp == 0)) && (lambda >= 0)){
stop("alpha and alpha^2 - beta^2 must be positive when lambda is nonnegative.")
}
nu<--2*lambda
if(alpha == 0){## gamma = 0 (in gig), scaled t-distribution
dens<-gamma((nu+1)/2)*(1+((x-mu)/delta)^2)^(-(nu+1)/2)/(delta*sqrt(pi)*gamma(nu/2))
}else if(tmp == 0){## skewed t-distribution
dens<-delta^nu*exp(beta*(x-mu))*gamma((nu+1)/2)*besselK(abs(beta)*sqrt(delta^2+(x-mu)^2),(nu+1)/2)/(2^((nu-1)/2)*sqrt(pi)*gamma(nu/2)*(sqrt(delta^2+(x-mu)^2)/abs(beta))^((nu+1)/2))
}else{
dens<-tmp^(lambda/2)*sqrt(delta^2+(x-mu)^2)^(lambda-1/2)*besselK(alpha*sqrt(delta^2+(x-mu)^2),lambda-1/2)*exp(beta*(x-mu))/(sqrt(2*pi)*alpha^(lambda-1/2)*delta^lambda*besselK(delta*sqrt(tmp),lambda))
}
}
else{## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp <- as.numeric(alpha^2 - t(beta) %*% Lambda %*% beta)
if(tmp < 0){
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be nonnegative")
}
if(((alpha == 0)||(tmp == 0))&&(lambda >=0)){
stop("alpha and alpha^2 - t(beta) %*% Lambda %*% beta must be positive when lambda is nonnegative.")
}
Lambdainv<-solve(Lambda)
nu<--2*lambda
d<-nrow(Lambda)
if(alpha == 0){ ## gamma = 0 (in gig) multivariate scaled t-distribution
dens<-gamma((nu+d)/2)*(1+t(x-mu)%*%Lambdainv%*%(x-mu)/delta^2)^(-(nu+d)/2)/(pi^(d/2)*gamma(nu/2)*delta^d)
}else if(tmp == 0){ ## multivariate skewed t-distribution
dens<-delta^nu*exp(t(beta)%*%(x-mu))*besselK(alpha*sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu)),-(nu+d)/2)/((pi)^(d/2)*2^((nu+d)/2-1)*(sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu))/alpha)^((nu+d)/2)*gamma(nu/2))
}else{
dens<-exp(t(beta)%*%(x-mu))*(sqrt(tmp)/delta)^lambda*besselK(alpha*sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu)),-(nu+d)/2)/((2*pi)^(d/2)*besselK(delta*sqrt(tmp),lambda)*(sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu))/alpha)^(d/2-lambda))
}
}
return(dens)
}
## (Multivariate) Variance gamma
rvgamma <- function(x,lambda,alpha,beta,mu,Lambda){
## Error check
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(missing(Lambda))
Lambda <- NA
if(any(is.na(Lambda))){
## univariate case
if(length(mu)!=1 || length(beta)!=1){
stop("Error: wrong input dimension.")
}
tmp <- as.numeric(alpha^2 - beta^2)
if( lambda <= 0 ){
stop("lambda must be positive.")
}
if( alpha <= 0 ){
stop("alpha must be positive.")
}
if( tmp <= 0 ){
stop("alpha^2 - beta^2 must be positive value.")
}
tau <- rgamma(x,lambda,tmp/2)
eta <- rnorm(x)
## z <- mu + beta * tau * Lambda + sqrt(tau * Lambda) * eta
z <- mu + beta * tau + sqrt(tau) * eta
X <- z
return(X)
}else{ ## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp <- as.numeric(alpha^2 - t(beta) %*% Lambda %*% beta)
if( lambda <= 0 )
stop("lambda must be positive.")
if( alpha <= 0 )
stop("alpha must be positive.")
if( tmp <=0)
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be positive.")
tau <- rgamma(x,lambda,tmp/2)
eta <- rnorm(x*length(beta))
sqrt.L <- svd(Lambda)
sqrt.L <- sqrt.L$u %*% diag(sqrt(sqrt.L$d)) %*% t(sqrt.L$v)
z <- mu + t(matrix(rep(tau,length(beta)),x,length(beta))) * matrix(rep(Lambda %*% beta,x),length(beta),x)+t(matrix(rep(sqrt(tau),length(beta)),x,length(beta))) * (sqrt.L %*% t(matrix(eta,x,length(beta))))
X <- z
return(X)
}
}
dvgamma <- function(x,lambda,alpha,beta,mu,Lambda){
## Error check
if(length(lambda)!=1||length(alpha)!=1)
stop("alpha and lambda must be positive reals.")
if( lambda <= 0 )
stop("lambda must be positive.")
if( alpha <= 0 )
stop("alpha must be positive.")
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(missing(Lambda))
Lambda <- NA
if(any(is.na(Lambda))){
## univariate case
if(length(mu)!=1 || length(beta)!=1){
stop("Error: wrong input dimension.")
}
if( alpha^2 - beta^2 <= 0 )
stop("alpha^2 - beta^2 must be positive.")
dens <- exp(beta*(x-mu))*((alpha^2 - beta^2)^(lambda))*besselK(alpha*abs(x-mu),lambda-1/2)*abs(x-mu)^(lambda-1/2)/(gamma(lambda)*sqrt(pi)*((2*alpha)^(lambda-1/2)))
dens}
else{ ## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp <- as.numeric(alpha^2 - t(beta) %*% Lambda %*% beta)
if( tmp <=0)
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be positive.")
Lambdainv<-solve(Lambda)
dens<- exp(t(beta)%*%(x-mu))*(alpha^2-t(beta)%*%Lambda%*%beta)^(lambda)*besselK(alpha*sqrt(t(x-mu)%*%Lambdainv%*%(x-mu)),lambda-nrow(Lambda)/2)*sqrt(t(x-mu)%*%Lambdainv%*%(x-mu))^{lambda-nrow(Lambda)/2}/(gamma(lambda)*pi^{nrow(Lambda)/2}*2^{nrow(Lambda)/2+lambda-1}*alpha^{lambda-nrow(Lambda)/2})
dens
}
}
## Inverse Gaussian
rIG <- function(x,delta,gamma){
if( delta <= 0 )
stop("delta must be positive.")
if( gamma <= 0 )
stop("gamma must be positive.")
V <- rchisq(x,df=1);
z1 <- ( delta/gamma + V/(2*gamma^2) ) - sqrt( V*delta/(gamma^3) + ( V/(2*gamma^2) )^2 )
U <- runif(x,min=0,max=1)
idx <- which( U < (delta/(delta+gamma*z1)) )
z2 <- (delta/gamma)^2 /z1[-idx]
ret <- numeric(x)
ret[idx] <- z1[idx]
ret[-idx] <- z2
return(ret)
}
dIG <- function(x,delta,gamma){
if(x <= 0)
stop("x must be positive")
if( delta <= 0 )
stop("delta must be positive.")
if( gamma <= 0 )
stop("gamma must be positive.")
dens <- delta*exp(delta*gamma)*(x^(-3/2))*exp(-((delta^2)/x+x*gamma^2)/2)/sqrt(2*pi)
dens
}
## (Multivariate) Normal inverse Gaussian
rNIG <- function(x,alpha,beta,delta,mu,Lambda){
## Error check
#print(match.call())
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(length(alpha)>1||length(delta)>1)
stop("alpha and delta must be positive reals.")
if( alpha < 0 )
stop("alpha must be nonnegative.")
if( delta <= 0 )
stop("delta must be positive.")
if(missing(Lambda))
Lambda <- NA
if(any(is.na(Lambda)) & length(Lambda)==1){
## univariate case
gamma <- sqrt(alpha^2 - beta^2)
if(gamma <0){
stop("alpha^2-beta^2 must be positive.")
}
if (gamma == 0) {
V = rnorm(x)^2
Z = delta * delta/V
X = sqrt(Z) * rnorm(x)
}else{
Z <- rIG(x,delta,gamma)
N <- rnorm(x,0,1)
X <- mu + beta*Z + sqrt(Z)*N
}
return(X)
}else{ ## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp <- as.numeric(alpha^2 - t(beta) %*% Lambda %*% beta)
if(tmp <0){
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be nonnegative.")
}
gamma <- sqrt(tmp)
}
tau <- rIG(x,delta,gamma)
eta <- rnorm(x*length(beta))
sqrt.L <- svd(Lambda)
sqrt.L <- sqrt.L$u %*% diag(sqrt(sqrt.L$d)) %*% t(sqrt.L$v)
z <- mu + t(matrix(rep(tau,length(beta)),x,length(beta))) * matrix(rep(Lambda %*% beta,x),length(beta),x)+t(matrix(rep(sqrt(tau),length(beta)),x,length(beta))) * (sqrt.L %*% t(matrix(eta,x,length(beta))))
X <- z
# print(str(X))
return(X)
}
dNIG <- function(x,alpha,beta,delta,mu,Lambda){
## Error check
#print(match.call())
if(length(alpha)>1||length(delta)>1)
stop("alpha and delta must be positive reals.")
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if( alpha < 0 )
stop("alpha must be nonnegative.")
if( delta <= 0 )
stop("delta must be positive.")
if(missing(Lambda))
Lambda <- NA
if(any(is.na(Lambda))){
#univraiate case
if(length(beta)>1||length(mu)>1)
stop("beta and mu must be numeric")
if( alpha^2 - beta^2 < 0 )
stop("alpha^2 - beta^2 must be nonnegative.")
dens <- alpha*delta*exp(delta*sqrt(alpha^{2}-beta^{2})+beta*(x-mu))*besselK(alpha*sqrt(delta^{2}+(x-mu)^{2}),1)/(pi*sqrt(delta^{2}+(x-mu)^{2}))
dens
}else{
#multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if(sum((Lambda-t(Lambda))*(Lambda-t(Lambda)))!=0){
stop("Lambda must be a symmetric matrix")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if(nrow(Lambda)!=length(mu)){
stop("Dimension of Lambda and mu must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tmp<-alpha-t(beta)%*%Lambda%*%beta
if(tmp <0){
stop("alpha^2 - t(beta) %*% Lambda %*% beta must be nonnegative.")
}
Lambdainv<-solve(Lambda)
dens<- alpha^{(nrow(Lambda)+1)/2}*delta*exp(delta*sqrt(tmp)+t(beta)%*%(x-mu))*besselK(alpha*sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu)),nrow(Lambda))/(2^{(nrow(Lambda)-1)/2}*pi^{(nrow(Lambda)+1)/2}*(sqrt(delta^2+t(x-mu)%*%Lambdainv%*%(x-mu)))^{(nrow(Lambda)+1)/2})
dens
}
}
## ## One-dim. NIG
## rNIG <- function(x,alpha=1,beta=0,delta=1,mu=0){
## gamma <- sqrt(alpha^2 - beta^2)
## if (gamma == 0) {
## V = rnorm(x)^2
## Z = delta * delta/V
## X = sqrt(Z) * rnorm(x)
## }else{
## Z <- rIG(x,delta,gamma)
## N <- rnorm(x,0,1)
## X <- mu + beta*Z + sqrt(Z)*N
## }
## return(X)
## }
## Univariate non-Gaussian stable: corrected Weron's algorithm incorporated
## (20160114, HM) "dstable" still unavailable in YUIMA... incorporate "stabledist" package?
rstable <- function(x,alpha,beta,sigma,gamma){
if( alpha <= 0 || alpha > 2)
stop("The index alpha must take values in (0,2].")
if( beta < -1 || beta > 1)
stop("The skeweness beta must take values in [-1,1].")
if( sigma <= 0 )
stop("The scale sigma must be positive.")
a <- (1 + (beta*tan(alpha*pi/2))^2)^(1/(2*alpha))
b <- atan(beta*tan(alpha*pi/2))/alpha
u <- runif(x,-pi/2,pi/2)
v <- rexp(x,1)
if(alpha!=1){
s <- a * (sin(alpha*(u+b))/cos(u)^(1/alpha)) * (cos(u-alpha*(u+b))/v)^((1-alpha)/alpha)
X <- sigma * s + gamma * rep(1,x)
}else{
s <- (2/pi) * ((pi/2 +beta*u)*tan(u) - beta * log((pi/2)*v*cos(u)/(beta*u + pi/2)))
X <- sigma * s + (gamma + (2/pi)*beta*sigma*log(sigma)) * rep(1,x)
}
return(X)
}
## rstable <- function(x,alpha,beta,sigma, gamma, eps){
## a <- (1 + (beta*tan(alpha*pi/2))^2)^(1/(2*alpha))
## b <- atan(beta*tan(alpha*pi/2))/alpha
## u <- runif(x,-pi/2,pi/2)
## v <- rexp(x,1)
## if(alpha!=1){
## s <- a * (sin(alpha*(u+b))/cos(u)^(1/alpha)) * (cos(u-alpha*(u+b))/v)^((1-alpha)/alpha)
## }else{
## s <- (2/pi) * ((pi/2 +beta*u)*tan(u) - beta * log(v*cos(u)/(beta*u + pi/2)))
## }
## X <- (eps^(1/alpha)) * sigma * s + gamma * eps * rep(1,x)
## return(X)
## }
## Positive exponentially tempered stable (AR method)
rpts<-function(x,alpha,a,b){
if( alpha <= 0 | alpha>= 1 )
stop("alpha must lie in (0,1).")
if( a <= 0 )
stop("a must be positive value.")
if( b <= 0 )
stop("b must be positive value.")
ar<-exp(a*gamma(-alpha)*b^(alpha))
if(ar <= 0.1)
stop("Acceptance rate is too small.")
else
.C("rpts",as.integer(x),as.double(alpha),as.double(a),as.double(b),rn=double(length=x))$rn}
## Normal tempered stable
rnts<-function(x,alpha,a,b,beta,mu,Lambda){
## Error check
if(length(mu)!=length(beta)){
stop("Error: wrong input dimension.")
}
if(missing(Lambda))
Lambda <- NA
if( alpha <= 0 || alpha > 2 ){
stop("The index alpha must take values in (0,2].")
}
if( a <= 0 )
stop("a must be positive value.")
if( b <= 0 )
stop("b must be positive value.")
if(any(is.na(Lambda))){
## univariate case
if(length(mu)!=1 || length(beta)!=1){
stop("Error: wrong input dimension.")
}
tau <- rpts(x,alpha,a,b)
eta <- rnorm(x)
## z <- mu + beta * tau * Lambda + sqrt(tau * Lambda) * eta
z <- mu + beta * tau + sqrt(tau) * eta
X <- z
return(X)
}else{ ## multivariate case
if( nrow(Lambda)!=ncol(Lambda)){
stop("Lambda must be a square matrix.")
}
if( nrow(Lambda)!=length(beta)){
stop("Dimension of Lambda and beta must be equal.")
}
if(nrow(Lambda)!=length(mu)){
stop("Dimension of Lambda and mu must be equal.")
}
if( min(eigen(Lambda)$value) <= 10^(-15) ){
stop("Lambda must be positive definite.")
}
if( det(Lambda) > 1+10^(-15) || det(Lambda) < 1-10^(-15) ){
stop("The determinant of Lambda must be 1.")
}
tau<-rpts(x,alpha,a,b)
eta <- rnorm(x*length(beta))
sqrt.L <- svd(Lambda)
sqrt.L <- sqrt.L$u %*% diag(sqrt(sqrt.L$d)) %*% t(sqrt.L$v)
z <- mu + t(matrix(rep(tau,length(beta)),x,length(beta))) * matrix(rep(Lambda %*% beta,x),length(beta),x)+t(matrix(rep(sqrt(tau),length(beta)),x,length(beta))) * (sqrt.L %*% t(matrix(eta,x,length(beta))))
z<-mu+ t(matrix(rep(tau,length(beta)),x,length(beta))) * matrix(rep(Lambda %*% beta,x),length(beta),x)+t(matrix(rep(sqrt(tau),length(beta)),x,length(beta))) * (sqrt.L %*% t(matrix(eta,x,length(beta))))
X <- z
return(X)
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/rng.R |
sampling2grid <- function(samp){
#Check if there is already a grid
if (length(samp@grid)>0){
#It is the general case where there is a grid
#including irregular deterministic case
return(samp@grid)
}
#Check if this grid is random
if (samp@random==FALSE){
#Regular deterministic grid
return(seq(samp@Initial[1],samp@Terminal[1],length=samp@n[1]+1)) #Attention! do not treat multifrequency grid
}
return(1) #To do: Random grid
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/sampling2grid.R |
setMethod("initialize", "carma.info",
function(.Object,
p=numeric(),
q=numeric(),
loc.par=character(),
scale.par=character(),
ar.par=character(),
ma.par=character(),
lin.par=character(),
Carma.var=character(),
Latent.var=character(),
XinExpr=logical()){
.Object@p <- p
.Object@q <- q
[email protected] <- loc.par
[email protected] <- scale.par
[email protected] <- ar.par
[email protected] <- ma.par
[email protected] <- lin.par
[email protected] <- Carma.var
[email protected] <- Latent.var
.Object@XinExpr <- XinExpr
return(.Object)
})
setMethod("initialize", "yuima.carma",
function(.Object,
info = new("carma.info"),
drift = expression() ,
diffusion = list() ,
hurst = 0.5,
jump.coeff = expression(),
measure=list(),
measure.type=character(),
parameter = new("model.parameter"),
state.variable = "x",
jump.variable = "z",
time.variable = "t",
noise.number = numeric(),
equation.number = numeric(),
dimension = numeric(),
solve.variable = character(),
xinit = expression(),
J.flag = logical()){
.Object@info <- info
.Object@drift <- drift
.Object@diffusion <- diffusion
.Object@hurst <- hurst
[email protected] <- jump.coeff
.Object@measure <- measure
[email protected] <- measure.type
.Object@parameter <- parameter
[email protected] <- state.variable
[email protected] <- jump.variable
[email protected] <- time.variable
[email protected] <- noise.number
[email protected] <- equation.number
.Object@dimension <- dimension
[email protected] <- solve.variable
.Object@xinit <- xinit
[email protected] <- J.flag
return(.Object)
})
setCarma<-function(p,
q,
loc.par=NULL,
scale.par=NULL,
ar.par="a",
ma.par="b",
lin.par=NULL,
Carma.var="v",
Latent.var="x",
XinExpr=FALSE,
Cogarch=FALSE,
...){
# We use the same parametrization as in Brockwell (2000)
# mydots$Carma.var= V
# mydots$Latent.var= X ?????
# The Carma model is defined by two equations:
# \begin{eqnarray}
# V_{t}&=&\alpha_{0}+a'Y_{t-}\\
# dY_{t}&=& BY_{t-}dt+ e dL\\
# \end{eqnarray}
# p is the number of the moving average parameters \alpha
# q is the number of the autoregressive coefficient \beta
#Default parameters
if (is.null(scale.par)){
ma.par1<-ma.par
} else{
ma.par1<-paste(scale.par,ma.par,sep="*")
}
call <- match.call()
quadratic_variation<-FALSE
mydots <- as.list(call)[-(1:2)]
# hurst<-0.5
# jump.coeff <- character()
# measure <- list()
# measure.type <- character()
# state.variable <- "Y"
# jump.variable <- "z"
# time.variable <- "t"
# mydots$xinit<- NULL
if (is.null(mydots$hurst)){
mydots$hurst<-0.5
}
if(is.null(mydots$time.variable)){
mydots$time.variable<-"t"
}
if(is.null(mydots$jump.variable)){
mydots$jump.variable<-"z"
}
# if(is.null(mydots$xinit)){
# if(is.null(mydots$XinExpr)){
# mydots$xinit<-as.character(0*c(1:p))
# }else{
# if(mydots$XinExpr==TRUE){
# Int.Var<-paste(Latent.var,"0",sep="")
# mydots$xinit<-paste(Int.Var,c(0:(p-1)),sep="")
# }
# }
# } else{
# dummy<-as.character(mydots$xinit)
# mydots$xinit<-dummy[-1]
# }
if(is.null(mydots$xinit)){
if(XinExpr==FALSE){
mydots$xinit<-as.character(0*c(1:p))
}else{
if(XinExpr==TRUE){
Int.Var<-paste(Latent.var,"0",sep="")
if(Cogarch==FALSE){
mydots$xinit<-paste(Int.Var,c(0:(p-1)),sep="")
}else{
mydots$xinit<-paste(Int.Var,c(1:p),sep="")
}
}
}
} else{
dummy<-as.character(mydots$xinit)
mydots$xinit<-dummy[-1]
}
if(p<q){
yuima.stop("order of AR must be larger than MA order")
}
beta_coeff0<-paste("-",ar.par,sep="")
beta_coeff<-paste(beta_coeff0,p:1,sep="")
if(Cogarch==FALSE){
coeff_alpha<-c(paste(ma.par1,0:q,sep=""),as.character(matrix(0,1,p-q-1)))
}else{
coeff_alpha<-c(paste(ma.par1,1:(q+1),sep=""),as.character(matrix(0,1,p-q-1)))
}
fin_alp<-length(coeff_alpha)
if(Cogarch==FALSE){
Y_coeff<-paste(Latent.var,0:(p-1),sep="")
# Y_coeff<-paste(Int.Var,0:(p-1),sep="")
}else{
Y_coeff<-paste(Latent.var,1:p,sep="")
}
fin_Y<-length(Y_coeff)
# V1<-paste(coeff_alpha,Y_coeff,sep="*")
V1<-paste(coeff_alpha,mydots$xinit,sep="*")
V2<-paste(V1,collapse="+")
# alpha0<-paste(ma.par1,0,sep="")
if(is.null(loc.par)){
Vt<-V2
V<-paste0("(",V2,")",collapse="")
} else {
Vt<-paste(loc.par,V2,sep="+")
V<-paste0("(",Vt,")",collapse="")
}
drift_last_cond<-paste(paste(beta_coeff,Y_coeff,sep="*"),collapse="")
# Drift condition for the dV_{t}
drift_first_cond_1<-c(paste(coeff_alpha[-fin_alp],Y_coeff[-1],sep="*"))
drift_first_cond_2<-paste(drift_first_cond_1,collapse="+")
drift_first_cond_a<-paste("(",drift_last_cond,")",sep="")
drift_first_cond_b<-paste(coeff_alpha[fin_alp],drift_first_cond_a,sep="*")
drift_first_cond<-paste(drift_first_cond_2,drift_first_cond_b,sep="+")
if(length(Y_coeff)>1)
{drift_Carma<-c(drift_first_cond,Y_coeff[2:length(Y_coeff)],drift_last_cond)}else
{drift_Carma<-c(drift_first_cond,drift_last_cond)}
# We need to consider three different situations
if(is.null(mydots$jump.coeff) & is.null(mydots$measure) &
is.null(mydots$measure.type) & quadratic_variation==FALSE){
# The Carma model is driven by a Brwonian motion
if (is.null(lin.par)){
diffusion_Carma<-matrix(c(coeff_alpha[fin_alp],as.character(matrix(0,(p-1),1)),"1"),(p+1),1)
# Latent.var<-Y_coeff
Model_Carma<-setModel(drift=drift_Carma,
diffusion=diffusion_Carma,
hurst=mydots$hurst,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(Vt,mydots$xinit))
#25/11
#
# carma.info<-new("carma.info",
# p=p,
# q=q,
# loc.par="character",
# scale.par="character",
# ar.par=ar.par,
# ma.par=ma.par,
# Carma.var=Carma.var,
# Latent.var=Latent.var,
# XinExpr=XinExpr)
if(length(Model_Carma)==0){
stop("Yuima model was not built")
} else {
# return(Model_Carma1)
}
} else{
if(ma.par==lin.par){
first_term<-paste(coeff_alpha[fin_alp],V,sep="*")
diffusion_Carma<-matrix(c(first_term,as.character(matrix(0,(p-1),1)),V),(p+1),1)
if(Cogarch==FALSE){
Model_Carma<-setModel(drift=drift_Carma,
diffusion=diffusion_Carma,
hurst=mydots$hurst,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(Vt,mydots$xinit))
}else{# We add this part to have as initial condition for the COGARCH model V_0=a_0+a'X_0 LM 13/02/2015
V01<-paste(coeff_alpha,mydots$xinit,sep="*")
V02<-paste(V01,collapse="+")
V0t<-paste(loc.par,V02,sep="+")
Model_Carma<-setModel(drift=drift_Carma,
diffusion=diffusion_Carma,
hurst=mydots$hurst,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(V0t,mydots$xinit))
}
# return(Model_Carma1)
}else{
# coeff_gamma<-c(paste(lin.par,1:p,sep=""),as.character(matrix(0,1,p-q)))
coeff_gamma<-c(paste(lin.par,1:p,sep=""))
Gamma1<-paste(coeff_gamma,Y_coeff,sep="*")
Gamma2<-paste(Gamma1,collapse="+")
gamma0<-paste(lin.par,0,sep="")
Gammat<-paste(gamma0,Gamma2,sep="+")
Gamma<-paste("(",Gammat,")",collapse="")
first_term<-paste(coeff_alpha[fin_alp],Gamma,sep="*")
diffusion_Carma<-matrix(c(first_term,as.character(matrix(0,(p-1),1)),Gamma),(p+1),1)
# Model_Carma1<-setModel(drift=drift_Carma,
# diffusion=diffusion_Carma,
# hurst=mydots$hurst,
# state.variable=c(Carma.var,Y_coeff),
# solve.variable=c(Carma.var,Y_coeff),
# xinit=c(V,mydots$xinit))
Model_Carma<-setModel(drift=drift_Carma,
diffusion=diffusion_Carma,
hurst=mydots$hurst,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(Vt,mydots$xinit))
# return(Model_Carma1)
}
}
} else {
if( is.null(mydots$jump.coeff) & is.null(mydots$measure) &
is.null(mydots$measure.type) & is.null(lin.par) &
quadratic_variation==TRUE){
stop("The CoGarch model requires a Carma process driven by the discrete part of the quadratic covariation:
You Must specify the Levy Measure")
} else {
if(quadratic_variation==FALSE & is.null(lin.par)){
# warning("At the moment, we need specify the underlying L?vy directly")
# diffusion_Carma<-matrix(c(coeff_alpha[fin_alp],as.character(matrix(0,(q-1),1)),"1"),(q+1),1)
# Model_Carma1<-setModel(drift=drift_Carma, diffusion=diffusion_Carma,
# hurst=hurst, state.variable=c(V,Y_coeff), solve.variable=c(V,Y_coeff))
# under_Lev1<-setModel(drift="0",diffusion="0",jump.coeff="1" ,
# measure=measure ,measure.type=measure.type ,
# jump.variable=jump.variable , time.variable=time.variable)
# if(length(Model_Carma1)==0){
# stop("Yuima model was not built")
# } else {
# Model_Carma<-Carma_Model()
# Model_Carma@model <- Model_Carma1
# Model_Carma@Cogarch_Model_Log <- Cogarch_Model
# Model_Carma@Under_Lev <-under_Lev1
# return(Model_Carma)
# }
# LM 27/09 We use a modified
# setModel that allows us to build a sde where the slot [email protected] is an vector
# jump_Carma<-matrix(c(coeff_alpha[fin_alp],as.character(matrix(0,(q-1),1)),"1"),(q+1),1)
jump_Carma<-c(coeff_alpha[fin_alp],as.character(matrix(0,(p-1),1)),"1")
# Model_Carma<-setModel(drift=drift_Carma,
# diffusion = NULL,
# hurst=mydots$hurst,
# jump.coeff=jump_Carma,
# measure=eval(mydots$measure),
# measure.type=mydots$measure.type,
# jump.variable=mydots$jump.variable,
# time.variable=mydots$time.variable,
# state.variable=c(Carma.var,Y_coeff),
# solve.variable=c(Carma.var,Y_coeff),
# xinit=c(V,mydots$xinit))
#
Model_Carma<-setModel(drift=drift_Carma,
diffusion = NULL,
hurst=mydots$hurst,
jump.coeff=as.matrix(jump_Carma),
measure=eval(mydots$measure),
measure.type=eval(mydots$measure.type),
jump.variable=mydots$jump.variable,
time.variable=mydots$time.variable,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(Vt,mydots$xinit))
# return(Model_Carma)
} else {
if (quadratic_variation==FALSE ){
# Selecting Quadratic_Variation==FALSE and specifying the Heteroskedatic.param in the model,
# The coefficient of the error term is a vector in which the last element is an affine linear function
# of the vector space Y
# We have to consider two different cases:
# a) The last component of the error term is $V_{t-}=\alpha_{0}+a'Y_{t-}$. Usually
# this condition is used for the definition of the COGARCH(p,q) introduced in Brockwell and Davis and
# b) The last component of the error term is a linear function of the state variable $Y_{t}$
# different of the observable variable V.
if(ma.par==lin.par){
jump_first_comp<-paste(coeff_alpha[fin_alp],V,sep="*")
jump_Carma<-c(jump_first_comp,as.character(matrix(0,(p-1),1)),V)
}else{
# coeff_gamma<-c(paste(lin.par,1:p,sep=""),as.character(matrix(0,1,q-p)))
coeff_gamma<-c(paste(lin.par,1:p,sep=""))
Gamma1<-paste(coeff_gamma,Y_coeff,sep="*")
Gamma2<-paste(Gamma1,collapse="+")
gamma0<-paste(lin.par,0,sep="")
Gammat<-paste(gamma0,Gamma2,sep="+")
Gamma<-paste("(",Gammat,")",collapse="")
jump_first_comp<-paste(coeff_alpha[fin_alp],Gamma,sep="*")
jump_Carma<-c(jump_first_comp,as.character(matrix(0,(p-1),1)),Gamma)
}
# Model_Carma<-setModel(drift=drift_Carma,
# diffusion =NULL,
# hurst=0.5,
# jump.coeff=jump_Carma,
# measure=eval(mydots$measure),
# measure.type=mydots$measure.type,
# jump.variable=mydots$jump.variable,
# time.variable=mydots$time.variable,
# state.variable=c(Carma.var,Y_coeff),
# solve.variable=c(Carma.var,Y_coeff),
# c(V,mydots$xinit))
# return(Model_Carma)
}
Model_Carma<-setModel(drift=drift_Carma,
diffusion =NULL,
hurst=mydots$hurst,
jump.coeff=jump_Carma,
measure=eval(mydots$measure),
measure.type=mydots$measure.type,
jump.variable=mydots$jump.variable,
time.variable=mydots$time.variable,
state.variable=c(Carma.var,Y_coeff),
solve.variable=c(Carma.var,Y_coeff),
xinit=c(Vt,mydots$xinit))
# return(Model_Carma)
if(quadratic_variation==TRUE){
#
stop("Work in Progress: Implementation of CARMA model for CoGarch.
We need the increments of Quadratic Variation")
#
# diffusion_first_cond<-paste(coeff_alpha[fin_alp],V,sep="*")
# diffusion_Carma<-matrix(c(diffusion_first_cond,as.character(matrix(0,(q-1),1)),Vt),(q+1),1)
# # At the present time, Yuima does not support Multi - Jumps
# Model_Carma1<-setModel(drift=drift_Carma, diffusion=diffusion_Carma,
# hurst=hurst, state.variable=c(V,Y_coeff), solve.variable=c(V,Y_coeff))
# under_Lev1<-setModel(drift="0",diffusion="0",jump.coeff="1" ,
# measure=measure ,measure.type=measure.type ,
# jump.variable=jump.variable , time.variable=time.variable)
# if(length(Model_Carma1)==0){
# stop("Yuima model was not built")
# } else {
# Model_Carma<-Carma_Model()
# Model_Carma@model <- Model_Carma1
# Model_Carma@Cogarch_Model_Log <- Cogarch_Model
# Model_Carma@Under_Lev <-under_Lev1
# return(Model_Carma)
# }
#
}
}
}
}
# 25/11
if(is.null(loc.par)){loc.par<-character()}
if(is.null(scale.par)){scale.par<-character()}
if(is.null(lin.par)){lin.par<-character()}
carmainfo<-new("carma.info",
p=p,
q=q,
loc.par=loc.par,
scale.par=scale.par,
ar.par=ar.par,
ma.par=ma.par,
lin.par=lin.par,
Carma.var=Carma.var,
Latent.var=Latent.var,
XinExpr=XinExpr)
Model_Carma1<-new("yuima.carma",
info=carmainfo,
drift=Model_Carma@drift,
diffusion =Model_Carma@diffusion,
hurst=Model_Carma@hurst,
[email protected],
measure=Model_Carma@measure,
[email protected],
parameter=Model_Carma@parameter,
[email protected],
[email protected],
[email protected],
noise.number = [email protected],
equation.number = [email protected],
dimension = Model_Carma@dimension,
[email protected],
xinit=Model_Carma@xinit,
J.flag = [email protected]
)
return(Model_Carma1)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setCarma.R |
setMethod("initialize", "cogarch.info",
function(.Object,
p=numeric(),
q=numeric(),
ar.par=character(),
ma.par=character(),
loc.par=character(),
Cogarch.var=character(),
V.var=character(),
Latent.var=character(),
XinExpr=logical(),
measure=list(),
measure.type=character()){
.Object@p <- p
.Object@q <- q
[email protected] <- ar.par
[email protected] <- ma.par
[email protected] <- loc.par
[email protected] <- Cogarch.var
[email protected] <- V.var
[email protected] <- Latent.var
.Object@XinExpr <- XinExpr
.Object@measure<-measure
[email protected]<-measure.type
return(.Object)
})
setMethod("initialize", "yuima.cogarch",
function(.Object,
info = new("cogarch.info"),
drift = expression() ,
diffusion = list() ,
hurst = 0.5,
jump.coeff = list(),
measure=list(),
measure.type=character(),
parameter = new("model.parameter"),
state.variable = "x",
jump.variable = "z",
time.variable = "t",
noise.number = numeric(),
equation.number = numeric(),
dimension = numeric(),
solve.variable = character(),
xinit = expression(),
J.flag = logical()
# quadr.var=new("quadratic.variation")
){
.Object@info <- info
.Object@drift <- drift
.Object@diffusion <- diffusion
.Object@hurst <- hurst
[email protected] <- jump.coeff
.Object@measure <- measure
[email protected] <- measure.type
.Object@parameter <- parameter
[email protected] <- state.variable
[email protected] <- jump.variable
[email protected] <- time.variable
[email protected] <- noise.number
[email protected] <- equation.number
.Object@dimension <- dimension
[email protected] <- solve.variable
.Object@xinit <- xinit
[email protected] <- J.flag
#[email protected] <- quadr.var
return(.Object)
})
setCogarch<-function(p,
q,
ar.par="b",
ma.par="a",
loc.par="a0",
Cogarch.var="g",
V.var="v",
Latent.var="y",
jump.variable="z",
time.variable="t",
measure=NULL,
measure.type=NULL,
XinExpr=FALSE,
startCogarch=0,
work=FALSE,...){
# We use the same parametrization as in Brockwell (2000)
call <- match.call()
mydots <- as.list(call)[-(1:2)]
if (work==TRUE){
Model_Cogarch=NULL
return(Model_Cogarch)
}
if(is.null(measure)){
yuima.warn("The Levy measure must be specified. The Brownian
is not allowed as underlying process for Cogarch model.")
return(NULL)
}
if(is.null(measure.type)){
yuima.warn("The measure.type must be specified. See yuima documentation.")
return(NULL)
}
# We need to build the auxiliar carma model that is the variance process
# If we consider a Cogarch(p,q) the variance process is a Carma(q,p-1) model
if(is.null(mydots$xinit)){
aux.Carma<-setCarma(p=q,
q=p-1,
ar.par=ar.par,
ma.par=ma.par,
loc.par=loc.par,
lin.par=ma.par,
Carma.var=V.var,
Latent.var=Latent.var,
XinExpr=XinExpr,
Cogarch=TRUE)
# In order to have a representation of a Cogarch(p,q) model coherent with the
# Chaadra Brockwell and Davis we need to modify the slot xinit and drift[1]
}else{
if(!is.null(mydots$xinit)){
aux.Carma<-setCarma(p=q,
q=p-1,
loc.par=loc.par,
ar.par=ar.par,
ma.par=ma.par,
lin.par=ma.par,
Carma.var=V.var,
Latent.var=Latent.var,
xinit=mydots$xinit,
Cogarch=TRUE)
# In order to have a representation of a Cogarch(p,q) model coherent with the
# Chaadra Brockwell and Davis we need to modify the slot xinit and drift[1]
}
}
newdrift<-c(0,as.character(aux.Carma@drift))
newdiffusion<-c(0,as.character(eval(aux.Carma@diffusion)))
line1<-c(paste0("sqrt(",V.var,")"),as.character(matrix(0,nrow=(q+1),ncol=1)))
dumm<-as.character(eval(aux.Carma@diffusion))
len.dumm<-length(dumm)
line2<-character(length = (len.dumm+1))
line2[1]<-"0"
for(i in 1:length(dumm)){
dumm.tmp<-nchar(x=dumm[i])
line2[i+1]<-substring(dumm[i],13,dumm.tmp-2)
}
state<-c(Cogarch.var,[email protected])
# We need now to modify the setModel in order to introduce a new component that represents
# the discrete parts of the quadratic variation.
# A possible solution is to write a new class that extends the yuima.model class and gives the possibility
# to add the additional components.
# The way to introduce the differentiation of the discrete part of the quadratic variation have to follow the same
# structure used for the jump component.
Lev.coeff<-matrix(c(line1,line2),ncol=2)
resdummy1<-setModel(drift=newdrift,
jump.coeff=Lev.coeff,
#quadr.coeff=line2,
measure=measure,
measure.type=measure.type,
#quadr.measure=measure,
#quadr.measure.type=measure.type,
state.variable=state,
jump.variable=jump.variable,
#quadr.variable=quadr.variable,
time.variable=time.variable,
xinit=c(startCogarch,as.character(aux.Carma@xinit)))
resdummy2 <- new("cogarch.info",
p=p,
q=q,
ar.par=ar.par,
ma.par=ma.par,
loc.par=loc.par,
Cogarch.var=Cogarch.var,
V.var=V.var,
Latent.var=Latent.var,
XinExpr=XinExpr,
measure=measure,
measure.type=measure.type
)
# res.quadr.var<-new("quadratic.variation",
# [email protected]@quadr.coeff,
# [email protected]@measure,
# [email protected]@measure.type,
# [email protected]@parms.quadr,
# parms.quadr.meas = [email protected]@parms.quadr.meas,
# quadr.variable = [email protected]@quadr.variable,
# Q.flag = [email protected]@Q.flag)
res<-new("yuima.cogarch",
info=resdummy2,
drift= resdummy1@drift,
diffusion= resdummy1@diffusion,
hurst=resdummy1@hurst,
[email protected],
measure= resdummy1@measure,
measure.type= [email protected],
parameter= resdummy1@parameter,
state.variable= [email protected],
jump.variable= [email protected],
time.variable= [email protected],
noise.number= [email protected],
equation.number= [email protected],
dimension= resdummy1@dimension,
solve.variable= [email protected],
xinit= resdummy1@xinit,
J.flag = [email protected]
# quadr.var=res.quadr.var
)
return(res)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setCogarch.R |
setHawkes <- function(lower.var="0", upper.var = "t", var.dt = "s",
process = "N", dimension = 1, intensity = "lambda",
ExpKernParm1="c", ExpKernParm2 ="a",
const = "nu", measure = NULL, measure.type = NULL){
if(dimension==1){
if(is.null(measure)){
my.rPPR1 <- function(n){
res <- t(t(rep(1,n)))
return(res)
}
my.dPPR1 <-function(x){
res<-1
return(res)
}
# yuima.law for the underlying source of randomness
Law.PPR1 <- setLaw(rng = my.rPPR1, density = my.dPPR1)
measure <- list(df = Law.PPR1)
measure.type <- "code"
}
}else{
if(is.null(measure)){
yuima.stop("Missing argument measure: see setHawkes documentation")
}
}
PROCESS <- paste0(process,c(1:dimension))
leng <- length(PROCESS)
mod1 <- setModel(drift = rep("0",leng),
diffusion = matrix("0",leng,leng),
jump.coeff = diag("1",leng,leng),
measure = measure, measure.type = measure.type,
solve.variable = PROCESS)
INTENSITY <- as.list(paste0(intensity,c(1:dimension)))
gFun <- paste0(const,c(1:dimension))
Ccoeff<-as.character(MatrCoeff(ExpKernParm1, dimension))
Acoeff<-as.character(MatrCoeff(ExpKernParm2, dimension))
Kernelpar<-c(Acoeff,Ccoeff)
Kernel<- matrix(paste0(Ccoeff,"*exp(-",Acoeff,"*(","t-",var.dt,"))"),dimension, dimension)
res <- aux.setPPR(yuima = mod1, counting.var=PROCESS,
gFun, Kernel,
var.dx = PROCESS, var.dt = var.dt, lambda.var = INTENSITY,
lower.var=lower.var, upper.var = upper.var,
nrow =dimension ,ncol=dimension, general = FALSE)
return(res)
}
MatrCoeff<-function(lett, dimension){
c1<-paste0(lett,c(1:dimension))
cMatrix<-matrix(NA,dimension,dimension)
for(i in c(1:dimension)){
cMatrix[i,]<-paste0(c1[i],c(1:dimension))
}
return(cMatrix)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setHawkes.R |
setMethod("initialize", "yuima.multimodel",
function(.Object,
#model = new("yuima.model")
drift = expression() ,
diffusion = list() ,
hurst = 0.5,
jump.coeff = list(),
#jump.coeff = expression(),
measure=list(),
measure.type=character(),
parameter = new("model.parameter"),
state.variable = "x",
jump.variable = "z",
time.variable = "t",
noise.number = numeric(),
equation.number = numeric(),
dimension = numeric(),
solve.variable = character(),
xinit = expression(),
J.flag = logical()
){
.Object@drift <- drift
.Object@diffusion <- diffusion
.Object@hurst <- hurst
[email protected] <- jump.coeff
.Object@measure <- measure
[email protected] <- measure.type
.Object@parameter <- parameter
[email protected] <- state.variable
[email protected] <- jump.variable
[email protected] <- time.variable
[email protected] <- noise.number
[email protected] <- equation.number
.Object@dimension <- dimension
[email protected] <- solve.variable
.Object@xinit <- xinit
[email protected] <- J.flag
return(.Object)
})
# We need a function that construct a Multivariate Model
setMultiModel <- function(drift=NULL,
diffusion=NULL,
hurst=0.5,
jump.coeff=NULL,
intensity = NULL,
df = NULL,
# jump.dimens = NULL,
# measure=list(),
measure.type=character(),
state.variable="x",
jump.variable="z",
time.variable="t",
solve.variable,
xinit=NULL){
## we need a temp env for simplifications
yuimaENV <- new.env()
##::measure and jump term #####################################
##::initialize objects ########
MEASURE <- list()
##::end initialize objects ########
##::make type function list ####
CPlist <- c("dnorm", "dgamma", "dexp", "dconst")
codelist <- c("rIG", "rNIG", "rgamma", "rbgamma", "rvgamma", "rstable","rpts","rnts") ## added "rpts" and "rnts" by YU (2016/10/4)
##::end make type function list ####
jump.dimens <- dim(jump.coeff)[2]
numbMeasure <- length(measure.type)
if(numbMeasure>0){
if(numbMeasure!=1){
if(jump.dimens!=numbMeasure){
yuima.stop("dimension of jump is not coherent")
}
}
# if(!length(measure.type)){
# if( length(jump.coeff) || length(measure) ){
# yuima.warn("measure type does not match with jump term.")
# return(NULL)
# }
# jump.variable <- character()
measure.par <- character()
if(any(measure.type=="CP")){
tmp.measure <- list(df=list(func=vector(mode="list", length=1),
expr=as.expression(rep("0",1))),
intensity=as.expression(rep("0",length(intensity))))
}else{
tmp.measure <- list(df=list(func=vector(mode="list", length=length(measure.type)),
expr=as.expression(rep("0",1))))
}
# measure.par <- character()
# }else{
# tmp.measure <- list(df=list(func=vector(mode="list",
# length=length(measure.type)),
# expr=as.expression(rep("0",length(measure.type)))),
# intensity=as.expression(rep("0",sum(measure.type=="CP"))))
# if( !length(jump.coeff) || !length(measure) ){
# yuima.warn("measure type isn't matched with jump term.")
# return(NULL)
# # }else
# # if(length(jump.coeff)!=1){
# # yuima.warn("multi dimentional jump term is not supported yet.")
# #
# # return(NULL)
# # }
#
# }
if("CP" %in% measure.type){
condCP <- (measure.type%in%"CP")
numbCP<-sum(condCP)
h <- 0
for(i in c(1:length(measure.type))){
if(length(measure.type[condCP[i]])!=0){
if(!is.na(intensity[i-h])){
tmp.measure$intensity[(i-h)] <- parse(text = intensity[i-h])
}
measure.par <- c(measure.par,all.vars(tmp.measure$intensity[(i-h)]))
}else{
h<-h+1
}
}
}
tmp.measure$df$expr <- parse(text=df[[1]])
measure.par <- c(measure.par, all.vars(tmp.measure$df$expr))
tmp <- regexpr("\\(", df[[1]])[1]
measurefunc <- substring(df[[1]], 1, tmp-1)
if(existsFunction(measurefunc)){
tmp.measure$df$func[[1]] <- eval(parse(text=measurefunc))
}else{
if(measurefunc!="yuima.law")
yuima.stop("density function for jump must be specified")
}
# if("CP"%in%measure.type){
# condCP <- (measure.type%in%"CP")
# numbCP<-sum(condCP)
# h <- 0
# for(i in c(1:length(measure.type))){
# if(length(measure[condCP[i]])!=0){
# tmp.measure$df$expr[i] <- parse(text=measure$df[[i]])
# tmp.measure$intensity[(i-h)] <- parse(text = measure$intensity[i-h])
#
# tmp <- regexpr("\\(", measure$df[[i]])[1]
# measurefunc <- substring(measure$df[[i]], 1, tmp-1)
# if(!is.na(match(measurefunc, codelist))){
# yuima.warn(paste("distribution function", measurefunc, "should be defined as type code."))
# return(NULL)
# }
# tmp.measure$df$func[[i]] <- eval(parse(text=measurefunc))
# }
# h<-h+1
# }
#
#
MEASURE$df$func<-tmp.measure$df$func
MEASURE$df$expr<-tmp.measure$df$expr
if("CP" %in% measure.type){
MEASURE$intensity<-tmp.measure$intensity
}
measure.par<-unique(measure.par)
n.eqn3 <- dim(jump.coeff)[1]
n.jump <- dim(jump.coeff)[2]
}
#
#
# ##measure.par$intensity <- unique(all.vars(MEASURE$intensity))
# ##::end check df name ####################
# ##::end CP
#
# }
# if("code"%in%measure.type){ ##::code
# if(!is.list(measure)){
# measure <- list(df=measure)
# }
# # else{
# # if(length(measure[[1]])!=1){
# # yuima.warn("multi dimentional jump term is considered.")
# # }
# ##::naming measure list #############
# # if(is.null(names(measure)) || names(measure)=="df"){
# # names(measure) <- "df"
# # }else{
# # yuima.warn("name of measure is incorrect.")
# # return(NULL)
# # }
# ##::end naming measure list #############
# # }
#
# condcode <- (measure.type%in%"code")
# numbcode<-sum(condcode)
# h <- 0
# for(i in c(1:length(measure.type))){
# if(condcode[i]){
# tmp.measure$df$expr[i]<-parse(text=measure$df[[i]])
#
# tmp <- regexpr("\\(", measure$df[[i]])[1]
# measurefunc <- substring(measure$df[[i]], 1, tmp-1)
# if(!is.na(match(measurefunc, CPlist))){
# yuima.warn(paste("\ndistribution function", measurefunc, "should be defined as type CP."))
# return(NULL)
# }else if(is.na(match(measurefunc, codelist))){
# warning(paste("\ndistribution function", measurefunc, "is not officialy supported as type code.\n"))
# }
# MEASURE$df$func[[i]] <- eval(parse(text=measurefunc))
# MEASURE$df$expr[i] <- tmp.measure$df$expr[i]
#
#
# # measure.par <- unique(all.vars(MEASURE$df$expr))
# #
# # tmp.measure$func
# }
# }
#
# if( "CP" %in% measure.type){
# measure.par <- unique( c( all.vars(MEASURE$intensity), all.vars(MEASURE$df$expr) ) )
# }else if("code" %in% measure.type){
# measure.par <- unique( c( all.vars(MEASURE$intensity), all.vars(MEASURE$df$expr) ) )
# }
#
#
# # ##::check df name ####################
# # tmp <- regexpr("\\(", measure$df)[1]
# # measurefunc <- substring(measure$df, 1, tmp-1)
# # if(!is.na(match(measurefunc, CPlist))){
# # yuima.warn(paste("\ndistribution function", measurefunc, "should be defined as type CP."))
# # return(NULL)
# # }else if(is.na(match(measurefunc, codelist))){
# # warning(paste("\ndistribution function", measurefunc, "is not officialy supported as type code.\n"))
# # }
# # ##MEASURE$df$func <- eval(parse(text=measurefunc))
# # MEASURE$df$expr <- parse(text=measure$df)
# #
# # measure.par <- unique(all.vars(MEASURE$df$expr))
# # ##::end check df name ####################
# # ##::end code
# }else if(measure.type=="density"){ ##::density
# if(length(measure)!=1){
# yuima.warn(paste("length of measure must be one on type", measure.type, "."))
# return(NULL)
# }
#
# if(!is.list(measure)){
# measure <- list(df=measure)
# }else{
# if(length(measure[[1]])!=1){
# yuima.warn("multi dimentional jump term is not supported yet.")
# return(NULL)
# }
#
# ##::naming measure list #############
# if(is.null(names(measure))){
# names(measure) <- "df"
# }else if(names(measure)!="density" && names(measure)!="df"){
# yuima.warn("name of measure is incorrect.")
# return(NULL)
# }
# ##::end naming measure list #############
# }
#
# ##::check df name ####################
# tmp <- regexpr("\\(", measure[[names(measure)]])[1]
# measurefunc <- substring(measure[[names(measure)]], 1, tmp-1)
# if(!is.na(match(measurefunc, CPlist))){
# yuima.warn(paste("distribution function", measurefunc, "should be defined as type CP."))
# return(NULL)
# }else if(!is.na(match(measurefunc, codelist))){
# yuima.warn(paste("distribution function", measurefunc, "should be defined as type code."))
# return(NULL)
# }
# MEASURE[[names(measure)]]$func <- eval(parse(text=measurefunc))
# MEASURE[[names(measure)]]$expr <- parse(text=measure[[names(measure)]])
#
# measure.par <- unique(all.vars(MEASURE[[names(measure)]]$expr))
# ##::end check df name ####################
# ##::end density
# }else{ ##::else
# yuima.warn(paste("measure type", measure.type, "isn't supported."))
# return(NULL)
# }
# n.eqn3 <- dim(jump.coeff)[1]
# n.jump <- length(measure.type)
# }
##::end measure and jump term #####################################
##:: check for errors and reform values
if(any(time.variable %in% state.variable)){
yuima.warn("time and state(s) variable must be different.")
return(NULL)
}
if(is.null(dim(drift))){ # this is a vector
n.eqn1 <- length(drift)
n.drf <- 1
}else{ # it is a matrix
n.eqn1 <- dim(drift)[1]
n.drf <- dim(drift)[2]
}
if(is.null(dim(diffusion))){ # this is a vector
n.eqn2 <- length(diffusion)
n.noise <- 1
}else{ # it is a matrix
n.eqn2 <- dim(diffusion)[1]
n.noise <- dim(diffusion)[2]
}
if(is.null(diffusion)){
diffusion <- rep("0", n.eqn1)
n.eqn2 <- n.eqn1
n.noise <- 1
}
## TBC
n.eqn3 <- n.eqn1
# if(!length(measure)){
# n.eqn3 <- n.eqn1
# }
if(n.eqn1 != n.eqn2 || n.eqn1 != n.eqn3){
yuima.warn("Malformed model, number of equations in the drift and diffusion do not match.")
return(NULL)
}
n.eqn <- n.eqn1
if(is.null(xinit)){
# xinit <- numeric(n.eqn)
xinit <- character(n.eqn)
}else if(length(xinit) != n.eqn){
if(length(xinit)==1){
xinit <- rep(xinit, n.eqn)
}else{
yuima.warn("Dimension of xinit variables missmatch.")
return(NULL)
}
}
if(missing(solve.variable)){
yuima.warn("Solution variable (lhs) not specified. Trying to use state variables.")
solve.variable <- state.variable
}
if(n.eqn != length(solve.variable)){
yuima.warn("Malformed model, number of solution variables (lhs) do no match number of equations (rhs).")
return(NULL)
}
loc.drift <- matrix(drift, n.eqn, n.drf)
loc.diffusion <- matrix(diffusion, n.eqn, n.noise)
# Modification starting point 6/11
loc.xinit<-matrix(xinit,n.eqn,n.drf)
##:: allocate vectors
DRIFT <- vector(n.eqn, mode="expression")
DIFFUSION <- vector(n.eqn, mode="list")
# Modification starting point 6/11
XINIT<-vector(n.eqn, mode = "expression")
##:: function to make expression from drift characters
pre.proc <- function(x){
for(i in 1:length(x)){
if(length(parse(text=x[i]))==0){
x[i] <- "0"
}
}
parse(text=paste(sprintf("(%s)", x), collapse="+"))
}
##22/11:: function to simplify expression in drift, diffusion, jump and xinit characters
yuima.Simplifyobj<-function(x){
dummy<-yuima.Simplify(x, yuima.env=yuimaENV)
dummy1<-yuima.Simplify(dummy, yuima.env=yuimaENV)
dummy2<-as.character(dummy1)
res<-parse(text=paste0("(",dummy2,")",collapse=NULL))
return(res)
}
##:: make expressions of drifts and diffusions and jump
for(i in 1:n.eqn){
DRIFT[i] <- pre.proc(loc.drift[i,])
# 22/11 Simplify expressions
DRIFT[i] <- yuima.Simplifyobj(DRIFT[i])
# Modification starting point 6/11
XINIT[i]<-pre.proc(loc.xinit[i, ])
XINIT[i]<- yuima.Simplifyobj(XINIT[i])
for(j in 1:n.noise){
expr <- parse(text=loc.diffusion[i,j])
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
# DIFFUSION[[i]][j] <- expr
#22/11
DIFFUSION[[i]][j] <- yuima.Simplifyobj(expr)
}
#22/11
#if (length(JUMP)>0){
# JUMP[i] <- parse(text=jump.coeff[i])
# JUMP[i] <- yuima.Simplifyobj(JUMP[i])
#}
}
#print(length(jump.coeff))
#if (length(jump.coeff)==0){
# JUMP <- list(parse(text=jump.coeff))
#}else{
# # JUMP <- vector(n.eqn, mode="expression")
# JUMP <- vector(n.eqn, mode="list")
#}
if(length(jump.coeff)==0){
JUMP <- list()
} else {
if(length(jump.coeff)==1 & !is.matrix(jump.coeff)){ # is a scalar
expr <- parse(text=jump.coeff)
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
JUMP <- list(yuima.Simplifyobj(expr))
} else { # must be matrix, n.col = dimension of Levy noise
jump.coeff <- as.matrix(jump.coeff)
c.j <- ncol(jump.coeff)
r.j <- nrow(jump.coeff)
#print(c.j)
#print(r.j)
#print(jump.coeff)
JUMP <- vector(r.j, mode="list")
for(i in 1:r.j){
for(j in 1:c.j){
#cat(sprintf("\ni=%d,j=%d\n",i,j))
expr <- parse(text=jump.coeff[i,j])
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
JUMP[[i]][j] <- yuima.Simplifyobj(expr)
}
}
}
}
#print(str(JUMP))
#
##:: get parameters in drift expression
drift.par <- unique(all.vars(DRIFT))
# Modification starting point 6/11
xinit.par <- unique(all.vars(XINIT))
drift.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), drift.par)))
if(length(drift.idx)>0){
drift.par <- drift.par[-drift.idx]
}
##:: get parameters in diffusion expression
diff.par <- unique(unlist(lapply(DIFFUSION, all.vars)))
diff.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), diff.par)))
if(length(diff.idx)>0){
diff.par <- diff.par[-diff.idx]
}
##:: get parameters in jump expression
J.flag <- FALSE
# jump.par <- unique(all.vars(JUMP))
jump.par <- unlist(lapply(JUMP,all.vars))
if(is.null(jump.par))
jump.par <- character()
if(length(na.omit(match(jump.par, jump.variable)))){
J.flag <- TRUE
}
jump.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), jump.par)))
if(length(jump.idx)>0){
jump.par <- jump.par[-jump.idx]
}
##:: get parameters in measure expression
measure.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), measure.par)))
if(length(measure.idx)>0){
measure.par <- measure.par[-measure.idx]
}
##:: order parameters for 'yuima.pars'
##id1 <- which(diff.par %in% drift.par)
##id2 <- which(drift.par %in% diff.par)
##common <- unique(c(diff.par[id1], drift.par[id2]))
common <- c(drift.par, diff.par)
common <- common[duplicated(common)]
common1<-common
# modification 06/11 common1 contains only
# parameters that appear in both drift and diffusion terms.
# Modification 06/11 common contains only parameters that appear
# in drift, diff, Jump and xinit
if (length(xinit)) {
common <- c(common, xinit.par)
common <- common[duplicated(common)]
common <- c(common, xinit.par)
common <- common[duplicated(common)]
}
if(length(measure.type)){
common <- c(common, jump.par)
common <- common[duplicated(common)]
common <- c(common, measure.par)
common <- common[duplicated(common)]
}
# all.par <- unique(c(drift.par, diff.par, jump.par, measure.par))
all.par <- unique(c(drift.par, diff.par, jump.par, measure.par, xinit.par))
##:: instanciate class
tmppar <- new("model.parameter",
all= all.par,
# common= common,
common= common1,
diffusion= diff.par,
drift= drift.par,
jump= jump.par,
measure= measure.par,
xinit=xinit.par)
tmp <- new("yuima.multimodel",
drift= DRIFT,
diffusion= DIFFUSION,
hurst=as.numeric(hurst),
jump.coeff=JUMP,
measure= MEASURE,
measure.type= measure.type,
parameter= tmppar,
state.variable= state.variable,
jump.variable= jump.variable,
time.variable= time.variable,
noise.number= n.noise,
equation.number= n.eqn,
dimension= c(
length(tmppar@all),
length(tmppar@common),
length(tmppar@diffusion),
length(tmppar@drift),
length(tmppar@jump),
length(tmppar@measure)
),
solve.variable= solve.variable,
xinit= XINIT,
J.flag <- J.flag)
return(tmp)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setMultiModel.R |
setMethod("initialize", "yuima.poisson",
function(.Object,
drift = expression() ,
diffusion = list() ,
hurst = 0.5,
jump.coeff = expression(),
measure=list(),
measure.type=character(),
parameter = new("model.parameter"),
state.variable = "x",
jump.variable = "z",
time.variable = "t",
noise.number = numeric(),
equation.number = numeric(),
dimension = numeric(),
solve.variable = character(),
xinit = expression(),
J.flag = logical()){
.Object@drift <- drift
.Object@diffusion <- diffusion
.Object@hurst <- 0.5
[email protected] <- jump.coeff
.Object@measure <- measure
[email protected] <- measure.type
.Object@parameter <- parameter
[email protected] <- state.variable
[email protected] <- jump.variable
[email protected] <- time.variable
[email protected] <- noise.number
[email protected] <- equation.number
.Object@dimension <- dimension
[email protected] <- solve.variable
.Object@xinit <- xinit
[email protected] <- J.flag
return(.Object)
})
setPoisson <- function(intensity=1, df=NULL, scale=1, dimension=1, ...){
poisson.model <- setModel(drift="0", diffusion="0",
jump.coeff=as.expression(scale),
measure=list(intensity=intensity, df=df),
measure.type="CP",...)
CPoisson <- new("yuima.poisson",
drift=poisson.model@drift,
diffusion = poisson.model@diffusion,
hurst=poisson.model@hurst,
[email protected],
measure=poisson.model@measure,
[email protected],
parameter=poisson.model@parameter,
[email protected],
[email protected],
[email protected],
noise.number = [email protected],
equation.number = dimension,
dimension = dimension,
[email protected],
xinit=poisson.model@xinit,
J.flag = [email protected]
)
return(CPoisson)
}
dconst <- function(x,k=1) k*(x==k)
rconst <- function(n,k=1) rep(k,n)
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setPoisson.R |
setPPR <- function(yuima, counting.var="N", gFun, Kernel,
var.dx= "s", var.dt = "s", lambda.var = "lambda",
lower.var="0", upper.var = "t",
nrow =1 ,ncol=1){
general <- TRUE
ret <- aux.setPPR(yuima, counting.var, gFun,
Kernel, var.dx, var.dt, lambda.var,
lower.var="0", upper.var = "t",
nrow =1 ,ncol=1,general =general)
return(ret)
}
aux.setPPR <-function(yuima, counting.var="N", gFun, Kernel,
var.dx, var.dt = "s", lambda.var = "lambda",
lower.var="0", upper.var = "t",
nrow =1 ,ncol=1, general){
g <- setMap(func = gFun, yuima = yuima,
nrow = nrow, ncol = ncol)
yuimadum <- yuima
[email protected] <- var.dt
HawkesType <- FALSE
if(all(counting.var %in% var.dx)){
HawkesType <- TRUE
}
if(!HawkesType){
Integral <- setIntegral(yuima=yuimadum,
integrand = Kernel, var.dx = var.dx,
lower.var = lower.var, upper.var = upper.var,
out.var = "", nrow = nrow, ncol = ncol)
}else{
Integral <- setIntegral(yuima=yuimadum,
integrand = Kernel, var.dx = var.dx,
lower.var = lower.var, upper.var = upper.var,
out.var = "", nrow = nrow, ncol = ncol)
}
if(g@Output@dimension[1]!=Integral@Integral@Integrand@dimIntegrand[1]){
yuima.stop("dimension gFun and kernel mismatch")
}
allparam <- unique(c(yuima@parameter@all, g@Output@param@allparamMap,
Integral@[email protected]@Integrandparam))
common <- unique(c(g@Output@param@common,
Integral@[email protected]@common))
paramHawkes <- list(allparam = allparam, common = common,
gFun = g@Output@param@allparamMap,
Kern = Integral@[email protected]@Integrandparam)
# IntPPR<- yuima:::setIntegral(yuima=yuimadum,
# integrand y= Kernel, var.dx = "N",
# lower.var = lower.var, upper.var = upper.var,
# out.var = "", nrow = nrow, ncol = ncol)
# return(list(Count.Proc = counting.var,
# gFun = list(param=g@Output@param, output=g@Output),
# Kernel = Integral, paramHawkes = paramHawkes,
# model = yuima, SelfEx = HawkesType))
yuima1 <- setYuima(model =yuima)
type <- [email protected]
if(all(type == "code")){
if(!(is(yuima@measure$df,"yuima.law")))
measure <- list(df = as.character(yuima@measure$df$expr))
}else{
measure <- yuima@measure
}
if(all(type == "CP")){
if(!(is(yuima@measure$df,"yuima.law")))
measure <- list(intensity = as.character(yuima@measure$intensity),
df= as.character(yuima@measure$df$expr))
}else{
measure <- yuima@measure
}
IntensWithCount<-FALSE
if(!is.list(g@Output@formula)){
if(any(counting.var%in%all.vars(g@Output@formula)))
IntensWithCount<-TRUE
}else{
ddd<- length(g@Output@formula)
for(i in c(1:ddd)){
if(any(counting.var%in%all.vars(g@Output@formula[[i]])))
IntensWithCount<-TRUE
}
}
if(any(counting.var%in%Integral@[email protected]@var.dx))
IntensWithCount<-TRUE
if(!is.list(Integral@Integral@Integrand@IntegrandList)){
if(any(counting.var%in%all.vars(Integral@Integral@Integrand@IntegrandList)))
IntensWithCount<-TRUE
}else{
ddd<- length(Integral@Integral@Integrand@IntegrandList)
for(i in c(1:ddd)){
if(any(counting.var%in%all.vars(Integral@Integral@Integrand@IntegrandList[[i]])))
IntensWithCount<-TRUE
}
}
RegressWithCount <- FALSE
if(general){
covariates<-character()
if(sum(!([email protected]))!=0){
condCovariate<-!([email protected])
covariates<[email protected][condCovariate]
if(length(covariates)>0){
covariate.drift <- yuima@drift[condCovariate]
covariate.diff <- yuima@diffusion[condCovariate]
covariate.jump <- [email protected][condCovariate]
}
if(any(counting.var %in% all.vars(covariate.drift))){
RegressWithCount <-TRUE
}
ddd.dif <-length(covariate.diff)
if(length(covariate.diff)>0){
for(i in c(1:ddd.dif)){
if(any(counting.var %in% all.vars(covariate.diff[[i]]))){
RegressWithCount <-TRUE
}
}
}
ddd.jump <-length(covariate.jump)
if(length(covariate.jump)>0){
for(i in c(1:ddd.jump)){
if(any(counting.var %in% all.vars(covariate.jump[[i]]))){
RegressWithCount <-TRUE
}
}
}
}
PPR <- new("info.PPR",
allparam = paramHawkes$allparam,
allparamPPR = unique(c(paramHawkes$gFun,paramHawkes$Kern)),
common = paramHawkes$common,
counting.var = counting.var,
var.dx = var.dx,
upper.var = upper.var,
lower.var = lower.var,
covariates = covariates,
var.dt = var.dt,
additional.info = lambda.var,
Info.measure = list(type=type,measure=measure),
RegressWithCount=RegressWithCount,
IntensWithCount=IntensWithCount)
ret <- new("yuima.PPR", PPR = PPR,
gFun = g@Output,
Kernel = Integral@Integral,
yuima = yuima1)
}else{
PPR <- new("info.PPR",
allparam = paramHawkes$allparam,
allparamPPR = unique(c(paramHawkes$gFun,paramHawkes$Kern)),
common = paramHawkes$common,
counting.var = counting.var,
var.dx = var.dx,
upper.var = upper.var,
lower.var = lower.var,
covariates = character(),
var.dt = var.dt,
additional.info = "Exponential Hawkes",
Info.measure = list(type=type,measure=measure))
ret <- new("yuima.Hawkes", PPR = PPR,
gFun = g@Output,
Kernel = Integral@Integral,
yuima = yuima1)
}
return(ret)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/setPpr.R |
euler<-function(xinit,yuima,dW,env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
V0 <- sdeModel@drift
V <- sdeModel@diffusion
r.size <- [email protected]
d.size <- [email protected]
Terminal <- yuima@sampling@Terminal[1]
Initial <- yuima@sampling@Initial[1]
n <- yuima@sampling@n[1]
dL <- env$dL
# dX <- xinit
# 06/11 xinit is an expression: the structure is equal to that of V0
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, env)
if(length(dummy.val)==1){dummy.val<-rep(dummy.val,length(xinit))}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,env)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(xinit)){
dX[i] <- dummy.val[i]
}
}else{
dX_dummy <- xinit
if(length(modelstate)==length(dX_dummy)){
for(i in 1:length(modelstate)) {
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}else{
yuima.warn("the number of model states do not match the number of initial conditions")
return(NULL)
}
# 06/11 we need a initial variable for X_0
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(dX_dummy)){
dX[i] <- eval(dX_dummy[i], env)
}
}
##:: set time step
# delta <- Terminal/n
delta <- yuima@sampling@delta
## moving the following lines enclosed if(0) after Levy (YK, Apr 12, 2017)
if(0){
##:: check if DRIFT and/or DIFFUSION has values
has.drift <- sum(as.character(sdeModel@drift) != "(0)")
var.in.diff <- is.logical(any(match(unlist(lapply(sdeModel@diffusion, all.vars)), [email protected])))
#print(is.Poisson(sdeModel))
##:: function to calculate coefficients of dW(including drift term)
##:: common used in Wiener and CP
p.b <- function(t, X=numeric(d.size)){
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
##:: solve diffusion term
if(has.drift){
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i], env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j],env)
}
}
} else { ##:: no drift term (faster)
tmp <- matrix(0, d.size, r.size)
if(!is.Poisson(sdeModel)){ # we do not need to evaluate diffusion
for(i in 1:d.size){
for(j in 1:r.size){
tmp[i,j] <- eval(V[[i]][j],env)
} # for j
} # foh i
} # !is.Poisson
} # else
return(tmp)
}
X_mat <- matrix(0, d.size, n+1)
X_mat[,1] <- dX
if(has.drift){ ##:: consider drift term to be one of the diffusion term(dW=1)
dW <- rbind( rep(1, n)*delta , dW)
}
}## if(0) finish
if(!length([email protected])){ ##:: Wiener Proc
##:: using Euler-Maruyama method
if(0){ # old version (Jan 25, 2017)
if(var.in.diff & (!is.Poisson(sdeModel))){ ##:: diffusions have state variables and it is not Poisson
##:: calcurate difference eq.
for( i in 1:n){
# dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i]
#dX <- dX + p.b(t=yuima@sampling@Initial+i*delta, X=dX) %*% dW[, i] # LM
dX <- dX + p.b(t=yuima@sampling@Initial+(i-1)*delta, X=dX) %*% dW[, i] # YK
X_mat[,i+1] <- dX
}
}else{ ##:: diffusions have no state variables (not use p.b(). faster)
sde.tics <- seq(0, Terminal, length=(n+1))
sde.tics <- sde.tics[2:length(sde.tics)]
X_mat[, 1] <- dX
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], dX[i])
}
assign(modeltime, sde.tics)
t.size <- length(sde.tics)
##:: solve diffusion term
if(has.drift){
pbdata <- matrix(0, d.size*(r.size+1), t.size)
for(i in 1:d.size){
pbdata[(i-1)*(r.size+1)+1, ] <- eval(V0[i], env)
for(j in 1:r.size){
pbdata[(i-1)*(r.size+1)+j+1, ] <- eval(V[[i]][j], env)
}
}
dim(pbdata)<-(c(r.size+1, d.size*t.size))
}else{
pbdata <- matrix(0, d.size*r.size, t.size)
if(!is.Poisson(sdeModel)){
for(i in 1:d.size){
for(j in 1:r.size){
pbdata[(i-1)*r.size+j, ] <- eval(V[[i]][j], env)
} # for j
} # for i
} # !is.Poisson
dim(pbdata)<-(c(r.size, d.size*t.size))
} # else
pbdata <- t(pbdata)
##:: calcurate difference eq.
for( i in 1:n){
if(!is.Poisson(sdeModel))
dX <- dX + pbdata[((i-1)*d.size+1):(i*d.size), ] %*% dW[, i]
X_mat[, i+1] <- dX
}
}
}
#if(0){ # currently ignored due to a bug (YK, Feb 23, 2017)
# new version (Jan 25, 2017)
b <- parse(text=paste("c(",paste(as.character(V0),collapse=","),")"))
vecV <- parse(text=paste("c(",paste(as.character(unlist(V)),collapse=","),")"))
X_mat <- .Call("euler", dX, Initial, as.integer(r.size),
rep(1, n) * delta, dW, modeltime, modelstate, quote(eval(b, env)),
quote(eval(vecV, env)), env, new.env(),
PACKAGE = "yuima") # PACKAGE is added (YK, Dec 4, 2018)
#}
#tsX <- ts(data=t(X_mat), deltat=delta , start=0)
tsX <- ts(data=t(X_mat), deltat=delta , start = yuima@sampling@Initial) #LM
}else{ ##:: Levy
### add (YK, Apr 12, 2017)
##:: check if DRIFT and/or DIFFUSION has values
has.drift <- sum(as.character(sdeModel@drift) != "(0)")
var.in.diff <- is.logical(any(match(unlist(lapply(sdeModel@diffusion, all.vars)), [email protected])))
#print(is.Poisson(sdeModel))
##:: function to calculate coefficients of dW(including drift term)
##:: common used in Wiener and CP
p.b <- function(t, X=numeric(d.size)){
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
##:: solve diffusion term
if(has.drift){
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i], env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j],env)
}
}
} else { ##:: no drift term (faster)
tmp <- matrix(0, d.size, r.size)
if(!is.Poisson(sdeModel)){ # we do not need to evaluate diffusion
for(i in 1:d.size){
for(j in 1:r.size){
tmp[i,j] <- eval(V[[i]][j],env)
} # for j
} # foh i
} # !is.Poisson
} # else
return(tmp)
}
X_mat <- matrix(0, d.size, n+1)
X_mat[,1] <- dX
if(has.drift){ ##:: consider drift term to be one of the diffusion term(dW=1)
dW <- rbind( rep(1, n)*delta , dW)
}
JP <- [email protected]
mu.size <- length(JP)
# cat("\n Levy\n")
##:: function to solve c(x,z)
p.b.j <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
# tmp <- numeric(d.size)
j.size <- length(JP[[1]])
tmp <- matrix(0, mu.size, j.size)
# cat("\n inside\n")
#print(dim(tmp))
for(i in 1:mu.size){
for(j in 1:j.size){
tmp[i,j] <- eval(JP[[i]][j],env)
}
# tmp[i] <- eval(JP[i], env)
}
return(tmp)
}
# print(ls(env))
### WHY I AM DOING THIS?
# tmp <- matrix(0, d.size, r.size)
#
#for(i in 1:d.size){
# for(j in 1:r.size){
# cat("\n here\n")
# tmp[i,j] <- eval(V[[i]][j],env)
# } # for j
# }
###
if([email protected] == "CP" ){ ##:: Compound-Poisson type
##:: delete 2010/09/13 for simulate func bug fix by s.h
## eta0 <- eval(sdeModel@measure$intensity)
##:: add 2010/09/13 for simulate func bug fix by s.h
eta0 <- eval(sdeModel@measure$intensity, env) ## intensity param
##:: get lambda from nu()
tmp.expr <- function(my.x){
assign([email protected],my.x)
return(eval(sdeModel@measure$df$expr))
}
#lambda <- integrate(sdeModel@measure$df$func, 0, Inf)$value * eta0
#lambda <- integrate(tmp.expr, 0, Inf)$value * eta0 ##bug:2013/10/28
dummyList<-as.list(env)
# print(str(dummyList))
#print(str(idx.dummy))
lgth.meas<-length(yuima@model@parameter@measure)
#if(lgth.meas>1){
if(lgth.meas>0){ # YK Aug 11, 2021
#for(i in c(2:lgth.meas)){
for(i in c(1:lgth.meas)){ # YK Aug 11, 2021
idx.dummy<-yuima@model@parameter@measure[i]
#print(i)
#print(yuima@model@parameter@measure[i])
assign(idx.dummy,as.numeric(dummyList[idx.dummy]))
}
}
lambda <- integrate(tmp.expr, -Inf, Inf)$value * eta0
##:: lambda = nu() (p6)
# N_sharp <- rpois(1,Terminal*eta0) ##:: Po(Ne)
N_sharp <- rpois(1,(Terminal-Initial)*eta0) ##:: Po(Ne)
if(N_sharp == 0){
JAMP <- FALSE
}else{
JAMP <- TRUE
Uj <- sort( runif(N_sharp, Initial, Terminal) )
# Uj <- sort( runif(N_sharp, 0, Terminal) )
ij <- NULL
for(i in 1:length(Uj)){
Min <- min(which(Initial+ c(1:n)*delta > Uj[i]))
# Min <- min(which(c(1:n)*delta > Uj[i]))
ij <- c(ij, Min)
}
}
##:: make expression to create iid rand J
if(grep("^[dexp|dnorm|dgamma|dconst]", sdeModel@measure$df$expr)){
##:: e.g. dnorm(z,1,1) -> rnorm(mu.size*N_sharp,1,1)
F <- suppressWarnings(parse(text=gsub("^d(.+?)\\(.+?,", "r\\1(mu.size*N_sharp,", sdeModel@measure$df$expr, perl=TRUE)))
}else{
stop("Sorry. CP only supports dconst, dexp, dnorm and dgamma yet.")
}
##:: delete 2010/09/13 for simulate func bug fix by s.h
## randJ <- eval(F) ## this expression is evaluated locally not in the yuimaEnv
##:: add 2010/09/13 for simulate func bug fix by s.h
F.env <- new.env(parent=env)
assign("mu.size", mu.size, envir=F.env)
assign("N_sharp", N_sharp, envir=F.env)
randJ <- eval(F, F.env) ## this expression is evaluated in the F.env
j <- 1
for(i in 1:n){
if(JAMP==FALSE || sum(i==ij)==0){
Pi <- 0
}else{
if(is.null(dL)){
J <- eta0*randJ[j]/lambda
j <- j+1
##cat(paste(J,"\n"))
##Pi <- zeta(dX, J)
assign([email protected], J, env)
if([email protected]){
J <- 1
}
# Pi <- p.b.j(t=i*delta,X=dX) * J #LM
#Pi <- p.b.j(t=yuima@sampling@Initial+i*delta,X=dX) * J
Pi <- p.b.j(t=yuima@sampling@Initial+(i-1)*delta,X=dX) * J # YK
}else{# we add this part since we allow the user to specify the increment of CP LM 05/02/2015
# Pi <- p.b.j(t=i*delta,X=dX) %*% dL[, i] #LM
#Pi <- p.b.j(t=yuima@sampling@Initial+i*delta,X=dX) %*% dL[, i]
Pi <- p.b.j(t=yuima@sampling@Initial+(i - 1)*delta,X=dX) %*% dL[, i] # YK
}
##Pi <- p.b.j(t=i*delta, X=dX)
}
# dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i] + Pi # LM
#dX <- dX + p.b(t=yuima@sampling@Initial + i*delta, X=dX) %*% dW[, i] + Pi
dX <- dX + p.b(t=yuima@sampling@Initial + (i - 1)*delta, X=dX) %*% dW[, i] + Pi # YK
X_mat[, i+1] <- dX
}
# tsX <- ts(data=t(X_mat), deltat=delta, start=0) #LM
tsX <- ts(data=t(X_mat), deltat=delta, start=yuima@sampling@Initial)
##::end CP
}else if([email protected]=="code"){ ##:: code type
##:: Jump terms
code <- suppressWarnings(sub("^(.+?)\\(.+", "\\1", sdeModel@measure$df$expr, perl=TRUE))
args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", sdeModel@measure$df$expr, perl=TRUE)), ","))
#print(args)
dZ <- switch(code,
rNIG=paste("rNIG(n, ", args[2], ", ", args[3], ", ", args[4], "*delta, ", args[5], "*delta, ", args[6],")"),
rIG=paste("rIG(n,", args[2], "*delta, ", args[3], ")"),
rgamma=paste("rgamma(n, ", args[2], "*delta, ", args[3], ")"),
rbgamma=paste("rbgamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], "*delta, ", args[5], ")"),
## rngamma=paste("rngamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], ", ", args[5], "*delta, ", args[6], ")"),
rvgamma=paste("rvgamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], ", ", args[5], "*delta,", args[6],")"),
## rstable=paste("rstable(n, ", args[2], ", ", args[3], ", ", args[4], ", ", args[5], ", ", args[6], ")")
rstable=paste("rstable(n, ", args[2], ", ", args[3], ", ", args[4], "*delta^(1/",args[2],"), ", args[5], "*delta)"),
rpts=paste("rpts(n, ", args[2], ", ", args[3], "*delta,", args[4],")"),
rnts=paste("rnts(n, ", args[2], ", ", args[3], "*delta,", args[4], ", ", args[5], ", ", args[6],"*delta,", args[7], ")")
)
## added "rpts" and "rnts" by YU (2016/10/4)
dummyList<-as.list(env)
#print(str(dummyList))
lgth.meas<-length(yuima@model@parameter@measure)
#print(lgth.meas)
if(lgth.meas!=0){
for(i in c(1:lgth.meas)){
#print(i)
#print(yuima@model@parameter@measure[i])
idx.dummy<-yuima@model@parameter@measure[i]
#print(str(idx.dummy))
assign(idx.dummy,dummyList[[idx.dummy]])
#print(str(idx.dummy))
#print(str(dummyList[[idx.dummy]]))
#print(get(idx.dummy))
}
}
if(is.null(dZ)){ ##:: "otherwise"
cat(paste("Code \"", code, "\" not supported yet.\n", sep=""))
return(NULL)
}
if(!is.null(dL))
dZ <- dL
else
dZ <- eval(parse(text=dZ))
##:: calcurate difference eq.
#print(str(dZ))
if(is.null(dim(dZ)))
dZ <- matrix(dZ,nrow=1)
# print(dim(dZ))
# print(str([email protected]))
for(i in 1:n){
assign([email protected], dZ[,i], env)
if([email protected]){
dZ[,i] <- 1
}
# cat("\np.b.j call\n")
# tmp.j <- p.b.j(t=i*delta, X=dX) #LM
#tmp.j <- p.b.j(t=yuima@sampling@Initial+i*delta, X=dX)
tmp.j <- p.b.j(t=yuima@sampling@Initial+(i - 1)*delta, X=dX) # YK
#print(str(tmp.j))
#cat("\np.b.j cback and dZ\n")
# print(str(dZ[,i]))
# print(sum(dim(tmp.j)))
if(sum(dim(tmp.j))==2)
tmp.j <- as.numeric(tmp.j)
#print(str(tmp.j))
#print(str(p.b(t = i * delta, X = dX) %*% dW[, i]))
# dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i] #LM
#dX <- dX + p.b(t=yuima@sampling@Initial+i*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i]
dX <- dX + p.b(t=yuima@sampling@Initial+(i - 1)*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i] # YK
X_mat[, i+1] <- dX
}
# tsX <- ts(data=t(X_mat), deltat=delta, start=0) #LM
tsX <- ts(data=t(X_mat), deltat=delta, start=yuima@sampling@Initial)
##::end code
}else{
cat(paste("Type \"", [email protected], "\" not supported yet.\n", sep=""))
return(NULL)
}
}##::end levy
yuimaData <- setData(original.data=tsX)
return(yuimaData)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/sim.euler.R |
space.discretized<-function(xinit,yuima, env){
##:: initialize state variable
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
V0 <- sdeModel@drift
V <- sdeModel@diffusion
r.size <- [email protected]
d.size <- [email protected]
Terminal <- yuima@sampling@Terminal[1]
n <- yuima@sampling@n[1]
# dX <- xinit
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, env)
if(length(dummy.val)==1){dummy.val<-rep(dummy.val,length(xinit))}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,env)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(xinit)){
dX[i] <- dummy.val[i]
}
}else{
dX_dummy <- xinit
if(length(modelstate)==length(dX_dummy)){
for(i in 1:length(modelstate)) {
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}else{
yuima.warn("the number of model states do not match the number of initial conditions")
return(NULL)
}
# 20/11 we need a initial variable for X_0
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(dX_dummy)){
dX[i] <- eval(dX_dummy[i], env)
}
}
##:: set time step
delta <- Terminal/n
##:: using Space-discretized Euler-Maruyama method
##:: function for approximation of function G
gfunc <- function(x){
c0 <- 10
c1 <- 10
ret <- rep(0, length(x))
idx <- which(x < 1/c0)
ret[idx] <- 1
idx <- which(1/c0 <= x)
ret[idx] <- 1-pnorm(x[idx])
for(i in 1:length(idx)){
n <- 1:floor(c1/x[idx[i]])
ret[idx[i]] <- 4 * (ret[idx[i]] - sum( pnorm((4*n+1)*x[idx[i]]) - pnorm((4*n-1)*x[idx[i]]) ))
}
idx <- which(1 < ret)
ret[idx] <- 1
return(ret)
}
dxx <- 0.0001
xx <- seq(0, 1.7, dxx)
##:: approximate function G(gg)
gg <- gfunc(xx)
appfunc <- suppressWarnings(approxfun(gg, xx))
##:: calculate inverse of G
unif.a <- runif(n*2)
inv.a <- pmin(qnorm(1 - unif.a/4), appfunc(unif.a), na.rm=TRUE)
##:: make random time steps
ep <- sqrt(delta)
dTW <- (ep/inv.a)^2
time_idx <- cumsum(dTW) ##:: time index should be attached
div_sd <- min(which(time_idx > Terminal)) ##:: cut by time=1
time_idx <- time_idx[1:div_sd]
##:: add diffusion term
dTW <- rbind(dTW[1:div_sd],
t(matrix( (rbinom(div_sd*r.size, 1, 0.5)*2-1) * ep,
nrow=div_sd,
ncol=r.size)
)
)
X_mat <- matrix(0, d.size, div_sd+1)
X_mat[,1] <- dX
##:: function to calculate coefficients of dTW
p.b <- function(t, X=numeric(d.size)){
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i],env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j], env)
}
}
return(tmp)
}
##:: calcurate difference equation
for(i in 1:div_sd){
dX <- dX + p.b(t=time_idx[i], X=dX) %*% dTW[,i]
X_mat[,i+1] <- dX
}
##tsX <- ts(data=t(X_mat), deltat=delta , start=0)
##:: output zoo data
zooX <- zoo(x=t(X_mat), order.by=c(0, time_idx))
yuimaData <- setData(original.data=zooX)
return(yuimaData)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/sim.euler.space.discretized.R |
# sinc function
sinc <- function(x){
out <- rep(1, length(x))
idx <- abs(x) > 1e-7
out[idx] <- sin(x[idx])/x[idx]
return(out)
}
# Littlewood-Paley wavelet function
psi.lp <- function(x)
2 * sinc(2 * pi * x) - sinc(pi * x)
# coefficient matrices for circulant embedding
simBmllag.coef <- function(n, J, rho, theta, delta = 1/2^(J+1)){
if(length(rho) < J + 1)
rho <- append(rho, double(J + 1 - length(rho)))
if(length(theta) < J + 1)
theta <- append(theta, double(J + 1 - length(theta)))
m <- 3^ceiling(log(2 * n - 2, base = 3))
M <- as.integer((m - 1)/2)
tl <- ((-M):M) * delta
c12 <- double(m)
for(j in 1:(J + 1)){
c12 <- c12 + 2^(J - j + 1) * delta^2 * rho[j] * psi.lp(2^(J - j + 1) * (tl - theta[j]))
}
c12 <- c(c12[-(1:M)], c12[1:M])
A12 <- fft(c12)
s <- sqrt(delta^2 - Mod(A12)^2)
t <- sqrt(2 * (delta + s))
a <- array(0, dim = c(m, 2, 2))
a[ ,1,1] <- (delta + s)/t
a[ ,2,2] <- a[ ,1,1]
a[ ,1,2] <- A12/t
a[ ,2,1] <- Conj(a[ ,1,2])
return(a)
}
# main function
simBmllag <- function(n, J, rho, theta, delta = 1/2^(J+1),
imaginary = FALSE){
a <- simBmllag.coef(n, J, rho, theta, delta)
m <- dim(a)[1]
z1 <- rnorm(m) + 1i * rnorm(m)
z2 <- rnorm(m) + 1i * rnorm(m)
y1 <- a[ ,1,1] * z1 + a[ ,1,2] * z2
y2 <- a[ ,2,1] * z1 + a[ ,2,2] * z2
dW <- mvfft(cbind(y1, y2))[1:n, ]/sqrt(m)
if(imaginary == TRUE){
return(dW)
}else{
return(Re(dW))
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simBmllag.r |
## Simulate Cox-Ingersoll-Ross process with parameters alpha, beta and gamma at times specified via time.points
simCIR <- function (time.points, n, h, alpha, beta, gamma, equi.dist=FALSE ) {
# generate an equidistant time vector of length n+1 and distant h between observations
if (equi.dist==TRUE) {time.points <- 0:n*h }
# must start in t=0, otherwise t_vec is adjusted
if ( time.points[1] != 0 ) { time.points <- c(0, time.points) }
# define auxiliary variables, following notation of Malham and Wiese
nu <- 4 * alpha / gamma # degrees of freedom
eta_vec <- 4 * beta * exp(-beta * diff(time.points) ) / # auxiliary vector for the computation of the
(gamma * (1 - exp(-beta * diff(time.points) )) ) # non-centrality parameter in each step
# sample X_0 from stationary distribution
X <- rgamma(1, scale = gamma / (2 * beta), shape = 2 * alpha / gamma)
# compute X_t iteratively, using Prop. 1 from Malham and Wiese (2012)
for ( i in seq_along(eta_vec) ) {
lambda <- tail(X, 1) * eta_vec[i] # non-centrality parameter of the conditional distribution
X <- c(X, rchisq(1, df = nu, ncp = lambda) * exp(-beta * diff(time.points)[i]) / eta_vec[i]) # calculate
# next step of the CIR
}
# return data
return(rbind(t = time.points, X = X)) # first row: time points, second row: CIR at time point
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simCIR.R |
simCP<-function(xinit,yuima,env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
Terminal <- yuima@sampling@Terminal[1]
Initial <- yuima@sampling@Initial[1]
dimension <- yuima@model@dimension
dummy.val <- numeric(dimension)
if(length(xinit) != dimension)
xinit <- rep(xinit, dimension)[1:dimension]
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],envir=env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, envir=env)
if(length(dummy.val)==1){
dummy.val<-rep(dummy.val,dimension)
}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,envir=env)
}
} else {
for(i in 1:dimension){
dummy.val[i] <- eval(xinit[i], envir=env)
}
}
### Simulation of CP using Lewis' method
##:: Levy
JP <- eval([email protected][[1]], envir=env)
mu.size <- length(JP)
# print(str(JP))
#assign(sdeModel@measure$intensity, env) ## intensity param
.CPintensity <- function(.t) {
assign(modeltime, .t, envir=env)
eval(sdeModel@measure$intensity, envir=env)
}
dummyList<-as.list(env)
lgth.meas<-length(yuima@model@parameter@measure)
if(lgth.meas>1){
for(i in c(2:lgth.meas)){
idx.dummy<-yuima@model@parameter@measure[i]
assign(idx.dummy,as.numeric(dummyList[idx.dummy]))
}
}
# we use Lewis' acceptance/rejection method
#if(grep("^[dexp|dnorm|dgamma|dconst]", sdeModel@measure$df$expr)){
##:: e.g. dnorm(z,1,1) -> rnorm(mu.size*N_sharp,1,1)
F <- suppressWarnings(parse(text=gsub("^d(.+?)\\(.+?,", "r\\1(mu.size*N_sharp,", sdeModel@measure$df$expr, perl=TRUE)))
#} else{
#stop("Sorry. CP only supports dconst, dexp, dnorm and dgamma yet.")
#}
ell <- optimize(f=.CPintensity, interval=c(Initial, Terminal), maximum = TRUE)$objective
ellMax <- ell * 1.01
time <- Initial
E <- Initial
# heuristic code to avoid loops
nLAMBDA <- ceiling(ellMax*(Terminal-Initial)*1.2)
ru1 <- runif(nLAMBDA)
ru2 <- runif(nLAMBDA)*ellMax
tLAMBDA <- Initial+cumsum( -log(ru1)/ellMax )
idxLAMBDA <- which(tLAMBDA<=Terminal)
testLAMBDA <- ru2[idxLAMBDA]<.CPintensity(tLAMBDA[idxLAMBDA])
E <- c(Initial,tLAMBDA[testLAMBDA])
# while(time < Terminal) {
# ellMax <- ell(time)*1.01
# time <- time - 1/ellMax * log(runif(1))
# if(runif(1) < .CPintensity(time)/ellMax)
# E <- c(E, time)
#}
N_sharp <- length(E)-1
F.env <- new.env(parent=env)
assign("mu.size", mu.size, envir=F.env)
assign("N_sharp", N_sharp, envir=F.env)
randJ <- eval(F, envir=F.env) ## this expression is evaluated in the F.env
randJ <- JP[1]*randJ
randJ <- as.matrix(randJ, ncol=yuima@dimension)
randJ <- rbind(dummy.val, randJ)
CP <- apply(randJ,2,cumsum)
tsX <- zoo(x=CP, order.by=E)
yuimaData <- setYuima(data=setData(tsX))
yuimaData <- subsampling(yuimaData, sampling=yuima@sampling)
return(yuimaData@data)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simCP.R |
# funcF
# function to calculate F in (13.2)
funcF <- function(yuima,X,e,expand.var="e"){
##:: fix bug 07/23
assign(expand.var, e)
division <- yuima@sampling@n[1] #number of observed time
F <- getF(yuima@functional)
d.size <- yuima@[email protected]
k.size <- length(F)
modelstate <- yuima@[email protected]
XT <- X[division+1,] #X observed last. size:vector[d.size]
resF <- numeric(k.size) #values of F. size:vector[k.size]
for(d in 1:d.size){
assign(modelstate[d],XT[d]) #input XT in state("x1","x2") to use eval function to F
}
for(k in 1:k.size){
resF[k] <- eval(F[k]) #calculate F with XT
}
return(resF)
}
# funcf
# function to calculate fa in (13.2)
funcf <- function(yuima,X,e, expand.var){
##:: fix bug 07/23
assign(expand.var, e)
## division <- yuima@sampling@n #number of observed time
division <- yuima@sampling@n[1]
F <- getF(yuima@functional)
f <- getf(yuima@functional)
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
k.size <- length(F)
modelstate <- yuima@[email protected]
resf <- array(0,dim=c(k.size,division,(r.size+1))) #value of f0,f1,~,fr. size:array[k.size,division,r.size+1]
for(r in 1:(r.size+1)){
for(t in 1:division){
Xt <- X[t,] #Xt is data X observed on time t. size:vector[d.size]
for(d in 1:d.size){
assign(modelstate[d],Xt[d]) #input Xt in state to use eval function to f
}
for(k in 1:k.size){
resf[k,t,r] <- eval(f[[r]][k]) #calculate k th expression of fr.
}
}
}
return(resf)
}
# funcFe.
# function to calculate Fe in (13.2).
# core function of 'simFunctional'
funcFe. <- function(yuima,X,e,expand.var="e"){
##:: fix bug 07/23
assign(expand.var, e) ## expand.var
F <- getF(yuima@functional)
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
k.size <- length(F)
modelstate <- yuima@[email protected]
## division <- yuima@sampling@n
division <- yuima@sampling@n[1] ## 2010/11/13 modified. Currently, division must be the same for each path
Terminal <- yuima@sampling@Terminal
delta <- Terminal/division #length between observed times
dw <- matrix(0,division,r.size+1) #Wr(t) size:matrix[division,r.size+1]
dw[,1] <- rep(delta,length=division) #W0(t)=t
for(r in 2:(r.size+1)){
tmp <- rnorm(division,0,sqrt(delta)) #calculate Wr(t)
tmp <- c(0,tmp)
## dw[,r] <- tmp[-1]-tmp[-(division+1)] #calculate dWr(t)
dw[,r] <- diff(tmp) #calculate dWr(t)
}
resF <- funcF(yuima,X,e,expand.var=expand.var) #calculate F with X,e. size:vector[k.size]
resf <- funcf(yuima,X,e,expand.var=expand.var) #calculate f with X,e. size:array[k.size,division,r.size+1] ## 2010/11/13
Fe <- numeric(k.size)
for(k in 1:k.size){
Fe[k] <- sum(resf[k,1:division,]*dw)+resF[k] #calculate Fe using resF and resf as (13.2).
}
return(Fe)
}
# Get.X.t0
# function to calculate X(t=t0) using runge kutta method
Get.X.t0 <- function(yuima, expand.var="e"){
##:: fix bug 07/23
assign(expand.var,0) ## epsilon=0
r.size <- yuima@[email protected]
d.size <- yuima@[email protected]
k.size <- length(getF(yuima@functional))
modelstate <- yuima@[email protected]
V0 <- yuima@model@drift
V <- yuima@model@diffusion
Terminal <- yuima@sampling@Terminal[1]
## division <- yuima@sampling@n
division <- yuima@sampling@n[1] ## 2010/11/13
##:: fix bug 07/23
#pars <- #yuima@model@parameter@all[1] #epsilon
xinit <- getxinit(yuima@functional)
## init
dt <- Terminal/division
X <- xinit
Xt <- xinit
k <- numeric(d.size)
k1 <- numeric(d.size)
k2 <- numeric(d.size)
k3 <- numeric(d.size)
k4 <- numeric(d.size)
## runge kutta
for(t in 1:division){
## k1
for(i in 1:d.size){
assign(modelstate[i],Xt[i])
}
for(i in 1:d.size){
k1[i] <- dt*eval(V0[i])
}
## k2
for(i in 1:d.size){
assign(modelstate[i],Xt[i]+k1[i]/2)
}
for(i in 1:d.size){
k2[i] <- dt*eval(V0[i])
}
## k3
for(i in 1:d.size){
assign(modelstate[i],Xt[i]+k2[i]/2)
}
for(i in 1:d.size){
k3[i] <- dt*eval(V0[i])
}
## k4
for(i in 1:d.size){
assign(modelstate[i],Xt[i]+k3[i])
}
for(i in 1:d.size){
k4[i] <- dt*eval(V0[i])
}
## V0(X(t+dt))
k <- (k1+k2+k2+k3+k3+k4)/6
Xt <- Xt+k
X <- rbind(X,Xt)
}
## return matrix : (division+1)*d
rownames(X) <- NULL
colnames(X) <- modelstate
return(ts(X,deltat=dt[1],start=0))
}
# simFunctional
# public function to calculate Fe in (13.2).
setGeneric("simFunctional",
function(yuima, expand.var="e")
standardGeneric("simFunctional")
)
setMethod("simFunctional", signature(yuima="yuima"),
function(yuima, expand.var="e"){
Xlen <- length(yuima@data)
if(sum(Xlen != mean(Xlen)) != 0) {
yuima.warn("All length must be same yet.")
return(NULL)
}
if( (Xlen[1]-1) != yuima@sampling@n){
yuima.warn("Length of time series and division do not much.")
return(NULL)
}
e <- gete(yuima@functional)
##:: fix bug 07/23
Fe <- funcFe.(yuima,as.matrix(onezoo(yuima)),e,expand.var=expand.var)
return(Fe)
})
# F0
# public function to calculate Fe at e=0
setGeneric("F0",
function(yuima, expand.var="e")
standardGeneric("F0")
)
setMethod("F0", signature(yuima="yuima"),
function(yuima, expand.var="e"){
X.t0 <- Get.X.t0(yuima, expand.var=expand.var)
F0 <- funcFe.(yuima, X.t0, 0, expand.var=expand.var)
return(F0)
})
# Fnorm
# public function to calculate (Fe-F0)/e
setGeneric("Fnorm",
function(yuima, expand.var="e")
standardGeneric("Fnorm")
)
setMethod("Fnorm", signature(yuima="yuima"),
function(yuima, expand.var="e"){
e <- gete(yuima@functional)
Fe <- simFunctional(yuima, expand.var=expand.var)
F0 <- F0(yuima, expand.var=expand.var)
return((Fe-F0)/e)
})
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simFunctional.R |
## We have splitted the simulate function into blocks to allow for future
## methods to be added. S.M.I. & A.B.
## Interface to simulate() changed to match the S3 generic function in the
## package stats
## added an environment to let R find the proper values defined in the main
## body of the function, which in turn calls different simulation methods
## All new simulation methods should look into the yuimaEnv for local variables
## when they need to "eval" R expressions
##:: function simulate
##:: solves SDE and returns result
subsampling <- function(x,y) return(x)
setGeneric("simulate",
function(object, nsim=1, seed=NULL, xinit, true.parameter, space.discretized=FALSE,
increment.W=NULL, increment.L=NULL, method="euler", hurst, methodfGn="WoodChan",
sampling=sampling, subsampling=subsampling, ...
# Initial = 0, Terminal = 1, n = 100, delta,
# grid = as.numeric(NULL), random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
)
standardGeneric("simulate")
)
setMethod("simulate", "yuima.model",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
tmpsamp <- NULL
if(missing(sampling)){
tmpsamp <- setSampling(...)
# tmpsamp <- setSampling(Initial = Initial, Terminal = Terminal, n = n,
# delta = delta, grid = grid, random = random, sdelta=sdelta,
# sgrid=sgrid, interpolation=interpolation)
} else {
tmpsamp <- sampling
}
tmpyuima <- setYuima(model=object, sampling=tmpsamp)
out <- simulate(tmpyuima, nsim=nsim, seed=seed, xinit=xinit,
true.parameter=true.parameter,
space.discretized=space.discretized,
increment.W=increment.W, increment.L=increment.L,
method=method,
hurst=hurst,methodfGn=methodfGn, subsampling=subsampling)
return(out)
})
# We rewrite the code of simulate method. We build a new internal function aux.simulate containing
# the old code. This function starts directly if the model is an object of yuima.model-class
# or yuima.carma-class. If the model is an object of class yuima.cogarch, simulate method runs
# the internal function aux.simulateCogarch that generates first a path of the underlying levy and then
# uses this path to construct the trajectories of the Cogarch model by calling the aux.simulate function.
setMethod("simulate", "yuima",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL,
method="euler",
hurst,methodfGn="WoodChan",
sampling, subsampling,
Initial = 0, Terminal = 1, n = 100, delta,
grid = as.numeric(NULL), random = FALSE, sdelta=as.numeric(NULL),
sgrid=as.numeric(NULL), interpolation="none"){
if(is(object,"yuima.LevyRM")){
res <- aux.simulateLevyRM(object = object,
nsim = nsim, seed = seed, xinit = xinit, true.parameter = true.parameter, space.discretized = space.discretized,
increment.W = increment.W, increment.L = increment.L, method = method, hurst = hurst, methodfGn = methodfGn,
sampling = sampling, subsampling = subsampling)
return(res)
}
if(is(object@model,"yuima.carmaHawkes")){
if(method == "Thinning"){
res <- aux.simulateCarmaHawkes_thin(object, true.parameter)
return(res)
}
res <- aux.simulateCarmaHawkes(object, true.parameter)
return(res)
}
if(is(object@model,"yuima.cogarch")){
res<-aux.simulateCogarch(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L, method,
hurst,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation)
}else{
if(is(object@model,"yuima.multimodel")||
is(object@model@measure$df,"yuima.law")
){
res <- aux.simulate.multimodel(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L,method,
hurst,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation)
}else{
res<-aux.simulate(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L,method,
hurst,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation)
}
}
return(res)
# ##:: errors checks
#
# ##:1: error on yuima model
# yuima <- object
#
# if(missing(yuima)){
# yuima.warn("yuima object is missing.")
# return(NULL)
# }
#
# tmphurst<-yuima@model@hurst
#
# if(!missing(hurst)){
# yuima@model@hurst=hurst
# }
#
# if (is.na(yuima@model@hurst)){
# yuima.warn("Specify the hurst parameter.")
# return(NULL)
# }
#
# tmpsamp <- NULL
# if(is.null(yuima@sampling)){
# if(missing(sampling)){
# tmpsamp <- setSampling(Initial = Initial,
# Terminal = Terminal, n = n, delta = delta,
# grid = grid, random = random, sdelta=sdelta,
# sgrid=sgrid, interpolation=interpolation)
# } else {
# tmpsamp <- sampling
# }
# } else {
# tmpsamp <- yuima@sampling
# }
#
# yuima@sampling <- tmpsamp
#
# sdeModel <- yuima@model
# Terminal <- yuima@sampling@Terminal[1]
# n <- yuima@sampling@n[1]
# r.size <- [email protected]
# d.size <- [email protected]
#
# ##:2: error on xinit
# if(missing(xinit)){
# xinit <- sdeModel@xinit
# } else {
# if(length(xinit) != d.size){
# if(length(xinit)==1){
# xinit <- rep(xinit, d.size)
# } else {
# yuima.warn("Dimension of xinit variables missmatch.")
# return(NULL)
# }
# }
# }
#
# xinit <- as.expression(xinit) # force xinit to be an expression
#
#
# par.len <- length(sdeModel@parameter@all)
#
# if(missing(true.parameter) & par.len>0){
# true.parameter <- vector(par.len, mode="list")
# for(i in 1:par.len)
# true.parameter[[i]] <- 0
# names(true.parameter) <- sdeModel@parameter@all
# }
#
# yuimaEnv <- new.env()
#
# if(par.len>0){
# for(i in 1:par.len){
# pars <- sdeModel@parameter@all[i]
#
# for(j in 1:length(true.parameter)){
# if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
# assign(sdeModel@parameter@all[i], true.parameter[[j]],yuimaEnv)
# }
# }
# #assign(sdeModel@parameter@all[i], true.parameter[[i]], yuimaEnv)
# }
# }
#
#
# if(space.discretized){
# if(r.size>1){
# warning("Space-discretized EM cannot be used for multi-dimentional models. Use standard method.")
# space.discretized <- FALSE
# }
# if(length([email protected])){
# warning("Space-discretized EM is for only Wiener Proc. Use standard method.")
# space.discretized <- FALSE
# }
# }
#
# ##:: Error check for increment specified version.
# if(!missing(increment.W) & !is.null(increment.W)){
# if(space.discretized == TRUE){
# yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
# return(NULL)
# }else if(dim(increment.W)[1] != r.size){
# yuima.warn("Length of increment's row must be same as yuima@[email protected].")
# return(NULL)
# }else if(dim(increment.W)[2] != n){
# yuima.warn("Length of increment's column must be same as sampling@n[1].")
# return(NULL)
# }
# }
#
# ##:: Error check for increment specified version.
# if(!missing(increment.L) & !is.null(increment.L)){
# if(space.discretized == TRUE){
# yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
# return(NULL)
# }else if(dim(increment.L)[1] != length(yuima@[email protected][[1]]) ){ #r.size){
# yuima.warn("Length of increment's row must be same as yuima@[email protected].")
# return(NULL)
# }else if(dim(increment.L)[2] != n){
# yuima.warn("Length of increment's column must be same as sampling@n[1].")
# return(NULL)
# }
# }
#
#
# yuimaEnv$dL <- increment.L
#
#
# if(space.discretized){
# ##:: using Space-discretized Euler-Maruyama method
# yuima@data <- space.discretized(xinit, yuima, yuimaEnv)
#
# yuima@model@hurst<-tmphurst
# return(yuima)
# }
#
#
# ##:: using Euler-Maruyama method
# delta <- Terminal/n
#
# if(missing(increment.W) | is.null(increment.W)){
#
# if( sdeModel@hurst!=0.5 ){
#
# grid<-sampling2grid(yuima@sampling)
# isregular<-yuima@sampling@regular
#
# if((!isregular) || (methodfGn=="Cholesky")){
# dW<-CholeskyfGn(grid, sdeModel@hurst,r.size)
# yuima.warn("Cholesky method for simulating fGn has been used.")
# } else {
# dW<-WoodChanfGn(grid, sdeModel@hurst,r.size)
# }
#
# } else {
#
# delta<-Terminal/n
# if(!is.Poisson(sdeModel)){ # if pure CP no need to setup dW
# dW <- rnorm(n * r.size, 0, sqrt(delta))
# dW <- matrix(dW, ncol=n, nrow=r.size,byrow=TRUE)
# } else {
# dW <- matrix(0,ncol=n,nrow=1) # maybe to be fixed
# }
# }
#
# } else {
# dW <- increment.W
# }
#
# if(is.Poisson(sdeModel)){
# yuima@data <- simCP(xinit, yuima, yuimaEnv)
# } else {
# yuima@data <- euler(xinit, yuima, dW, yuimaEnv)
# }
#
# for(i in 1:length(yuima@[email protected]))
# index(yuima@[email protected][[i]]) <- yuima@sampling@grid[[1]] ## to be fixed
# yuima@model@xinit <- xinit
# yuima@model@hurst <-tmphurst
#
# if(missing(subsampling))
# return(yuima)
# subsampling(yuima, subsampling)
#
}
)
aux.simulate<-function(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L,method,
hurst,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation){
##:: errors checks
##:1: error on yuima model
yuima <- object
if(missing(yuima)){
yuima.warn("yuima object is missing.")
return(NULL)
}
tmphurst<-yuima@model@hurst
if(!missing(hurst)){
yuima@model@hurst=hurst
}
if (is.na(yuima@model@hurst)){
yuima.warn("Specify the hurst parameter.")
return(NULL)
}
tmpsamp <- NULL
if(is.null(yuima@sampling)){
if(missing(sampling)){
tmpsamp <- setSampling(Initial = Initial,
Terminal = Terminal, n = n, delta = delta,
grid = grid, random = random, sdelta=sdelta,
sgrid=sgrid, interpolation=interpolation)
} else {
tmpsamp <- sampling
}
} else {
tmpsamp <- yuima@sampling
}
yuima@sampling <- tmpsamp
sdeModel <- yuima@model
Terminal <- yuima@sampling@Terminal[1]
Initial <- yuima@sampling@Initial[1]
n <- yuima@sampling@n[1]
r.size <- [email protected]
d.size <- [email protected]
##:2: error on xinit
if(missing(xinit)){
xinit <- sdeModel@xinit
} else {
if(length(xinit) != d.size){
if(length(xinit)==1){
xinit <- rep(xinit, d.size)
} else {
yuima.warn("Dimension of xinit variables missmatch.")
return(NULL)
}
}
}
xinit <- as.expression(xinit) # force xinit to be an expression
par.len <- length(sdeModel@parameter@all)
if(missing(true.parameter) & par.len>0){
true.parameter <- vector(par.len, mode="list")
for(i in 1:par.len)
true.parameter[[i]] <- 0
names(true.parameter) <- sdeModel@parameter@all
}
yuimaEnv <- new.env()
if(par.len>0){
for(i in 1:par.len){
pars <- sdeModel@parameter@all[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(sdeModel@parameter@all[i], true.parameter[[j]],yuimaEnv)
}
}
#assign(sdeModel@parameter@all[i], true.parameter[[i]], yuimaEnv)
}
}
if(space.discretized){
if(r.size>1){
warning("Space-discretized EM cannot be used for multi-dimentional models. Use standard method.")
space.discretized <- FALSE
}
if(length([email protected])){
warning("Space-discretized EM is for only Wiener Proc. Use standard method.")
space.discretized <- FALSE
}
}
##:: Error check for increment specified version.
if(!missing(increment.W) & !is.null(increment.W)){
if(space.discretized == TRUE){
yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
return(NULL)
}else if(dim(increment.W)[1] != r.size){
yuima.warn("Length of increment's row must be same as yuima@[email protected].")
return(NULL)
}else if(dim(increment.W)[2] != n){
yuima.warn("Length of increment's column must be same as sampling@n[1].")
return(NULL)
}
}
##:: Error check for increment specified version.
if(!missing(increment.L) & !is.null(increment.L)){
if(space.discretized == TRUE){
yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
return(NULL)
}else if(dim(increment.L)[1] != length(yuima@[email protected][[1]]) ){ #r.size){
yuima.warn("Length of increment's row must be same as yuima@[email protected].")
return(NULL)
}else if(dim(increment.L)[2] != n){
yuima.warn("Length of increment's column must be same as sampling@n[1].")
return(NULL)
}
}
yuimaEnv$dL <- increment.L
if(space.discretized){
##:: using Space-discretized Euler-Maruyama method
yuima@data <- space.discretized(xinit, yuima, yuimaEnv)
yuima@model@hurst<-tmphurst
return(yuima)
}
##:: using Euler-Maruyama method
delta <- (Terminal-Initial)/n
if(missing(increment.W) | is.null(increment.W)){
if( sdeModel@hurst!=0.5 ){
grid<-sampling2grid(yuima@sampling)
isregular<-yuima@sampling@regular
if((!isregular) || (methodfGn=="Cholesky")){
dW<-CholeskyfGn(grid, sdeModel@hurst,r.size)
yuima.warn("Cholesky method for simulating fGn has been used.")
} else {
dW<-WoodChanfGn(grid, sdeModel@hurst,r.size)
}
} else {
delta<-(Terminal-Initial)/n
if(!is.Poisson(sdeModel)){ # if pure CP no need to setup dW
dW <- rnorm(n * r.size, 0, sqrt(delta))
dW <- matrix(dW, ncol=n, nrow=r.size,byrow=TRUE)
} else {
dW <- matrix(0,ncol=n,nrow=1) # maybe to be fixed
}
}
} else {
dW <- increment.W
}
if(is.Poisson(sdeModel)){
yuima@data <- simCP(xinit, yuima, yuimaEnv)
} else {
yuima@data <- euler(xinit, yuima, dW, yuimaEnv)
}
for(i in 1:length(yuima@[email protected]))
index(yuima@[email protected][[i]]) <- yuima@sampling@grid[[1]] ## to be fixed
yuima@model@xinit <- xinit
yuima@model@hurst <-tmphurst
if(missing(subsampling))
return(yuima)
subsampling(yuima, subsampling)
}
aux.simulateCogarch<-function(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L, method,
hurst,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation){
yuimaCogarch<-object
model<-yuimaCogarch@model
info<-model@info
samp <- yuimaCogarch@sampling
if([email protected]=="CP" && !is.null(increment.L)){
method="euler"
}
if(method=="euler"||(method=="mixed" && [email protected]=="code")){
if(length(increment.L)==0){
aux.Noise<-setModel(drift="0",
diffusion="0",
jump.coeff="1",
measure=info@measure,
[email protected])
# aux.samp<-setSampling(Initial = samp@Initial, Terminal = samp@Terminal[1], n = samp@n[1], delta = samp@delta,
# grid=samp@grid, random = samp@random, sdelta=samp@sdelta,
# sgrid=samp@sgrid, interpolation=samp@interpolation )
aux.samp<-setSampling(Initial = samp@Initial,
Terminal = samp@Terminal[1],
n = samp@n[1])
auxModel<-setYuima(model=aux.Noise, sampling= aux.samp)
if(length(model@parameter@measure)==0){
aux.incr2<-aux.simulate(object=auxModel, nsim=nsim, seed=seed,
space.discretized=space.discretized, increment.W=increment.W,
increment.L=increment.L,
hurst=0.5,methodfGn=methodfGn)
}else{
aux.incr2<-aux.simulate(object=auxModel, nsim=nsim, seed=seed,
true.parameter = true.parameter[model@parameter@measure],
space.discretized=space.discretized, increment.W=increment.W,
increment.L=increment.L,
hurst=0.5,methodfGn=methodfGn)
}
increment<-diff(as.numeric(get.zoo.data(aux.incr2)[[1]]))
} else{increment<-increment.L}
# Using the simulated increment for generating the quadratic variation
# As first step we compute it in a crude way. A more fine approach is based on
# the mpv function.
quadratVariation <- increment^2
incr.L <- t(matrix(c(increment,quadratVariation),ncol=2))
incr.W <- matrix(0, nrow=1,ncol=length(increment))
# Simulate trajectories Cogarch
}
d.size <- [email protected]
if(missing(xinit)){
xinit <- yuimaCogarch@model@xinit
} else {
if(length(xinit) != d.size){
if(length(xinit)==1){
xinit <- rep(xinit, d.size)
} else {
yuima.warn("Dimension of xinit variables missmatch.")
return(NULL)
}
}
}
xinit <- as.expression(xinit) # force xinit to be an expression
if(method=="euler"){
# result <- aux.simulate(object=yuimaCogarch, nsim=nsim, seed=seed, xinit=xinit,
# true.parameter = true.parameter,
# space.discretized = space.discretized,increment.W =incr.W,
# increment.L=incr.L, method=method,
# hurst=hurst,methodfGn=methodfGn,
# sampling=sampling, subsampling=subsampling,
# Initial=Initial, Terminal=Terminal, n=n, delta=delta,
# grid=grid, random=random, sdelta=sdelta,
# sgrid=sgrid, interpolation=interpolation)
Terminal <- samp@Terminal[1]
Initial <- samp@Initial[1]
n <- samp@n[1]
Delta <- (Terminal-Initial)/n
name.ar <- paste0([email protected],c(1:info@q))
name.ma <- paste0([email protected],c(1:info@p))
name.loc <- [email protected]
name.param <- names(true.parameter)
parms <- as.numeric(true.parameter)
names(parms)<-name.param
value.ar <- parms[name.ar]
value.ma <- parms[name.ma]
value.a0 <- parms[name.loc]
AMatrix <- MatrixA(value.ar)
avect<-evect<-matrix(0,info@q,1)
evect[info@q,] <- 1
avect[c(1,info@p),1] <- value.ma
Indent<-diag(info@q)
# Inputs: incr.L
tavect<-t(avect)
ncolsim <- (info@q+2)
sim <- matrix(0,n+1,ncolsim)
par.len <- length(model@parameter@all)
if(missing(true.parameter) & par.len>0){
true.parameter <- vector(par.len, mode="list")
for(i in 1:par.len)
true.parameter[[i]] <- 0
names(true.parameter) <- model@parameter@all
}
yuimaEnv <- new.env()
if(par.len>0){
for(i in 1:par.len){
pars <- model@parameter@all[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(model@parameter@all[i], true.parameter[[j]], yuimaEnv)
}
}
#assign(sdeModel@parameter@all[i], true.parameter[[i]], yuimaEnv)
}
}
for(i in c(1:ncolsim)){
sim[1,i] <- eval(xinit[i], yuimaEnv)
}
for(t in c(2:n)){
#sim[t,3:ncolsim] <- value.a0*expm(AMatrix*Delta)%*%evect*incr.L[2,t-1]+
# expm(AMatrix*Delta)%*%(Indent+evect%*%tavect*incr.L[2,t-1])%*%sim[t-1,3:ncolsim]
# sim[t,2]<-value.a0+tavect%*%sim[t,3:ncolsim]
# sim[t,1]<-sim[t-1,1]+sqrt(sim[t,2])*incr.L[1,t]
# sim[t,3:ncolsim]<-expm(AMatrix*Delta)%*%sim[t-1,3:ncolsim]+expm(AMatrix)%*%evect*sim[t-1,2]*incr.L[2,t]
sim[t,2]<-value.a0+tavect%*%sim[t-1,3:ncolsim]
sim[t,3:ncolsim]<-sim[t-1,3:ncolsim]+(AMatrix*Delta)%*%sim[t-1,3:ncolsim]+evect*sim[t-1,2]*incr.L[2,t]
sim[t,1]<-sim[t-1,1]+sqrt(sim[t,2])*incr.L[1,t]
}
X <- ts(sim[-(samp@n[1]+1),])
Data <- setData(X,delta = Delta,t0=Initial)
result <- setYuima(data=Data,model=yuimaCogarch@model, sampling=yuimaCogarch@sampling)
}else{
Terminal <- samp@Terminal[1]
Initial <- samp@Initial[1]
n <- samp@n[1]
Delta <- (Terminal-Initial)/n
name.ar <- paste0([email protected],c(1:info@q))
name.ma <- paste0([email protected],c(1:info@p))
name.loc <- [email protected]
name.param <- names(true.parameter)
parms <- as.numeric(true.parameter)
names(parms)<-name.param
value.ar <- parms[name.ar]
value.ma <- parms[name.ma]
value.a0 <- parms[name.loc]
AMatrix <- MatrixA(value.ar)
avect<-evect<-matrix(0,info@q,1)
evect[info@q,] <- 1
avect[c(1,info@p),1] <- value.ma
Indent<-diag(info@q)
# Inputs: incr.L
tavect<-t(avect)
ncolsim <- (info@q+2)
sim <- matrix(0,n+1,ncolsim)
par.len <- length(model@parameter@all)
if(missing(true.parameter) & par.len>0){
true.parameter <- vector(par.len, mode="list")
for(i in 1:par.len)
true.parameter[[i]] <- 0
names(true.parameter) <- model@parameter@all
}
yuimaEnv <- new.env()
if(par.len>0){
for(i in 1:par.len){
pars <- model@parameter@all[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(model@parameter@all[i], true.parameter[[j]], yuimaEnv)
}
}
#assign(sdeModel@parameter@all[i], true.parameter[[i]], yuimaEnv)
}
}
for(i in c(1:ncolsim)){
sim[1,i] <- eval(xinit[i], yuimaEnv)
}
if(yuimaCogarch@[email protected]=="code"){
for(t in c(2:n)){
# sim[t,2]<-value.a0+tavect%*%sim[t,3:ncolsim]
# sim[t,1]<-sim[t-1,1]+sqrt(sim[t,2])*incr.L[1,t]
# sim[t,3:ncolsim]<-expm(AMatrix*Delta)%*%sim[t-1,3:ncolsim]+expm(AMatrix)%*%evect*sim[t-1,2]*incr.L[2,t]
# sim[t,3:ncolsim]<-sim[t-1,3:ncolsim]+AMatrix*Delta%*%sim[t-1,3:ncolsim]+evect*sim[t-1,2]*incr.L[2,t-1]
sim[t,2]<-value.a0+tavect%*%sim[t-1,3:ncolsim]
sim[t,3:ncolsim] <- value.a0*expm(AMatrix*Delta)%*%evect*incr.L[2,t]+
expm(AMatrix*Delta)%*%(Indent+evect%*%tavect*incr.L[2,t])%*%sim[t-1,3:ncolsim]
sim[t,1]<-sim[t-1,1]+sqrt(sim[t,2])*incr.L[1,t]
}
X <- ts(sim[-(samp@n[1]+1),])
Data <- setData(X,delta = Delta, t0=Initial)
result <- setYuima(data=Data,model=yuimaCogarch@model, sampling=yuimaCogarch@sampling)
return(result)
}else{
lambda <- eval(model@measure$intensity, yuimaEnv)
#Simulating jump times
#intensity <- lambda*Delta
intensity<-lambda
jump_time<-numeric()
jump_time[1] <- rexp(1, rate = intensity)
# In yuima this part is evaluated using function eval
Time <-numeric()
Time[1] <- jump_time[1]
j <- 1
numb_jum<-numeric()
# for (i in c(1:n) ){
# numb_jum[i]<-0
# while(Time[j]<i){
# numb_jum[i]<-numb_jum[i]+1
# jump_time[j+1]<-rexp(1,rate=intensity)
# Time[j+1]<-Time[j]+jump_time[j+1]
# j<-j+1
# }
# }
while(Time[j] < (Terminal-Initial)){
jump_time[j+1]<-rexp(1,rate=intensity)
Time[j+1]<-Time[j]+jump_time[j+1]
j<-j+1
}
total_NumbJ <- j
# Counting the number of jumps
# N<-matrix(1,n,1)
# N[1,1]<-numb_jum[1]
# for(i in c(2:n)){
# N[i,1]=N[i-1,1]+numb_jum[i]
# }
# Simulating the driving process
F <- suppressWarnings(parse(text=gsub("^d(.+?)\\(.+?,", "r\\1(total_NumbJ,", model@measure$df$expr, perl=TRUE)))
assign("total_NumbJ",total_NumbJ, envir=yuimaEnv)
dL<-eval(F, envir=yuimaEnv)
#dL<-rnorm(total_NumbJ,mean=0,sd=1)
# L<-matrix(1,total_NumbJ,1)
# L[1]<-dL[1]
# for(j in c(2:total_NumbJ)){
# L[j]<-L[j-1] + dL[j]
# }
# Computing the processes V and Y at jump
V<-matrix(1,total_NumbJ,1)
Y<-matrix(1,info@q,total_NumbJ)
Y[,1]<-matrix(sim[1,c(3:(3+info@q-1))],info@q,1) #Starting point for unobservable State Space Process.
V[1,]<-value.a0+sum(tavect*Y[,1])
G<-matrix(1, total_NumbJ,1)
G[1]<-0
for(j in c(2:total_NumbJ)){
Y[,j]<-as.numeric(expm(AMatrix*jump_time[j])%*%Y[,j-1])+(V[j-1,])*evect*dL[j]^2
V[j,]<-value.a0+sum(tavect*Y[,j])
# }
# # Computing the process G at jump time
#
# for(j in c(2:total_NumbJ)){
G[j]<-G[j-1]+sqrt(V[j-1])*dL[j]
}
res<-approx(x=c(0,Time), y = c(0,G),
xout=seq(0,(Terminal-Initial), by=(Terminal-Initial)/n),
method = "constant")
sim[,1]<-res$y
i<-1
for(j in 1:length(Time)){
while (i*Delta < Time[j] && i <= n){
sim[i+1,3:ncolsim]<-expm(AMatrix*(Time[j]-i*Delta))%*%Y[,j]
sim[i+1,2]<-value.a0+as.numeric(tavect%*%sim[i,3:ncolsim])
i<-i+1
}
}
# # Realizations observed at integer times
# i<-1
# while(N[i]==0){
# i <- i+1
# }
# # G_obs<-numeric()
# # L_obs<-numeric()
# # V_obs<-numeric()
# # Y_obs<-matrix(0,info@q,)
# sim[c(1:(i-1)),1]<-0
# sim[c(i:n),1]<-G[N[c(i:n)]]
# # L_obs[c(1:(i-1))]<-0
# # L_obs[c(i:n)]<-L[N[c(i:n)]]
# for(j in c(1:(i-1))){
# sim[j,3:ncolsim]<-as.numeric(Y[,j])
# sim[j,2]<-value.a0+tavect%*%expm(AMatrix*j)%*%matrix(1,info@q,1)#Starting point for unobservable State Space Process
# }
# for(j in c(i:n)){
# sim[j,3:ncolsim]<-as.numeric(Y[,N[j]])
# sim[j,2]<-value.a0+as.numeric(tavect%*%expm(AMatrix*(j-Time[N[j]]))%*%Y[,N[j]])
# }
}
X <- ts(sim[-1,])
Data <- setData(X,delta = Delta, t0 = Initial)
result <- setYuima(data=Data,model=yuimaCogarch@model, sampling=yuimaCogarch@sampling)
}
if(missing(subsampling))
return(result)
subsampling(result, subsampling)
#return(result)
}
# Simulate method for an object of class cogarch.gmm.incr
setMethod("simulate","cogarch.est.incr",
function(object, nsim=1, seed=NULL, xinit, ...){
out <-aux.simulategmm(object=object, nsim=nsim, seed=seed, xinit=xinit, ...)
# out <- simulate(object = model, nsim = nsim, seed=seed, xinit=xinit,
# sampling = samp,
# method = "euler",
# increment.L = t(as.matrix(c(0,Incr.L))),
# true.parameter = true.parameter,
# )
return(out)
}
)
aux.simulategmm<-function(object, nsim=1, seed=NULL, xinit, ...){
Time<-index([email protected])
Incr.L<-coredata([email protected])
model <- object@yuima@model
EndT <- Time[length(Time)]
numb <- (length(Incr.L)+1)
valpar<-coef(object)
idx <- na.omit(match(names(valpar),model@parameter@xinit))
solnam <- model@parameter@xinit[-idx]
solval <- as.numeric(Diagnostic.Cogarch(object, display=FALSE)$meanStateVariable)
# solval <-50.33
names(solval) <- solnam
true.parameter <- as.list(c(valpar,solval))
samp <- setSampling(Initial = 0, Terminal = EndT, n = numb)
out <- simulate(object = model, nsim = nsim, seed=seed, xinit=xinit,
sampling = samp,
method = "euler",
increment.L = t(as.matrix(c(0,Incr.L))),
true.parameter = true.parameter
)
return(out)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simulate.R |
# Method for Map
setMethod("simulate", "yuima.Map",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
res <- aux.simulatOutput(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling)
return(res)
}
)
#aux.simulatOutput<-function(yuima.output, param=list(), sampling){
# param <- list(a=1,b=1,s1=0.1,s2=0.2,a1=0.1,a2=0.1)
aux.simulatOutput<-function(object, nsim, seed, xinit,
true.parameter, space.discretized, increment.W, increment.L, method, hurst,
methodfGn, sampling, subsampling){
mod <- object@model
if(missing(sampling)){
sampling <- setSampling()
}
sim.Inputs <- simulate(mod, nsim, seed, xinit,
true.parameter, space.discretized,
increment.W, increment.L, method, hurst,
methodfGn, sampling, subsampling)
for(i in c(1:object@[email protected])){
assign(object@Output@[email protected][[i]],
get.zoo.data(sim.Inputs)[[i]])
}
assign(object@Output@[email protected],
sim.Inputs@sampling@grid[[1]])
par <- unlist(true.parameter)
nam <- names(par)
for(i in c(1:length(nam))){
assign(nam[i],par[i])
}
my.data<-eval(object@Output@formula[[1]])
if(prod(object@Output@dimension)>1){
for(i in c(2:prod(object@Output@dimension))){
my.data<-cbind(my.data,
eval(object@Output@formula[[i]]))
}
}
names(my.data)<-object@Output@[email protected]
data1 <- setData(my.data)
object@data <- data1
return(object)
}
# Method for Map
setMethod("simulate", "yuima.Integral",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
res <- aux.simulatIntegral(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling)
return(res)
}
)
aux.simulatIntegral <- function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter, space.discretized = space.discretized,
increment.W = increment.W, increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling){
if(missing(sampling)){
sampling <- setSampling()
}
param <- unlist(true.parameter)
info.par <- object@[email protected]
info.var <- object@[email protected]
info.int <- object@Integral@Integrand
mod1 <- object@model
labmod.par <- mod1@parameter@all
nm <- names(param)
CondModPar <- nm%in%labmod.par
ValModPar <- param[CondModPar]
IntModPar <- param[!CondModPar]
#Simulation Internal trajectories
sim.Inputs <- simulate(mod1, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L, method, hurst,
methodfGn, sampling)
# Data of underlying SDE
Data <- get.zoo.data(sim.Inputs)
time <- index(sim.Inputs@[email protected])
my.env <- new.env()
assign([email protected],time,envir=my.env)
for(i in c(1:[email protected])){
assign([email protected][i],
as.numeric(Data[[i]]), envir = my.env)
}
df <- character(length=info.int@dimIntegrand[2])
M.dX <- matrix(0,
nrow = info.int@dimIntegrand[2],
ncol = sim.Inputs@sampling@n)
for(i in c(1:info.int@dimIntegrand[2])){
df[i] <- paste0("diff(as.numeric(",[email protected][i],"))")
M.dX[i,] <- eval(parse(text = df[i]), envir = my.env)
}
for(i in c(1:length(info.par@Integrandparam))){
cond <- nm%in%info.par@Integrandparam[i]
if(any(cond))
assign(nm[cond],param[nm[cond]], envir = my.env)
}
#assign([email protected],time[-length(time)],envir=my.env)
# matrInt <-matrix(0, nrow = info.int@dimIntegrand[1],
# ncol = info.int@dimIntegrand[2])
res <- NULL
PosInMatr <- matrix(c(1:(info.int@dimIntegrand[2]*info.int@dimIntegrand[1])),
nrow = info.int@dimIntegrand[1], ncol = info.int@dimIntegrand[2])
my.fun <- function(my.list, my.env, i){
dum <- eval(my.list,envir = my.env)
#if(length(dum)==1){
# return(rep(dum,i))
#}else{
return(dum[1:i])
#}
}
# res<-matrix(0,info.int@dimIntegrand[1],(length(time)-1))
# element <- matrix(0, nrow =info.int@dimIntegrand[1], ncol = 1)
#
# for(i in c(1:(length(time)-1))){
# assign([email protected],time[i+1],envir=my.env)
# Inter2 <- lapply(info.int@IntegrandList,
# FUN = my.fun, my.env = my.env, i = i)
# for(j in c(1:info.int@dimIntegrand[1])){
# element[j,] <- M.dX[,c(1:i)]%*%matrix(unlist(Inter2[PosInMatr[j,]]),
# ncol = info.int@dimIntegrand[2])
# }
# res[,i] <- element
# }
LengTime<-(length(time)-1)
my.listenv<-as.list(c(1:LengTime))
names(my.listenv)<- rep("i",LengTime)
globalMyEnv <-new.env()
globalMyEnv$time <- time
globalMyEnv$my.env <- my.env
element <- matrix(0, nrow =info.int@dimIntegrand[1], ncol = 1)
my.listenv2<-lapply(X=my.listenv,
globalMyEnv=globalMyEnv,
FUN = function(X,globalMyEnv){
assign([email protected],globalMyEnv$time[X+1],
envir= globalMyEnv$my.env)
assign(object@[email protected],globalMyEnv$time[c(1:X)],
envir= globalMyEnv$my.env)
Inter2 <- lapply(info.int@IntegrandList,
FUN = my.fun, my.env = globalMyEnv$my.env,
i = X)
for(j in c(1:info.int@dimIntegrand[1])){
element[j,] <- M.dX[,c(1:X)]%*%matrix(unlist(Inter2[PosInMatr[j,]]),
ncol = info.int@dimIntegrand[2])
}
list(element)
})
res<-as.numeric(unlist(my.listenv2))
res<-matrix(res,info.int@dimIntegrand[1],(length(time)-1))
res <- cbind(0,res)
rownames(res) <- object@[email protected]@out.var
my.data <- zoo(x = t(res), order.by = time)
data1 <- setData(my.data)
object@data <- data1
if(missing(subsampling))
return(object)
subsampling(object, subsampling)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simulateForMapsIntegralAndOperator.R |
setMethod("simulate", "yuima.Hawkes",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
res <- aux.simulatHawkes(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling)
return(res)
}
)
aux.simulatHawkes<- function(object, nsim, seed,
xinit, true.parameter, space.discretized, increment.W,
increment.L, method, hurst, methodfGn, sampling, subsampling){
# Here we can construct specific algorithm for the standard Hawkes process
res <- aux.simulatPPR(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling)
return(res)
# object@[email protected]@allparam
# simOzaki.aux(gFun=object@gFun@formula,a,cCoeff, Time, numJump)
}
setMethod("simulate", "yuima.PPR",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
res <- aux.simulatPPR(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling, subsampling = subsampling)
return(res)
}
)
constHazIntPr <- function(g.Fun , Kern.Fun, covariates, counting.var,statevar=NULL){
numb.Int <- length(g.Fun)
Int.Intens <- list()
dum.g <- character(length=numb.Int)
for(i in c(1:numb.Int)){
dum.g0 <- as.character(g.Fun[i])
dum.g0 <- gsub("(", "( ", fixed=TRUE,x = dum.g0)
dum.g0 <- gsub(")", " )", fixed=TRUE,x = dum.g0)
if(length(counting.var)>0){
for(j in c(1:length(counting.var))){
my.countOld <- paste0(counting.var[j] ," ")
#my.countNew <- paste0("as.numeric(", counting.var[i] ,")")
my.countNew <- paste0("(", counting.var[j] ,")")
dum.g0 <- gsub(my.countOld, my.countNew, x = dum.g0, fixed=TRUE)
my.countOld <- paste0(counting.var[j] ,"[",[email protected]@upper.var,"]")
my.countNew <- paste0("(", counting.var[j] ,")")
dum.g0 <- gsub(my.countOld, my.countNew, x = dum.g0, fixed=TRUE)
}
}
if(length(covariates)>0){
for(j in c(1:length(covariates))){
my.covarOld <- paste0(covariates[j] ," ")
my.covarNew <- covariates[j]
dum.g0 <- gsub(my.covarOld, my.covarNew, x = dum.g0, fixed=TRUE)
my.covarOld <- paste0(covariates[j] ,"[",[email protected]@upper.var,"]")
my.covarNew <- covariates[j]
dum.g0 <- gsub(my.covarOld, my.covarNew, x = dum.g0, fixed=TRUE)
}
}
dum.g[i] <- paste("tail(",dum.g0,", n=1L)")
}
dum.Ker <- as.character(unlist(Kern.Fun@Integrand@IntegrandList))
dum.Ker <- gsub("(", "( ", fixed=TRUE,x = dum.Ker)
dum.Ker <- gsub(")", " )", fixed=TRUE,x = dum.Ker)
dimKernel<-length(dum.Ker)
condIntInKer <- FALSE
for(i in c(1:dimKernel)){
if(!condIntInKer)
condIntInKer <- statevar%in%all.vars(parse(text=dum.Ker[i]))
}
if(condIntInKer){
for(i in c(1:length(statevar))){
my.countOld <- paste0(statevar[i] ," ")
my.countNew <- paste0( statevar[i] ,
"ForKernel[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(statevar[i] ,"[",[email protected]@upper.var,"]")
# my.countNew <- paste0( counting.var[i] ,
# "[ as.character( ",[email protected]@upper.var ," ) ]")
my.countNew <- paste0( "tail(",statevar[i] ,"ForKernel ,n=1L) ")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(statevar[i] ,"[",[email protected]@var.time,"]")
my.countNew <- paste0(statevar[i] ,
"ForKernel[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
}
}
if(length(counting.var)>0){
for(i in c(1:length(counting.var))){
my.countOld <- paste0(counting.var[i] ," ")
my.countNew <- paste0( counting.var[i] ,
"[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(counting.var[i] ,"[",[email protected]@upper.var,"]")
# my.countNew <- paste0( counting.var[i] ,
# "[ as.character( ",[email protected]@upper.var ," ) ]")
my.countNew <- paste0( "tail(",counting.var[i] ,",n=1L) ")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(counting.var[i] ,"[",[email protected]@var.time,"]")
my.countNew <- paste0(counting.var[i] ,
"[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
}
}
if(length(covariates)>0){
for(i in c(1:length(covariates))){
my.countOld <- paste0(covariates[i] ," ")
my.countNew <- paste0( covariates[i] ,
"[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(covariates[i] ,"[",[email protected]@upper.var,"]")
my.countNew <- paste0("tail(", covariates[i] , ", n=1L ) ")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
my.countOld <- paste0(covariates[i] ,"[",[email protected]@var.time,"]")
my.countNew <- paste0( covariates[i] ,
"[CondJumpGrid]")
dum.Ker <- gsub(my.countOld, my.countNew, x = dum.Ker, fixed=TRUE)
}
}
dif.dx <- paste("d",[email protected]@var.dx, sep="")
if(Kern.Fun@Integrand@dimIntegrand[1]==1){
dum.Ker <- paste(dum.Ker,dif.dx, sep = "*")
}else{
# dum.Ker <- paste(dum.Ker,rep(dif.dx, Kern.Fun@Integrand@dimIntegrand[1]), sep = "*")
dum.Ker <- matrix(dum.Ker,Kern.Fun@Integrand@dimIntegrand[1],
Kern.Fun@Integrand@dimIntegrand[2], byrow=T)
dum.Ker <- paste(dum.Ker,dif.dx, sep = "*")
dum.Ker <- matrix(dum.Ker,Kern.Fun@Integrand@dimIntegrand[1],
Kern.Fun@Integrand@dimIntegrand[2], byrow=T)
}
cond.Sup <- paste([email protected]@var.time, "<", [email protected]@upper.var)
dum.Ker <- paste("(",dum.Ker, ") * (", cond.Sup, ")")
dum.Ker <- paste0("sum(",dum.Ker,")")
if(Kern.Fun@Integrand@dimIntegrand[2]>1 & Kern.Fun@Integrand@dimIntegrand[1]==1){
dum.Ker <- paste(dum.Ker,collapse = " + ")
}
if(Kern.Fun@Integrand@dimIntegrand[1]>1){
mydum <- matrix(dum.Ker,Kern.Fun@Integrand@dimIntegrand[1],
Kern.Fun@Integrand@dimIntegrand[2])
dum.Ker <- character(length = Kern.Fun@Integrand@dimIntegrand[1])
for (i in c(1:Kern.Fun@Integrand@dimIntegrand[1])){
dum.Ker[i] <- paste(mydum[i,],collapse = " + ")
}
#yuima.stop("Check")
}
# dum.Ker <- paste("(",dum.Ker,") * (")
# cond.Sup <- paste([email protected]@var.time, "<", [email protected]@upper.var)
# dum.Ker <- paste(dum.Ker, cond.Sup, ")")
for(i in c(1:numb.Int)){
Int.Intens[[i]] <- parse(text = paste(dum.g[i], dum.Ker[i], sep = " + "))
}
res <- list(Intens = Int.Intens)
}
aux.simulatPPR<- function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling){
ROLDVER<-!(is(object@model@measure$df,"yuima.law"))
if(ROLDVER){
object <- aux.simulatPPRROldVersion(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling)
}else{
if(object@PPR@RegressWithCount){
object <-aux.simulatPPRWithCount(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling)
}else{
posLambda <- object@[email protected] %in% object@[email protected]
condInteFeedbackCov <- any(posLambda)
if(condInteFeedbackCov){
object <- aux.simulatPPRWithIntesFeedBack(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling,
posLambda=posLambda)
}else{
object <- aux.simulatPPRROldNew(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling)
}
}
}
return(object)
}
eulerPPR<-function(xinit,yuima,Initial,Terminal, dW, n, env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
V0 <- sdeModel@drift
V <- sdeModel@diffusion
r.size <- [email protected]
d.size <- [email protected]
# Terminal <- yuima@sampling@Terminal[1]
# Initial <- yuima@sampling@Initial[1]
#n <- ceiling((Terminal-Initial)/yuima@sampling@delta)
dL <- env$dL
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, env)
if(length(dummy.val)==1){dummy.val<-rep(dummy.val,length(xinit))}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,env)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(xinit)){
dX[i] <- dummy.val[i]
}
}else{
dX_dummy <- xinit
if(length(modelstate)==length(dX_dummy)){
for(i in 1:length(modelstate)) {
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}else{
yuima.warn("the number of model states do not match the number of initial conditions")
return(NULL)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(dX_dummy)){
dX[i] <- eval(dX_dummy[i], env)
}
}
delta <- yuima@sampling@delta
if(!length([email protected])){
b <- parse(text=paste("c(",paste(as.character(V0),collapse=","),")"))
vecV <- parse(text=paste("c(",paste(as.character(unlist(V)),collapse=","),")"))
X_mat <- .Call("euler", dX, Initial, as.integer(r.size),
rep(1, n) * delta, dW, modeltime, modelstate, quote(eval(b, env)),
quote(eval(vecV, env)), env, new.env())
tsX <- ts(data=t(X_mat), deltat=delta , start = Initial) #LM
}else{
has.drift <- sum(as.character(sdeModel@drift) != "(0)")
var.in.diff <- is.logical(any(match(unlist(lapply(sdeModel@diffusion, all.vars)), [email protected])))
p.b <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
if(has.drift){
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i], env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j],env)
}
}
} else { ##:: no drift term (faster)
tmp <- matrix(0, d.size, r.size)
if(!is.Poisson(sdeModel)){ # we do not need to evaluate diffusion
for(i in 1:d.size){
for(j in 1:r.size){
tmp[i,j] <- eval(V[[i]][j],env)
} # for j
} # foh i
} # !is.Poisson
} # else
return(tmp)
}
X_mat <- matrix(0, d.size, (n+1))
X_mat[,1] <- dX
if(has.drift){
dW <- rbind( rep(1, n)*delta , dW)
}
JP <- [email protected]
mu.size <- length(JP)
p.b.j <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
j.size <- length(JP[[1]])
tmp <- matrix(0, mu.size, j.size)
for(i in 1:mu.size){
for(j in 1:j.size){
tmp[i,j] <- eval(JP[[i]][j],env)
}
}
return(tmp)
}
dZ <- dL
if(is.null(dim(dZ)))
dZ <- matrix(dZ,nrow=1)
for(i in 1:n){
# if(i==720 & n==720){
# aa<-NULL
# }
assign([email protected], dZ[,i], env)
if([email protected]){
dZ[,i] <- 1
}
tmp.j <- p.b.j(t=Initial+(i - 1)*delta, X=dX) # YK
if(sum(dim(tmp.j))==2)
tmp.j <- as.numeric(tmp.j)
dX <- dX + p.b(t=Initial+(i - 1)*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i] # YK
X_mat[, i+1] <- dX
}
#tsX <- ts(data=t(X_mat), deltat=delta, start=yuima@sampling@Initial)
}
return(X_mat)
}
eulerPPRwithInt<-function(xinit,yuima,Initial,Terminal, dW, dL, n, env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
V0 <- sdeModel@drift
V <- sdeModel@diffusion
r.size <- [email protected]
d.size <- [email protected]
# Terminal <- yuima@sampling@Terminal[1]
# Initial <- yuima@sampling@Initial[1]
#n <- ceiling((Terminal-Initial)/yuima@sampling@delta)
#dL <- env$dL
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, env)
if(length(dummy.val)==1){dummy.val<-rep(dummy.val,length(xinit))}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,env)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(xinit)){
dX[i] <- dummy.val[i]
}
}else{
dX_dummy <- xinit
if(length(modelstate)==length(dX_dummy)){
for(i in 1:length(modelstate)) {
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}else{
yuima.warn("the number of model states do not match the number of initial conditions")
# return(NULL)
dX_dummy <- c(dX_dummy,env[[yuima@[email protected]]])
for(i in 1:length(modelstate)){
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(dX_dummy)){
dX[i] <- eval(dX_dummy[i], env)
}
}
delta <- yuima@sampling@delta
if(!length([email protected])){
b <- parse(text=paste("c(",paste(as.character(V0),collapse=","),")"))
vecV <- parse(text=paste("c(",paste(as.character(unlist(V)),collapse=","),")"))
X_mat <- .Call("euler", dX, Initial, as.integer(r.size),
rep(1, n) * delta, dW, modeltime, modelstate, quote(eval(b, env)),
quote(eval(vecV, env)), env, new.env())
tsX <- ts(data=t(X_mat), deltat=delta , start = Initial) #LM
}else{
has.drift <- sum(as.character(sdeModel@drift) != "(0)")
var.in.diff <- is.logical(any(match(unlist(lapply(sdeModel@diffusion, all.vars)), [email protected])))
p.b <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
if(has.drift){
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i], env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j],env)
}
}
} else { ##:: no drift term (faster)
tmp <- matrix(0, d.size, r.size)
if(!is.Poisson(sdeModel)){ # we do not need to evaluate diffusion
for(i in 1:d.size){
for(j in 1:r.size){
tmp[i,j] <- eval(V[[i]][j],env)
} # for j
} # foh i
} # !is.Poisson
} # else
return(tmp)
}
X_mat <- matrix(0, d.size, (n+1))
X_mat[,1] <- dX
if(has.drift){
dW <- rbind( rep(1, n)*delta , dW)
}
JP <- [email protected]
mu.size <- length(JP)
p.b.j <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
j.size <- length(JP[[1]])
tmp <- matrix(0, mu.size, j.size)
for(i in 1:mu.size){
for(j in 1:j.size){
tmp[i,j] <- eval(JP[[i]][j],env)
}
}
return(tmp)
}
dZ <- dL
if(is.null(dim(dZ)))
dZ <- matrix(dZ,nrow=1)
for(i in 1:n){
# if(i==720 & n==720){
# aa<-NULL
# }
assign([email protected], dZ[,i], env)
if([email protected]){
dZ[,i] <- 1
}
tmp.j <- p.b.j(t=Initial+(i - 1)*delta, X=dX) # YK
if(sum(dim(tmp.j))==2)
tmp.j <- as.numeric(tmp.j)
dX <- dX + p.b(t=Initial+(i - 1)*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i] # YK
X_mat[, i+1] <- dX
}
#tsX <- ts(data=t(X_mat), deltat=delta, start=yuima@sampling@Initial)
}
return(X_mat)
}
aux.simulatPPRWithCount<-function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = 0.5,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling){
samp <- sampling
Model <- object@model
gFun <- object@gFun
Kern <- object@Kernel
object@sampling <- samp
randomGenerator<-object@model@measure$df
if(missing(increment.W) | is.null(increment.W)){
if( Model@hurst!=0.5 ){
grid<-sampling2grid(object@sampling)
isregular<-object@sampling@regular
if((!isregular) || (methodfGn=="Cholesky")){
dW<-CholeskyfGn(grid, Model@hurst,[email protected])
yuima.warn("Cholesky method for simulating fGn has been used.")
} else {
dW<-WoodChanfGn(grid, Model@hurst,[email protected])
}
} else {
# delta<-(Terminal-Initial)/n
delta <- samp@delta
if(!is.Poisson(Model)){ # if pure CP no need to setup dW
dW <- rnorm(samp@n * [email protected], 0, sqrt(delta))
dW <- matrix(dW, ncol=samp@n, [email protected],byrow=TRUE)
} else {
dW <- matrix(0,ncol=samp@n,nrow=1) # maybe to be fixed
}
}
} else {
dW <- increment.W
}
if(missing(xinit)){
if(length(object@model@xinit)!=0){
xinit<-numeric(length=length(object@model@xinit))
for(i in c(1:object@[email protected]))
xinit[i] <- eval(object@model@xinit[i])
}else{
xinit <- rep(0,object@[email protected])
object@model@xinit<-xinit
}
}
if(missing(hurst)){
hurst<-0.5
}
if(samp@regular){
tForMeas<-samp@delta
NumbIncr<-samp@n
if(missing(true.parameter)){
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}else{
measureparam<-true.parameter[object@model@parameter@measure]
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}
Noise.L <- t(rand(object = randomGenerator, n=NumbIncr, param=measureparam))
rownames(Noise.L)<[email protected]
#dIncr <- apply(cbind(0,Noise.L),1,diff)
Noise.count <- Noise.L[object@[email protected],]
Noise.Laux <- Noise.L
for(i in c(1:length(object@[email protected]))){
Noise.Laux[object@[email protected][i],]<-0
}
}
myenv<-new.env()
par.len <- length(object@PPR@allparam)
if(par.len>0){
for(i in 1:par.len){
pars <- object@PPR@allparam[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(object@PPR@allparam[i], true.parameter[[j]],myenv)
}
}
}
}
assign("dL",Noise.Laux,myenv)
condMatrdW <- is.matrix(dW)
if(condMatrdW){
dimdW <- dim(dW)[2]
}else{
dimdW <- length(dW)
}
CovariateSim<- eulerPPR(xinit=xinit,yuima=object,dW=dW,
Initial=samp@Initial,Terminal=samp@Terminal,n=samp@n,
env=myenv)
rownames(CovariateSim)<- [email protected]
assign("info.PPR", object@PPR, myenv)
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(j in c(1:dimCov)){
assign(object@PPR@covariates[j],
as.numeric(CovariateSim[object@PPR@covariates[j],1]),
envir = myenv)
}
}
dimNoise<-dim(Noise.Laux)
dimCovariateSim <- dim(CovariateSim)
ExprHaz <- constHazIntPr(g.Fun = object@gFun@formula,
Kern.Fun = object@Kernel, covariates = object@PPR@covariates,
counting.var = object@[email protected],
statevar = object@[email protected])$Intens
# Start Simulation PPR
compErrHazR4 <- function(samp, Kern,
capitalTime, Model,
my.env, ExprHaz, Time, dN,
Index, pos){
assign([email protected]@var.time, Time, envir = my.env)
assign([email protected], capitalTime, envir = my.env)
l <- 1
for(i in c(1:length([email protected]@var.dx)) ){
if(any([email protected]@var.dx[i][email protected])){
assign(paste0("d",[email protected]@var.dx[i]), dN[l,], envir =my.env)
l <- l + 1
}
if([email protected]@var.dx[i]%in%my.env$info.PPR@covariates){
assign(paste0("d",[email protected]@var.dx[i]),
diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
envir =my.env)
}
}
condPointIngrid <- samp@grid[[1]]<=my.env$t
PointIngridInt <- samp@grid[[1]][condPointIngrid]
CondJumpGrid <- PointIngridInt %in% my.env$s
assign("CondJumpGrid", CondJumpGrid, envir = my.env)
Lambda <- NULL
# for(h in c(1:Index)){
# Lambdadum <- eval(ExprHaz[[h]], envir = my.env)
# Lambda <- rbind(Lambda,Lambdadum)
#
Lambda <- eval(ExprHaz[[pos]], envir = my.env)
# rownames(Lambda) <- [email protected]
return(Lambda)
}
dN <- matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
grid <- samp@grid[[1]]
const <- -log(runif(gFun@dimension[1]))
condMyTR <- const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
jumpT<-NULL
i <- 1
dimGrid <-length(grid)
cond <- const
Index <- gFun@dimension[1]
inter_i <- rep(i,Index)
noExit<-rep(T,Index)
while(any(noExit)){
for(j in c(1:Index)){
HazardRate<-0
while(cond[j]>0 && noExit[j]){
lambda<-compErrHazR4(samp, Kern, capitalTime=samp@grid[[1]][inter_i[j]],
Model, myenv, ExprHaz, Time=jumpT, dN, Index, j)
incrlambda <- lambda*delta
HazardRate <- HazardRate+incrlambda
cond[j] <- const[j]-HazardRate
inter_i[j]<-inter_i[j]+1
if(inter_i[j]>=(dimGrid-1)){
noExit[j] <- FALSE
}
if(inter_i[j]<dim(CovariateSim)[2]){
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(j in c(1:dimCov)){
assign(object@PPR@covariates[j],
as.numeric(CovariateSim[object@PPR@covariates[j],1:inter_i[j]]),
envir = myenv)
}
}
}
}
}
i <- min(inter_i)
if(any(noExit)){
if(i<dim(CovariateSim)[2]){
jumpT<-c(jumpT,grid[i])
if(dim(dN)[2]==1 & all(dN[,1]==0)){
dN[i==inter_i,1] <- Noise.count[i-1]
Noise.Laux[object@[email protected],i-1]<-Noise.count[i-1]
dumdN <- dN
}else{
dumdN <- rep(0,Index)
dumdN[i==inter_i] <- Noise.count[i-1]
Noise.Laux[object@[email protected],i-1] <- dumdN[i==inter_i]
dN <- cbind(dN,dumdN)
}
# cat("\n ", i, grid[i])
# assign("dL",Noise.Laux,myenv)
#
# CovariateSim<- eulerPPR(xinit=xinit,yuima=object,dW=dW,
# Initial=samp@Initial,Terminal=samp@Terminal,
# env=myenv)
assign("dL",Noise.Laux[,c((i-1):dimNoise[2])],myenv)
xinit <- CovariateSim[,i-1]
if(condMatrdW){
CovariateSim[,(i-1):dimCovariateSim[2]] <- eulerPPR(xinit=xinit,
yuima=object,dW=dW[,(i-1):dimdW],
Initial=samp@grid[[1]][i-1],Terminal=samp@Terminal,n=(samp@n-(i-1)+1),
env=myenv)
}else{
CovariateSim[,(i-1):dimCovariateSim[2]] <- eulerPPR(xinit=xinit,
yuima=object, dW=dW[(i-1):dimdW],
Initial=samp@grid[[1]][i-1],Terminal=samp@Terminal,n=(samp@n-(i-1)+1),
env=myenv)
}
rownames(CovariateSim)<- [email protected]
const <- -log(runif(object@gFun@dimension[1]))
condMyTR <- const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
cond <- const
if(all(noExit)){
inter_i <- rep(i, Index)
}else{
if(any(noExit)){
inter_i[noExit] <- i
inter_i[!noExit] <- samp@n+1
}
}
}
}
}
tsX <- ts(data=t(CovariateSim), deltat=delta, start=object@sampling@Initial)
object@data <- setData(original.data=tsX)
for(i in 1:length(object@[email protected]))
index(object@[email protected][[i]]) <- object@sampling@grid[[1]] ## to be fixed
#object@model@hurst <-tmphurst
if(missing(subsampling))
return(object)
subsampling(object, subsampling)
}
aux.simulatPPRWithIntesFeedBack<-function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized,
increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling,
posLambda=posLambda){
samp <- sampling
Model <- object@model
gFun <- object@gFun
Kern <- object@Kernel
object@sampling <- samp
randomGenerator<-object@model@measure$df
nameIntensityProc <- object@[email protected]
if(missing(increment.W) | is.null(increment.W)){
if( Model@hurst!=0.5 ){
grid<-sampling2grid(object@sampling)
isregular<-object@sampling@regular
if((!isregular) || (methodfGn=="Cholesky")){
dW<-CholeskyfGn(grid, Model@hurst,[email protected])
yuima.warn("Cholesky method for simulating fGn has been used.")
} else {
dW<-WoodChanfGn(grid, Model@hurst,[email protected])
}
} else {
# delta<-(Terminal-Initial)/n
delta <- samp@delta
if(!is.Poisson(Model)){ # if pure CP no need to setup dW
dW <- rnorm(samp@n * [email protected], 0, sqrt(delta))
dW <- matrix(dW, ncol=samp@n, [email protected],byrow=TRUE)
} else {
dW <- matrix(0,ncol=samp@n,nrow=1) # maybe to be fixed
}
}
} else {
dW <- increment.W
}
if(missing(xinit)){
if(length(object@model@xinit)!=0){
xinit<-numeric(length=length(object@model@xinit))
for(i in c(1:object@[email protected]))
xinit[i] <- eval(object@model@xinit[i])
}else{
xinit <- rep(0,object@[email protected])
object@model@xinit<-xinit
}
}
if(missing(hurst)){
hurst<-0.5
}
if(samp@regular){
tForMeas<-samp@delta
NumbIncr<-samp@n
if(missing(true.parameter)){
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}else{
measureparam<-true.parameter[object@model@parameter@measure]
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}
Noise.L <- t(rand(object = randomGenerator, n=NumbIncr, param=measureparam))
rownames(Noise.L)<[email protected]
#dIncr <- apply(cbind(0,Noise.L),1,diff)
Noise.count <- Noise.L[object@[email protected],]
Noise.Laux <- Noise.L
for(i in c(1:length(object@[email protected]))){
Noise.Laux[object@[email protected][i],]<-0
}
}
myenv<-new.env()
par.len <- length(object@PPR@allparam)
if(par.len>0){
for(i in 1:par.len){
pars <- object@PPR@allparam[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(object@PPR@allparam[i], true.parameter[[j]],myenv)
}
}
}
}
assign("dL",Noise.Laux,myenv)
condMatrdW <- is.matrix(dW)
if(condMatrdW){
dimdW <- dim(dW)[2]
}else{
dimdW <- length(dW)
}
assign("info.PPR", object@PPR, myenv)
dimCov <- length(object@PPR@covariates)
dimNoise<-dim(Noise.Laux)
# Start Simulation PPR
compErrHazR4 <- function(samp, Kern,
capitalTime, Model,
my.env, ExprHaz, Time, dN,
Index, pos){
assign([email protected]@var.time, Time, envir = my.env)
assign([email protected], capitalTime, envir = my.env)
l <- 1
for(i in c(1:length([email protected]@var.dx)) ){
if(any([email protected]@var.dx[i][email protected])){
assign(paste0("d",[email protected]@var.dx[i]), dN[l,], envir =my.env)
l <- l + 1
}
if([email protected]@var.dx[i]%in%my.env$info.PPR@covariates){
assign(paste0("d",[email protected]@var.dx[i]),
diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
envir =my.env)
}
}
condPointIngrid <- samp@grid[[1]]<=my.env$t
PointIngridInt <- samp@grid[[1]][condPointIngrid]
CondJumpGrid <- PointIngridInt %in% my.env$s
assign("CondJumpGrid", CondJumpGrid, envir = my.env)
Lambda <- NULL
# for(h in c(1:Index)){
# Lambdadum <- eval(ExprHaz[[h]], envir = my.env)
# Lambda <- rbind(Lambda,Lambdadum)
#
Lambda <- eval(ExprHaz[[pos]], envir = my.env)
# rownames(Lambda) <- [email protected]
return(Lambda)
}
if (dimCov>0){
for(j in c(1:dimCov)){
assign(object@PPR@covariates[j],
as.numeric(xinit[j]),
envir = myenv)
}
}
CovariateSim <-matrix(0,[email protected],(samp@n+1))
#IntensityProcInter <- matrix(0,length(nameIntensityProc),(samp@n+1))
ExprHaz <- constHazIntPr(g.Fun = object@gFun@formula,
Kern.Fun = object@Kernel, covariates = object@PPR@covariates,
counting.var = object@[email protected], statevar=nameIntensityProc)$Intens
IntensityProcInter <- as.matrix(tryCatch(eval(object@gFun@formula,envir=myenv),error =function(){1}))
dN <- matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
rownames(CovariateSim)<- [email protected]
assign(object@[email protected],CovariateSim[object@[email protected],1],envir=myenv)
grid <- samp@grid[[1]]
const <- -log(runif(gFun@dimension[1]))
condMyTR <- const<delta
AllnameIntensityProc <- paste0(nameIntensityProc,"ForKernel")
assign(AllnameIntensityProc,IntensityProcInter,envir=myenv)
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
jumpT<-NULL
i <- 1
Initial_i <- i-1
dimGrid <-length(grid)
cond <- const
Index <- gFun@dimension[1]
inter_i <- rep(i,Index)
noExit<-rep(T,Index)
while(any(noExit)){
for(j in c(1:Index)){
HazardRate<-0
while(cond[j]>0 && noExit[j]){
lambda<-compErrHazR4(samp, Kern, capitalTime=samp@grid[[1]][inter_i[j]],
Model, myenv, ExprHaz, Time=jumpT, dN, Index, j)
if(is.matrix(posLambda)){}else{
#assign(object@[email protected][posLambda],lambda, envir = myenv)
assign(nameIntensityProc,lambda[j], envir = myenv)
# myenv[[AllnameIntensityProc]][j,]<-cbind(myenv[[AllnameIntensityProc]][j,],
# lambda[j])
assign(AllnameIntensityProc,
cbind(t(myenv[[AllnameIntensityProc]][j,]),
lambda[j]),
envir=myenv)
}
incrlambda <- lambda*delta
HazardRate <- HazardRate+incrlambda
cond[j] <- const[j]-HazardRate
# if(cond[j]>0){
# dN<-cbind(dN,rep(0,Index))
# }
inter_i[j]<-inter_i[j]+1
if(inter_i[j]-1==1){
CovariateSim[,c((inter_i[j]-1):inter_i[j])]<- eulerPPRwithInt(xinit=xinit,yuima=object,dW=dW[,(inter_i[j]-1)],
dL=as.matrix(myenv$dL[,c(i-Initial_i)]),Initial=samp@Initial,Terminal=samp@grid[[1]][inter_i[j]],n=1,
env=myenv)
rownames(CovariateSim)<- [email protected]
}else{
CovariateSim[,inter_i[j]]<- eulerPPRwithInt(xinit=CovariateSim[,(inter_i[j]-1)],
yuima=object,dW=dW[,(inter_i[j]-1)],
dL=as.matrix(myenv$dL[,c(inter_i[j]-1-Initial_i)]),
Initial=samp@grid[[1]][(inter_i[j]-1)],
Terminal=samp@grid[[1]][inter_i[j]],n=1,
env=myenv)[,-1]
}
# if(inter_i[j]==66){
# aaaaa<-1
# }
if(inter_i[j]>=(dimGrid)){
noExit[j] <- FALSE
}
if(inter_i[j]<=dimGrid){
assign(object@[email protected],CovariateSim[object@[email protected][j],1:inter_i[j]],envir=myenv)
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(jj in c(1:dimCov)){
assign(object@PPR@covariates[jj],
as.numeric(CovariateSim[object@PPR@covariates[jj],1:inter_i[j]]),
envir = myenv)
}
}
}
}
}
i <- min(inter_i)
Initial_i <- i-1
if(any(noExit)){
if(i<dim(CovariateSim)[2]){
jumpT<-c(jumpT,grid[i])
if(dim(dN)[2]==1 & all(dN[,1]==0)){
dN[i==inter_i,1] <- Noise.count[i-1]
Noise.Laux[object@[email protected],i-1]<-Noise.count[i-1]
dumdN <- dN
}else{
dumdN <- rep(0,Index)
dumdN[i==inter_i] <- Noise.count[i-1]
Noise.Laux[object@[email protected],i-1] <- dumdN[i==inter_i]
dN <- cbind(dN,dumdN)
}
# cat("\n ", i, grid[i])
# assign("dL",Noise.Laux,myenv)
#
# CovariateSim<- eulerPPR(xinit=xinit,yuima=object,dW=dW,
# Initial=samp@Initial,Terminal=samp@Terminal,
# env=myenv)
assign("dL",Noise.Laux[,c((i-1):dimNoise[2])],myenv)
xinit <- CovariateSim[,i-1]
# if(condMatrdW){
# CovariateSim[,(i-1):dimCovariateSim[2]] <- eulerPPRwithInt(xinit=xinit,
# yuima=object,dW=dW[,(i-1):dimdW],
# Initial=samp@grid[[1]][i-1],Terminal=samp@Terminal,n=(samp@n-(i-1)+1),
# env=myenv)
# }else{
# CovariateSim[,(i-1):dimCovariateSim[2]] <- eulerPPRwithInt(xinit=xinit,
# yuima=object, dW=dW[(i-1):dimdW],
# Initial=samp@grid[[1]][i-1],Terminal=samp@Terminal,n=(samp@n-(i-1)+1),
# env=myenv)
# }
#
# rownames(CovariateSim)<- [email protected]
const <- -log(runif(object@gFun@dimension[1]))
condMyTR <- const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
cond <- const
if(all(noExit)){
inter_i <- rep(i, Index)
}else{
if(any(noExit)){
inter_i[noExit] <- i
inter_i[!noExit] <- samp@n+1
}
}
}
}
}
tsX <- ts(data=t(CovariateSim), deltat=delta, start=object@sampling@Initial)
object@data <- setData(original.data=tsX)
for(i in 1:length(object@[email protected]))
index(object@[email protected][[i]]) <- object@sampling@grid[[1]] ## to be fixed
#object@model@hurst <-tmphurst
if(missing(subsampling))
return(object)
subsampling(object, subsampling)
}
aux.simulatPPRROldNew<-function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = 0.5,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling){
myhawkesP <- function(simMod, Kern,
samp, Model, my.env, ExprHaz,
Time, dN){
noExit<-TRUE
const <- -log(runif(1))
delta <- samp@delta
grid <- samp@grid[[1]]
while(const<delta){
const <- -log(runif(1))
}
jumpT<-NULL
i <- 1
dimGrid <-length(grid)
cond <- const
allconst <- NULL
allcond <- NULL
allhaz <- NULL
while(noExit){
HazardRate<-0
while(cond>0 && noExit){
#lastJump <- tail(jumpT,n=1L)
lambda<-compErrHazR2(simMod, Kern, capitalTime=samp@grid[[1]][i], Model, my.env, ExprHaz,
Time=jumpT, dN)
# lambda<-hawkesInt(mu=mu, alpha=alpha, beta=beta,
# timet=grid[i], JumpT=jumpT)
incrlambda <- lambda*delta
HazardRate <- HazardRate+incrlambda
cond <- const-HazardRate
i<-i+1
if(i>=(dimGrid-1)){
noExit <- FALSE
}
if(i<dim(simMod@[email protected])[1]){
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(j in c(1:dimCov)){
# my.covdata <- simMod@[email protected][1:i,object@PPR@covariates[j]]
# names(my.covdata) <-simMod@sampling@grid[[1]][1:i]
#
# assign(object@PPR@covariates[j],
# my.covdata,
# envir = my.env)
assign(object@PPR@covariates[j],
as.numeric(simMod@[email protected][1:i,object@PPR@covariates[j]]),
envir = my.env)
}
}
# Line 354 necessary for the development of the code.
# cat("\n ", i, grid[i])
}
}
if(i<dim(simMod@[email protected])[1]){
jumpT<-c(jumpT,grid[i])
# if(i==7001){
# cat("\n",noExit)
# }
if(dN[1]==0){
#dN <- 1
dN <- simMod@[email protected][i,object@[email protected]]-simMod@[email protected][i-1,object@[email protected]]
}else{
dN <- c(dN,
simMod@[email protected][i,object@[email protected]]-simMod@[email protected][i-1,object@[email protected]])
}
#names(dN)<-jumpT
allhaz <- c(allhaz,HazardRate)
allcond <- c(allcond,cond)
cond <- const
allconst <- c(allconst, const)
const <- -log(runif(1))
while(const<delta){
const <- -log(runif(1))
}
}
}
return(list(jumpT=jumpT,allcond=allcond,allconst=allconst, allhaz=allhaz))
}
#myhawkesPMulti
myhawkesPMulti <- function(simMod, Kern,
samp, Model, my.env, ExprHaz,
Time, dN, Index){
#noExit<-TRUE
delta <- samp@delta
grid <- samp@grid[[1]]
const <- -log(runif(object@gFun@dimension[1]))
condMyTR <- const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
# while(const<delta){
# const <- -log(runif(1))
# }
jumpT<-NULL
i <- 1
dimGrid <-length(grid)
cond <- const
inter_i <- rep(i,Index)
noExit<-rep(T,Index)
while(any(noExit)){
for(j in c(1:Index)){
HazardRate<-0
while(cond[j]>0 && noExit[j]){
lambda<-compErrHazR3(simMod, Kern, capitalTime=samp@grid[[1]][inter_i[j]],
Model, my.env, ExprHaz, Time=jumpT, dN, Index, j)
# lambda<-hawkesInt(mu=mu, alpha=alpha, beta=beta,
# timet=grid[i], JumpT=jumpT)
incrlambda <- lambda*delta
HazardRate <- HazardRate+incrlambda
cond[j] <- const[j]-HazardRate
inter_i[j]<-inter_i[j]+1
if(inter_i[j]>=(dimGrid-1)){
noExit[j] <- FALSE
}
if(inter_i[j]<dim(simMod@[email protected])[1]){
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(jj in c(1:dimCov)){
# my.covdata <- simMod@[email protected][1:i,object@PPR@covariates[j]]
# names(my.covdata) <-simMod@sampling@grid[[1]][1:i]
#
# assign(object@PPR@covariates[j],
# my.covdata,
# envir = my.env)
assign(object@PPR@covariates[jj],
as.numeric(simMod@[email protected][1:inter_i[j],object@PPR@covariates[jj]]),
envir = my.env)
}
}
# Line 354 necessary for the development of the code.
# cat("\n ", i, grid[i])
}
}
}
i <- min(inter_i)
if(any(noExit)){
if(i<dim(simMod@[email protected])[1]){
jumpT<-c(jumpT,grid[i])
if(dim(dN)[2]==1 & all(dN[,1]==0)){
dN[i==inter_i,1] <- 1
dumdN <- dN
}else{
dumdN <- rep(0,Index)
dumdN[i==inter_i] <- 1
dN <- cbind(dN,dumdN)
}
#names(dN)<-jumpT
# const <- -log(runif(1))
# while(const<delta){
# const <- -log(runif(1))
# }
const <- -log(runif(object@gFun@dimension[1]))
condMyTR <- const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- -log(runif(length(condMyTR)))
condMyTR <- const<delta
}else{
const[condMyTR] <- -log(runif(sum(condMyTR)))
condMyTR <- const<delta
}
}
cond <- const
if(all(noExit)){
inter_i <- rep(i, Index)
}else{
if(any(noExit)){
inter_i[noExit] <- i
inter_i[!noExit] <- samp@n+1
}
}
}
}
}
return(list(jumpT=jumpT,dN = dN))
}
# compErrHazR2 <- function(simMod, Kern,
# capitalTime, Model, my.env, ExprHaz,
# Time, dN){
# # dummyLambda <- numeric(length=(samp@n+1))
# if(length([email protected]@var.dx)==1){
# # MyPos <- sum(samp@grid[[1]]<=tail(Time,n=1L))
# assign([email protected]@var.time, Time, envir = my.env)
# # cond <- -log(cost)-sum(dummyLambda)*samp@delta
#
# assign([email protected], capitalTime, envir = my.env)
# assign(paste0("d",[email protected]@var.dx), dN, envir =my.env)
#
# condPointIngrid <- simMod@sampling@grid[[1]]<=my.env$t
# PointIngridInt <- simMod@sampling@grid[[1]][condPointIngrid]
# CondJumpGrid <- PointIngridInt %in% my.env$s
# assign("CondJumpGrid", CondJumpGrid, envir = my.env)
#
# Lambda <- eval(ExprHaz[[1]], envir=my.env)
# return(Lambda)
# }else{
# if(Kern@Integrand@dimIntegrand[1]==1){
# assign([email protected]@var.time, Time, envir = my.env)
# # cond <- -log(cost)-sum(dummyLambda)*samp@delta
#
# assign([email protected], capitalTime, envir = my.env)
# for(i in c(1:length([email protected]@var.dx)) ){
# if([email protected]@var.dx[i][email protected]){
# assign(paste0("d",[email protected]@var.dx[i]), dN, envir =my.env)
# }
# if([email protected]@var.dx[i]%in%my.env$info.PPR@covariates){
# assign(paste0("d",[email protected]@var.dx[i]),
# diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
# envir =my.env)
# }
# if([email protected]@var.dx[i]%in%[email protected]){
# assign(paste0("d",[email protected]@var.dx[i]),
# diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
# envir =my.env)
# }
# }
# condPointIngrid <- simMod@sampling@grid[[1]]<=my.env$t
# PointIngridInt <- simMod@sampling@grid[[1]][condPointIngrid]
# CondJumpGrid <- PointIngridInt %in% my.env$s
# assign("CondJumpGrid", CondJumpGrid, envir = my.env)
#
# Lambda <- eval(ExprHaz[[1]], envir=my.env)
# return(Lambda)
# }
# }
# }
compErrHazR2 <- function(simMod, Kern,
capitalTime, Model, my.env, ExprHaz,
Time, dN){
# dummyLambda <- numeric(length=(samp@n+1))
if(length([email protected]@var.dx)==1){
# MyPos <- sum(samp@grid[[1]]<=tail(Time,n=1L))
assign([email protected]@var.time, Time, envir = my.env)
# cond <- -log(cost)-sum(dummyLambda)*samp@delta
assign([email protected], capitalTime, envir = my.env)
assign(paste0("d",[email protected]@var.dx), dN, envir =my.env)
condPointIngrid <- simMod@sampling@grid[[1]]<=my.env$t
PointIngridInt <- simMod@sampling@grid[[1]][condPointIngrid]
CondJumpGrid <- PointIngridInt %in% my.env$s
assign("CondJumpGrid", CondJumpGrid, envir = my.env)
Lambda <- eval(ExprHaz[[1]], envir=my.env)
return(Lambda)
}else{
if(Kern@Integrand@dimIntegrand[1]==1){
assign([email protected]@var.time, Time, envir = my.env)
# cond <- -log(cost)-sum(dummyLambda)*samp@delta
assign([email protected], capitalTime, envir = my.env)
for(i in c(1:length([email protected]@var.dx)) ){
if([email protected]@var.dx[i][email protected]){
assign(paste0("d",[email protected]@var.dx[i]), dN, envir =my.env)
}
if([email protected]@var.dx[i]%in%my.env$info.PPR@covariates){
assign(paste0("d",[email protected]@var.dx[i]),
diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
envir =my.env)
}
if([email protected]@var.dx[i]%in%[email protected]){
assign(paste0("d",[email protected]@var.dx[i]),
diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
envir =my.env)
}
}
condPointIngrid <- simMod@sampling@grid[[1]]<=my.env$t
PointIngridInt <- simMod@sampling@grid[[1]][condPointIngrid]
CondJumpGrid <- PointIngridInt %in% my.env$s
assign("CondJumpGrid", CondJumpGrid, envir = my.env)
Lambda <- eval(ExprHaz[[1]], envir=my.env)
return(Lambda)
}
}
}
compErrHazR3 <- function(simMod, Kern,
capitalTime, Model, my.env, ExprHaz, Time, dN, Index, pos){
assign([email protected]@var.time, Time, envir = my.env)
assign([email protected], capitalTime, envir = my.env)
l <- 1
for(i in c(1:length([email protected]@var.dx)) ){
if(any([email protected]@var.dx[i][email protected])){
assign(paste0("d",[email protected]@var.dx[i]), dN[l,], envir =my.env)
l <- l + 1
}
if([email protected]@var.dx[i]%in%my.env$info.PPR@covariates){
assign(paste0("d",[email protected]@var.dx[i]),
diff(c(0,my.env[[[email protected]@var.dx[i]]])) ,
envir =my.env)
}
}
condPointIngrid <- simMod@sampling@grid[[1]]<=my.env$t
PointIngridInt <- simMod@sampling@grid[[1]][condPointIngrid]
CondJumpGrid <- PointIngridInt %in% my.env$s
assign("CondJumpGrid", CondJumpGrid, envir = my.env)
Lambda <- NULL
# for(h in c(1:Index)){
# Lambdadum <- eval(ExprHaz[[h]], envir = my.env)
# Lambda <- rbind(Lambda,Lambdadum)
#
Lambda <- eval(ExprHaz[[pos]], envir = my.env)
# rownames(Lambda) <- [email protected]
return(Lambda)
}
if(missing(hurst)){
hurst<-0.5
}
samp <- sampling
Model <- object@model
gFun <- object@gFun
Kern <- object@Kernel
if(missing(xinit)){
if(object@PPR@RegressWithCount){
yuima.warn("Counting Variables are also covariates.
In this case, the algorthim will be implemented
as soon as possible.")
return(NULL)
}
}else{
if(object@PPR@RegressWithCount){
yuima.warn("Counting Variables are also covariates.
In this case, the algorthim will be implemented
as soon as possible.")
return(NULL)
}
}
if(!object@PPR@RegressWithCount && !object@PPR@IntensWithCount){
auxg <- setMap(func = gFun@formula, yuima =Model)
dummyKernIntgrand <- Kern@Integrand@IntegrandList
dummyUpperTime<- paste0([email protected]@upper.var,
[email protected]@upper.var,
collapse = "")
dummyTime <[email protected]
for(i in c(1:length(dummyKernIntgrand))){
if([email protected]@upper.var %in% all.vars(dummyKernIntgrand[[i]])){
dumExpr <- paste0("substitute(expression(",
dummyKernIntgrand[[i]],"), list(",
[email protected]@upper.var,
" = as.symbol(dummyUpperTime), ",
[email protected]@var.time,
" = as.symbol([email protected])))")
dummyKernIntgrand[[i]] <- eval(parse(text=dumExpr))
}
}
auxIntMy <- unlist(lapply(dummyKernIntgrand, FUN = function(X){as.character(X)[2]}))
auxIntMy <- matrix(auxIntMy, Kern@Integrand@dimIntegrand[1],
Kern@Integrand@dimIntegrand[2], byrow=T)
if(object@[email protected]@var.dx==object@[email protected]@var.time){
auxInt <- setIntegral(yuima = Model,
integrand = auxIntMy,
var.dx = [email protected],
upper.var = dummyUpperTime,
lower.var = [email protected]@lower.var)
}else{
auxInt <- setIntegral(yuima = Model,
integrand = auxIntMy,
var.dx =object@[email protected]@var.dx ,
upper.var = dummyUpperTime,
lower.var = [email protected]@lower.var)
}
randomGenerator<-object@model@measure$df
if(samp@regular){
tForMeas<-samp@delta
NumbIncr<-samp@n
if(missing(true.parameter)){
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}else{
measureparam<-true.parameter[object@model@parameter@measure]
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}
Noise.L <- t(rand(object = randomGenerator, n=NumbIncr, param=measureparam))
Noise.W <- t(rnorm(NumbIncr, 0,tForMeas))
if(length(object@model@diffusion[[1]])>1){
for(i in c(2:length(object@model@diffusion[[1]]))){
Noise.W <- rbind(Noise.W, rnorm(NumbIncr, 0,tForMeas))
}
}
if(missing(xinit)){
simg <- simulate(object = auxg, true.parameter = true.parameter[auxg@Output@param@allparam],
sampling = samp, hurst = hurst,
increment.W = Noise.W, increment.L = Noise.L)
simK <- simulate(object = auxInt, true.parameter = true.parameter[auxInt@[email protected]@allparam],
sampling = samp, hurst = hurst,
increment.W = Noise.W,
increment.L = Noise.L)
Lambda.data <- simg@[email protected]+simK@[email protected]
Pos<-0
globPos<-Pos
condWhile <- TRUE
while(condWhile){
Hazard<--cumsum(as.numeric(Lambda.data)[c(Pos:(samp@n+1))])*samp@delta
U<-runif(1)
CondPos <- log(U)<=Hazard
Pos <- Pos+sum(CondPos)
if(Pos > (samp@n+1)){
condWhile <- FALSE
}else{
globPos <- c(globPos,Pos)
}
}
globPos <- unique(globPos)
globPos <- globPos[(globPos<=samp@n)]
NewNoise.L <- Noise.L
cod <[email protected]%in%object@[email protected]
NeWNoise.W<-Noise.W
NeWNoise.W[cod,] <- 0
NewNoise.L[cod,] <- 0
NewNoise.L[cod,globPos[-1]] <- Noise.L[cod,globPos[-1]]
simM <- simulate(object = Model, true.parameter = true.parameter[Model@parameter@all],
sampling = samp, hurst = hurst,
increment.W = NeWNoise.W,
increment.L = NewNoise.L)
object@data <- simM@data
object@sampling <- samp
return(object)
#Lambda.data <- simg@[email protected]+simK@[email protected]
}else{
simg <- simulate(object = auxg, xinit=xinit,
sampling = samp)
}
}
}else{
if(!object@PPR@RegressWithCount && object@PPR@IntensWithCount){
## Here we consider the case where we have a counting variable in the intensity but
## we haven't it in the coefficients of the covariates.
# Simulation of the noise
DummyT <- c(true.parameter[Model@parameter@measure], samp@delta)
names(DummyT) <- c(names(true.parameter[Model@parameter@measure]),
[email protected])
increment.L <- rand(object = Model@measure$df,
n = samp@n ,
param = DummyT)
if(!is.matrix(increment.L)){
increment.L <- matrix(increment.L,ncol = 1)
}
if(missing(xinit)){
simMod <- simulate(object = Model, hurst = hurst,
sampling = samp,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}else{
simMod <- simulate(object = Model, hurst = hurst,
sampling = samp, xinit =xinit,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}
colnames(simMod@[email protected]) <- [email protected]
Data.tot <- as.matrix(simMod@[email protected])
ExprHaz <- constHazIntPr(g.Fun = object@gFun@formula,
Kern.Fun = object@Kernel, covariates = object@PPR@covariates,
counting.var = object@[email protected],
statevar = object@[email protected])$Intens
# if(FALSE){
if(length(ExprHaz)>=1){
Time <- samp@Initial
my.env <- new.env()
assign("info.PPR", object@PPR, my.env)
for(i in c(1:length(object@PPR@allparam))){
assign(object@PPR@allparam[i],
as.numeric(true.parameter[object@PPR@allparam[i]]),
envir = my.env)
}
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(j in c(1:dimCov)){
assign(object@PPR@covariates[j],
as.numeric(simMod@[email protected][1,object@PPR@covariates[j]]),
envir = my.env)
}
}
if(object@gFun@dimension[1]==1){
#if(FALSE){
IntensityProc <- 0
#set.seed(1)
dN <- 0
prova1 <- myhawkesP(simMod, Kern,
samp, Model, my.env, ExprHaz, Time, dN)
}else{
CPP<-FALSE
IntensityProc <- matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
#set.seed(1)
dN <- matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
prova1 <- myhawkesPMulti(simMod, Kern,
samp, Model, my.env, ExprHaz, Time, dN,
Index = object@gFun@dimension[1])
Time<-unique(prova1$jumpT)
dN <- prova1$dN[,1:length(Time)]
cond <- samp@grid[[1]][-1] %in% Time
countVar <- [email protected] %in% object@[email protected]
increment.L[!cond, countVar]<-0
increment.L[cond, countVar]<-t(dN)
if(missing(xinit)){
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}else{
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp, xinit =xinit,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}
object@data<-simModNew@data
object@sampling<-simModNew@sampling
return(object)
}
#cond <- samp@grid[[1]][-1] %in% Time[-1]
Time<-prova1$jumpT
cond <- samp@grid[[1]][-1] %in% Time
countVar <- [email protected] %in% object@[email protected]
increment.L[!cond, countVar]<-0
if(missing(xinit)){
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}else{
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp, xinit =xinit,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}
object@data<-simModNew@data
object@sampling<-simModNew@sampling
return(object)
}else{
my.env <- new.env()
assign("info.PPR", object@PPR, my.env)
# ExprHaz
for(i in c(1:length(object@PPR@allparam))){
assign(object@PPR@allparam[i],
as.numeric(true.parameter[object@PPR@allparam[i]]),
envir = my.env)
}
dimCov <- length(object@PPR@covariates)
if (dimCov>0){
for(j in c(1:dimCov)){
assign(object@PPR@covariates[j],
as.numeric(simMod@[email protected][1,object@PPR@covariates[j]]),
envir = my.env)
}
}
CPP<-FALSE
IntensityProc <- matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
#set.seed(1)
#dN<-matrix(0,object@gFun@dimension[1],object@gFun@dimension[2])
dN <- NULL
CondGlobal <- TRUE
CondWhile <- TRUE
# JumpTime <- samp@grid[[1]]
u_bar <- samp@Initial
u_old <- samp@Initial
jumpT<-NULL
posInitial <- 1
while(CondGlobal){
CondWhile <- TRUE
const <- log(runif(object@gFun@dimension[1]))
delta <- samp@delta
grid <- samp@grid[[1]]
condMyTR <- -const<delta
while(any(condMyTR)){
if(sum(condMyTR)==0){
const <- log(runif(length(condMyTR)))
condMyTR <- -const<delta
}else{
const[condMyTR] <- log(runif(sum(condMyTR)))
condMyTR <- -const<delta
}
}
posfin <- sum(samp@grid[[1]]<=u_bar)
dimGrid <-length(grid)
cond <- const
allconst <- NULL
allcond <- NULL
allhaz <- NULL
# if(u_bar>=47.83){
# aaa<-1
# }
checkFunDum_old <- const
checkFunDum <- const
count <- 0
while(CondWhile & count<20){
HowManylambda <- posfin-posInitial+1
lambda <- matrix(NA,object@gFun@dimension[1],HowManylambda)
for(hh in c(1:HowManylambda)){
lambda[,hh] <- compErrHazR3(simMod, Kern,
capitalTime=samp@grid[[1]][hh+(posInitial-1)],
Model, my.env, ExprHaz,
Time=jumpT, dN, object@gFun@dimension[1])
}
# Find the optimal u_i next as minimum
u_next_comp <- numeric(length = object@gFun@dimension[1])
FunDum <- numeric(length=object@gFun@dimension[1])
for(l in c(1:object@gFun@dimension[1])){
FunDum[l] <- const[l] + sum(lambda[l, ]*delta)
denomi <- lambda[l,HowManylambda]
u_next_comp[l] <- u_bar-FunDum[l]/denomi
}
u_next <- min(u_next_comp)
if(abs(tail(grid[grid<=u_next],1L) - tail(grid[grid<=u_bar],1L))<delta/2){
CondWhile<-FALSE
}
condpos <- u_next_comp %in% u_next
checkFunDumAll <- FunDum[condpos]
checkFunDum <- checkFunDumAll
if(u_next > u_old){
if(checkFunDum_old<=0){
if(checkFunDum <= 0){
u_old<-u_bar
checkFunDum_old <- checkFunDum
}else{
checkFunDum_old <- checkFunDum
}
}
u_bar <- u_next
}else{
if(CondWhile){
u_bar <- (u_bar + u_old)/2
}else{
u_bar <- u_next
}
}
posfin <- sum(samp@grid[[1]]<=u_bar)
count <- count+1
#end while
}
next_jump <- tail(grid[grid<=u_next],1L)
dummydN <- rep(0,object@gFun@dimension[1])
for(hhh in c(1:object@gFun@dimension[1])){
#condJumpComp <- tail(grid[grid<=u_next_comp[hhh]],1L)==u_bar
condJumpComp <- u_next == u_next_comp[hhh]
if(condJumpComp)
dummydN[hhh] <- 1
}
dN <- cbind(dN,dummydN)
# if(length(jumpT)>0){
# if(abs(tail(jumpT,1L)-u_bar)<(delta-10^(-12))){
# u_bar <- u_bar + delta
# }
# }
if(length(jumpT)>0){
if(tail(jumpT, 1L)+delta >= next_jump){
next_jump <- next_jump+delta
}
}else{
if(next_jump < delta){
next_jump <- next_jump+delta
}
}
jumpT<-c(jumpT,next_jump)
# cat("\n ", c(next_jump, checkFunDum,count))
u_bar <- tail(jumpT,1L)
posInitial<- sum(grid<=next_jump)
posfin <- posInitial
u_old <- next_jump
if((next_jump+delta)>=samp@Terminal-delta){
CondGlobal <- FALSE
}
# end First while
}
#return(list(dN=dN,jumpT=jumpT))
Time<-jumpT
cond <- samp@grid[[1]][-1] %in% Time
countVar <- [email protected] %in% object@[email protected]
increment.L[!cond, countVar]<-0
if(missing(xinit)){
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}else{
simModNew <- simulate(object = Model, hurst = hurst,
sampling = samp, xinit =xinit,
true.parameter = true.parameter[Model@parameter@all],
increment.L = t(increment.L))
}
object@data<-simModNew@data
object@sampling<-simModNew@sampling
return(object)
}
}
}
return(NULL)
}
# SolvePPR <- function(posMid, posLeft, posRight, solveLeft = NULL, solveRight = NULL,
# cost, Kern, simMod, samp, Model, ExprHaz,
# my.env, Time, IntensityProc){
#
# if((posMid+1)>=(samp@n+1)){
# mylist <- list(VeryExit = TRUE)
# return(mylist)
# }
# if((posMid+1)>=(samp@n+1)){
# mylist <- list(VeryExit = TRUE)
# return(mylist)
# }
#
#
# solveMid<- compErrHazR(posMid, simMod, Kern, samp, Model, my.env, ExprHaz, cost, Time)
# if(solveMid$solveLambda <= 0){
# # first check
# if(solveMid$solveLambda<0 ){
# if(posLeft == (posMid-1)){
# if(solveLeft*solveMid$solveLambda<0){
# mylist <- list()
# mylist$exit <- TRUE
# mylist$left <- TRUE
# mylist$posLeft <- posMid
# mylist$posRight <- samp@n+1
# mylist$solveLeft <- solveMid$solveLambda
# mylist$solveRight <- NULL
# mylist$Time <- c(Time,samp@grid[[1]][-1][posMid])
# mylist$IntensityProc <- c(IntensityProc, solveMid$dummyLambda)
#
# return(mylist)
# }
# }
# solveMidLeft <- compErrHazR(posMid-1, simMod, Kern, samp, Model, my.env, ExprHaz, cost, Time)
# if(solveMidLeft$solveLambda >=0){
# mylist <- list()
# mylist$exit <- TRUE
# mylist$left <- TRUE
# mylist$posLeft <- posMid-1
# mylist$posRight <- samp@n+1
# mylist$solveLeft <- solveMidLeft$solveLambda
# mylist$solveRight <- NULL
# mylist$Time <- c(Time,samp@grid[[1]][-1][posMid-1])
# mylist$IntensityProc <- c(IntensityProc, solveMidLeft$dummyLambda)
# return(mylist)
# }else{
# mylist <- list()
# mylist$exit <- FALSE
# mylist$left <- TRUE
# mylist$posLeft <- posLeft
# mylist$posRight <- posMid
# mylist$solveLeft <- solveLeft
# mylist$solveRight <-solveMidLeft$solveLambda
# mylist$Time <- Time
# mylist$IntensityProc <- c(IntensityProc)
# return(mylist)
# }
# }
# }
# if(solveMid$solveLambda==0){
# mylist <- list()
# mylist$exit <- TRUE
# mylist$left <- FALSE
# mylist$posLeft <-posMid
# mylist$posRight <- samp@n+1
# mylist$solveLeft <- solveMid$solveLambda
# mylist$solveRight <- solveRight
# mylist$Time <- c(Time,samp@grid[[1]][-1][posMid-1])
# mylist$IntensityProc <- c(IntensityProc, solveMid$dummyLambda)
# return(mylist)
# }
# if(solveMid$solveLambda > 0 && (posMid+1) <(samp@n+1)){
# solveMidRight <- compErrHazR(posMid+1, simMod, Kern, samp, Model, my.env, ExprHaz, cost, Time)
# if(solveMidRight$solveLambda <=0){
# mylist <- list()
# mylist$exit <- TRUE
# mylist$left <- FALSE
# mylist$posLeft <- posMid+1
# mylist$posRight <- samp@n+1
# mylist$solveLeft <- solveMidRight$solveLambda
# mylist$solveRight <- solveRight
# mylist$Time <- c(Time,samp@grid[[1]][-1][posMid+1])
# mylist$IntensityProc <- c(IntensityProc, solveMidRight$dummyLambda)
# return(mylist)
# }else{
# mylist <- list()
# mylist$exit <- FALSE
# mylist$left <- FALSE
# mylist$posLeft <- posMid+1
# mylist$posRight <- posRight
# mylist$solveLeft <- solveMidRight$solveLambda
# mylist$solveRight <-solveRight
# mylist$Time <- Time
# mylist$IntensityProc <- c(IntensityProc)
# return(mylist)
# }
# }
# }
# SolvePPR <- function(TopposInGridIn, OldTimePoint, solveLambdaInOld,
# cost, Kern, simMod, samp, Model, ExprHaz, dN,
# LastTime, my.env, Time, IntensityProc, checkside = FALSE,
# solveLeft=NULL, solveRight=NULL){
#
# if(is.null(solveLambdaInOld)){
# solveLambdaOld <- -log(cost)
# solveLeft <- solveLambdaOld
# solveRight <- NULL
# dummyLambda <- numeric(length=(TopposInGridIn+1))
# if(length([email protected]@var.dx)==1){
# dN <- rep(0, (TopposInGridIn+1))
# #if(length(Time)==1){
# con <- (samp@grid[[1]] %in% Time)
# dN[c(FALSE, con)[c(1:length(dN))]] <- as.numeric(simMod@[email protected][c(FALSE, con[-length(con)]),[email protected]@var.dx]
# -simMod@[email protected][con,[email protected]@var.dx])
# #}
# }else{
#
# }
# for(i in c(2:(TopposInGridIn+1))){
# posInGrid <- i
# LastTime <- samp@grid[[1]][(posInGrid)]
# LastStime <- samp@grid[[1]][c(1:(posInGrid-1))]
# assign([email protected], LastTime, envir = my.env)
# assign([email protected]@var.time, LastStime, envir = my.env)
# assign(paste0("d",[email protected]@var.dx), dN[c(2:posInGrid)], envir =my.env)
# dummyLambda[i] <- eval(ExprHaz[[1]], envir=my.env)
# }
# solveLambdaOld00 <- -log(cost)-sum(dummyLambda[c(sum(samp@grid[[1]]<=tail(Time,n=1L)):(TopposInGridIn+1))])
# if(solveLambdaOld*solveLambdaOld00<0){
# TotposInGrid<-samp@n
# mylist <- list(InfTopposInGridInOld = min(TopposInGridIn,TotposInGrid),
# supTopposInGridInOld = max(TopposInGridIn,TotposInGrid))
# if(mylist$InfTopposInGridInOld==TopposInGridIn){
# mylist$left <- TRUE
# }else{
# mylist$left <- FALSE
# }
# mylist$TotposInGrid <- TopposInGridIn+1
# mylist$OldSolveLambda <- solveLambdaOld00
# mylist$solveLeft <- solveLambdaOld00
# mylist$solveRight <- solveRight
# mylist$exit <- TRUE
# mylist$Time <- c(Time,samp@grid[[1]][-1][mylist$TotposInGrid])
# mylist$IntensityProc <- c(IntensityProc, tail(dummyLambda,n=1L))
# return(mylist)
# }
#
# }else{
# if(TopposInGridIn>1){
# solveLambdaOld <- solveLambdaInOld
# }else{
#
# dummyLambda <- numeric(length=(TopposInGridIn-1))
# if(length([email protected]@var.dx)==1){
# dN <- rep(0, (TopposInGridIn))
# dN[(TopposInGridIn)] <- as.numeric(simMod@[email protected][TopposInGridIn,[email protected]@var.dx]
# -simMod@[email protected][TopposInGridIn-1,[email protected]@var.dx])
# }else{
#
# }
# for(i in c(2:(TopposInGridIn))){
# posInGrid <- i
# LastTime <- samp@grid[[1]][(posInGrid)]
# LastStime <- samp@grid[[1]][c(1:(posInGrid-1))]
# assign([email protected], LastTime, envir = my.env)
# assign([email protected]@var.time, LastStime, envir = my.env)
# assign(paste0("d",[email protected]@var.dx), dN[c(1:posInGrid)], envir =my.env)
# dummyLambda[i] <- eval(ExprHaz[[1]], envir=my.env)
# }
#
#
# solveLambdaOld <- -log(cost)-sum(dummyLambda)
#
# }
# }
#
# TotposInGrid <- floor(abs((OldTimePoint)-TopposInGridIn)/2)+min(TopposInGridIn,(OldTimePoint))
#
# cat(sprintf("\n%.5f ", TotposInGrid))
#
#
# dummyLambda <- numeric(length=(TotposInGrid-1))
# if(length([email protected]@var.dx)==1){
# dN <- rep(0, (TotposInGrid))
# con <- (samp@grid[[1]] %in% Time)
# con[TotposInGrid-1] <- TRUE
# dN[c(FALSE, con)[c(1:length(dN))]] <- as.numeric(simMod@[email protected][c(FALSE, con[-length(con)]),[email protected]@var.dx]
# -simMod@[email protected][con,[email protected]@var.dx])
# }else{
#
# }
# for(i in c(2:(TotposInGrid))){
# posInGrid <- i
# LastTime <- samp@grid[[1]][(posInGrid)]
# LastStime <- samp@grid[[1]][c(1:(posInGrid-1))]
# assign([email protected], LastTime, envir = my.env)
# assign([email protected]@var.time, LastStime, envir = my.env)
# assign(paste0("d",[email protected]@var.dx), dN[c(2:posInGrid)], envir =my.env)
# dummyLambda[i] <- eval(ExprHaz[[1]], envir=my.env)
# }
#
#
# Solvelambda1 <- -log(cost)-sum(dummyLambda[c(sum(samp@grid[[1]]<=tail(Time,n=1L)):(TotposInGrid))])
# TotposInGridFin <- TotposInGrid
#
# if(Solvelambda1*solveLambdaOld < 0 | Solvelambda1*solveLambdaOld > 0){
#
# if(solveLeft*Solvelambda1>0){
# #solveLeft<-Solvelambda1
# TotposInGridFin <- TotposInGridFin+1
# dummyLambda <- numeric(length=(TotposInGridFin))
# }else{
# #solveRight <- Solvelambda1
# TotposInGridFin <- TotposInGridFin-1
# dummyLambda <- numeric(length=(TotposInGridFin-1))
# }
#
# if(length([email protected]@var.dx)==1){
# dN <- rep(0, (TotposInGridFin))
# # dN[(TotposInGridFin)] <- as.numeric(simMod@[email protected][TotposInGridFin,[email protected]@var.dx]
# # -simMod@[email protected][TotposInGridFin-1,[email protected]@var.dx])
# con <- (samp@grid[[1]] %in% Time)
# con[TotposInGridFin-1] <- TRUE
# dN[c(FALSE, con)[c(1:length(dN))]] <- as.numeric(simMod@[email protected][c(FALSE, con[-length(con)]),[email protected]@var.dx]
# -simMod@[email protected][con,[email protected]@var.dx])
# }else{
#
# }
# for(i in c(2:(TotposInGridFin))){
# posInGrid <- i
# LastTime <- samp@grid[[1]][(posInGrid)]
# LastStime <- samp@grid[[1]][c(1:(posInGrid-1))]
# assign([email protected], LastTime, envir = my.env)
# assign([email protected]@var.time, LastStime, envir = my.env)
# assign(paste0("d",[email protected]@var.dx), dN[c(2:posInGrid)], envir =my.env)
# dummyLambda[i] <- eval(ExprHaz[[1]], envir=my.env)
# }
#
#
# Solvelambda2 <- -log(cost)-sum(dummyLambda[c(sum(samp@grid[[1]]<=tail(Time,n=1L)):(TotposInGridFin))])
# if(Solvelambda2*Solvelambda1<0){
# mylist <- list(InfTopposInGridInOld = min(TopposInGridIn,TotposInGridFin),
# supTopposInGridInOld = max(TopposInGridIn,TotposInGridFin))
# if(mylist$InfTopposInGridInOld==TopposInGridIn){
# mylist$left <- TRUE
# mylist$solveLeft <- solveLeft
# mylist$solveRight <- Solvelambda2
# }else{
# mylist$left <- FALSE
# mylist$solveRight <- solveRight
# mylist$solveLeft <- Solvelambda2
# }
# # TotposInGrid <- floor(abs(TotposInGrid-TopposInGridIn)/2)+min(TotposInGrid,TopposInGridIn)
#
# mylist$TotposInGrid <- TotposInGridFin
# mylist$OldSolveLambda <- Solvelambda2
#
# mylist$exit <- TRUE
# # mylist$Time <- c(Time,my.env$t)
# mylist$Time <- c(Time,samp@grid[[1]][-1][mylist$TotposInGrid])
# mylist$IntensityProc<- c(IntensityProc,tail(dummyLambda,n=1L))
# return(mylist)
# }else{
# mylist <- list(InfTopposInGridInOld = min(TopposInGridIn,TotposInGridFin),
# supTopposInGridInOld = max(TopposInGridIn,TotposInGridFin))
#
# if(solveLambdaOld>Solvelambda2){
# mylist$left <- TRUE
# mylist$solveLeft <- solveLeft
# mylist$solveRight <- Solvelambda2
# }else{
# mylist$left <- FALSE
# mylist$solveRight <- solveRight
# mylist$solveLeft <- Solvelambda2
# }
# }
# # if(solveLambdaOld>0){
# # if(solveLambdaOld>Solvelambda2){
# # mylist$left <- TRUE
# # }else{
# # TotposInGridFin <- TotposInGridFin-1
# # dummyLambda <- numeric(length=(TotposInGridFin-1))
# # }
# # }else{
# # if(solveLambdaOld>Solvelambda1){
# # TotposInGridFin <- TotposInGridFin+1
# # dummyLambda <- numeric(length=(TotposInGridFin))
# # }else{
# # TotposInGridFin <- TotposInGridFin-1
# # dummyLambda <- numeric(length=(TotposInGridFin-1))
# # }
# # }
#
# # TotposInGrid <- floor(abs(TotposInGrid-TopposInGridIn)/2)+min(TotposInGrid,TopposInGridIn)
#
# mylist$TotposInGrid <- TotposInGridFin
# mylist$OldSolveLambda <- Solvelambda2
# mylist$exit <- FALSE
# mylist$Time <- Time
# mylist$IntensityProc <- IntensityProc
# return(mylist)
# #repeat
# }
#
# if(Solvelambda1 == 0){
# mylist <- list(InfTopposInGridInOld = min(TopposInGridIn,TotposInGridFin),
# supTopposInGridInOld = max(TopposInGridIn,TotposInGridFin))
# if(solveLambdaOld>=Solvelambda1){
# mylist$left <- TRUE
# mylist$solveLeft <- solveLeft
# mylist$solveRight <- Solvelambda1
# }else{
# mylist$left <- FALSE
# mylist$solveRight <- solveRight
# mylist$solveLeft <- Solvelambda1
# }
# # TotposInGrid <- floor(abs(TotposInGrid-TopposInGridIn)/2)+min(TotposInGrid,TopposInGridIn)
#
# mylist$TotposInGrid <- TotposInGridFin
# mylist$OldSolveLambda <- Solvelambda2
# mylist$exit <- TRUE
# # mylist$Time <- c(Time,my.env$t)
# mylist$Time <- c(Time,samp@grid[[1]][-1][mylist$TotposInGrid])
# mylist$IntensityProc<- c(IntensityProc,tail(dummyLambda,n=1L))
# return(mylist)
# }
# }
# compErrHazR <- function(TopposInGrid, simMod, Kern,
# samp, Model, my.env, ExprHaz,
# cost, Time){
# dummyLambda <- numeric(length=(TopposInGrid))
# if(length([email protected]@var.dx)==1){
# dN <- rep(0, TopposInGrid)
#
# con <- (samp@grid[[1]] %in% c(Time[-1],samp@grid[[1]][TopposInGrid]))
# dN[con[c(1:length(dN))]] <- as.numeric(simMod@[email protected][c(FALSE, con[-length(con)]),[email protected]@var.dx]
# -simMod@[email protected][con,[email protected]@var.dx])
# }else{}
# #for(i in c(1:TopposInGrid)){
# #MyPos
# MyPos <- sum(samp@grid[[1]]<=tail(Time,n=1L))
# #dummyLambda <- numeric(length=TopposInGrid)
# assign([email protected]@var.time, Time, envir = my.env)
# for(i in c(MyPos:TopposInGrid)){
# posInGrid <- i
# LastTime <- samp@grid[[1]][-1][(posInGrid)]
# #LastStime <- samp@grid[[1]][c(1:posInGrid)]
# assign([email protected], LastTime, envir = my.env)
# #assign([email protected]@var.time, LastStime, envir = my.env)
# #assign(paste0("d",[email protected]@var.dx), dN[c(1:posInGrid)], envir =my.env)
# assign(paste0("d",[email protected]@var.dx), 1, envir =my.env)
# dummyLambda[i] <- eval(ExprHaz[[1]], envir=my.env)
# }
# # solveLambda <- -log(cost)-sum(dummyLambda[c(sum(samp@grid[[1]]<=tail(Time,n=1L)):(TopposInGrid))])*samp@delta
# solveLambda <- -log(cost)-sum(dummyLambda[c(MyPos:(TopposInGrid))])*samp@delta
# res <- list(solveLambda = solveLambda, dummyLambda = tail(dummyLambda,n=1L))
# return(res)
# }
aux.simulatPPRROldVersion <- function(object, nsim = nsim, seed = seed,
xinit = xinit, true.parameter = true.parameter,
space.discretized = space.discretized, increment.W = increment.W,
increment.L = increment.L, method = method, hurst = hurst,
methodfGn = methodfGn, sampling = sampling,
subsampling = subsampling){
Time <- sampling@Terminal
numbVardx <- length(object@[email protected])
numbCountVar <- length(object@[email protected])
U <- runif(numbCountVar)
my.env<- new.env()
true.parameter <- unlist(true.parameter)
if(!all(names(true.parameter)==object@PPR@allparam)){
yuima.stop("true.parameters mismatch the model parameters")
}
for(i in c(1:length(object@PPR@allparam))){
assign(object@PPR@allparam[i],true.parameter[object@PPR@allparam[i]], envir = my.env)
}
assign("t",object@gFun@[email protected], envir = my.env)
nameu <- object@gFun@[email protected]
assign("dt",sampling@delta, envir = my.env)
if(is.null(increment.W)){
dimW <- length(object@model@diffusion[[1]])
W <- matrix(rnorm(dimW*sampling@n,mean=0,sd= sqrt(sampling@delta)),nrow=dimW,ncol=sampling@n)
}
Condcovariate <- TRUE
if(is.null(increment.L)){
dimL <- length(object@[email protected][[1]])
L <- matrix(0,nrow=dimL,ncol=sampling@n)
Condcovariate <- FALSE
# if(length(object@PPR@covariates)!=0)
# Condcovariate <- TRUE
cond <- !(object@[email protected] %in% object@[email protected])
if(any(cond)){
Condcovariate <- TRUE
}
dimMd <- length(object@[email protected])
dumMod <- setModel(drift = rep("0",dimMd),
diffusion = matrix("0",dimMd,1),
jump.coeff = diag("1",dimMd,dimMd),
measure = object@[email protected]$measure,
measure.type = object@[email protected]$type,
solve.variable = object@[email protected])
if(length(object@model@parameter@measure)!=0){
simMod <- simulate(object = dumMod,
true.parameter = true.parameter[object@model@parameter@measure],
sampling = sampling)
}else{
simMod <- simulate(object = dumMod,
sampling = sampling)
}
L <- t(diff(simMod@[email protected]))
}
assign("Condcovariate",Condcovariate, envir = my.env)
assign("W", W, envir = my.env)
rownames(L)<- object@[email protected]
assign("L", L, envir = my.env)
assign("All.labKern",object@[email protected],envir = my.env)
assign("All.labgFun",object@gFun@param,envir = my.env)
Fun1 <- function(u,env){
part <- seq(0,u,by=env$dt)
env$t<-part[-length(part)]
if(Condcovariate){
yuima<- object@model
for(i in c(1:length(object@PPR@covariates))){
assign(object@PPR@covariates[i],
eval(yuima@xinit[[email protected]==object@PPR@covariates[i]],
envir = env), envir = env)
}
if(u!=0){
# Mat<-matrix(0,length([email protected]),length(env$t)+1)
# for(i in c(1:length([email protected]))){
# Mat[i,1] = eval(yuima@xinit[i],envir = env)
# }
Linc <- env$L[,c(1:(length(part)-1))]
# Linc[[email protected]!=object@PPR@covariates,]<-matrix(0,
# sum([email protected]!=object@PPR@covariates), dim(Linc)[2])
Linc[[email protected]!=object@PPR@covariates,] <- 0
DumUnderlMod <- simulate(yuima, true.parameter = true.parameter,
increment.L = env$L[,c(1:(length(part)-1))],
sampling = setSampling(Terminal = u, n= (length(part)-1)))
for(i in c(1:length(object@PPR@covariates))){
VariableDum <- DumUnderlMod@[email protected][,[email protected]==object@PPR@covariates[i]]
assign(object@PPR@covariates[i], as.numeric(VariableDum), envir = env)
}
}
}
(log(env$U)+sum(eval(env$gFun,envir = env)*env$dt))^2
}
Fun2 <- function(u,env){
u <- max(env$old_u,u)
dumpart <- seq(0,env$old_u, by=env$dt)
part <- seq(env$old_u,u,by=env$dt)
t_k <- env$t
env$t<-part[-length(part)]
if(u>=sampling@Terminal){
# Think a better solution
my.env$utrue<-u
return(0)
}
if(Condcovariate){
LevIncr <- env$L[, length(dumpart)+c(1:(length(env$t)))]
LevIncr[object@[email protected],]<-0
yuima<- object@model
xinit<- numeric(length(object@PPR@covariates))
names(xinit)<- object@PPR@covariates
for(i in c(1:length(object@PPR@covariates))){
xinit[i] <- env[[object@PPR@covariates[i]]]
}
xinitCount <- numeric(length(object@[email protected]))
names(xinitCount) <- object@[email protected]
for(i in c(1:length(xinitCount))){
xinitCount[i] <- tail(env[[object@[email protected][i]]],n = 1)
}
xinit <- c(xinit,xinitCount)
if(part[length(part)]-part[1]!=0){
DumVarCov <- simulate(yuima,
true.parameter = true.parameter,
increment.L = LevIncr,
sampling = setSampling(Terminal = (part[length(part)]-part[1]),
n = dim(LevIncr)[2]),
xinit=xinit[[email protected]])
for(i in c(1:length(object@PPR@covariates))){
VariableDum <- DumVarCov@[email protected][,[email protected]==object@PPR@covariates[i]]
assign(object@PPR@covariates[i], as.numeric(VariableDum), envir = env)
}
}else{
for(i in c(1:length(object@PPR@covariates))){
VariableDum <- xinit[[email protected]==object@PPR@covariates[i]]
assign(object@PPR@covariates[i], as.numeric(VariableDum), envir = env)
}
}
#Insert Here simulation Covariate
}
integG <-sum(eval(env$gFun,envir = env)*env$dt)
env$s <- unique(c(env$s,t_k))[-length(env$s)]
dumt <- env$t
num <- length(env$Kern)
integKer <- 0
for(j in c(1:length(dumt))){
env$t <- dumt[j]
dumKernInt <- 0
for(i in c(1:num)){
lab.dx <- [email protected][i]
dumKernInt <- dumKernInt+sum(eval(env$Kern,envir=env)*diff(eval(env[[lab.dx]])))
}
integKer <- integKer + dumKernInt
}
NewTerm <- 0
if(env$Condcovariate){
## Insert Her
}
my.env$utrue<-u
(log(env$U)+ integG + integKer+NewTerm)^2
}
u <- numeric(length = numbCountVar)
names(u) <- object@[email protected]
for(i in c(1:numbCountVar)){
assign("gFun", object@gFun@formula[[i]], envir=my.env)
assign("U",runif(1),envir = my.env)
u[i]<- as.numeric(optim(0,Fun1,env=my.env)$par)
}
t_1 <- min(u)
if(t_1>Time){
yuima.stop("No jump occurs in the considered time interval.
Increasing Terminal in setSampling is suggested")
}
condt1<- u%in%t_1
namesContVarJump <- names(u[condt1])
JUMP <- matrix(0,nrow=numbCountVar,ncol=sampling@n)
rownames(JUMP)<- object@[email protected]
pos<-sum(sampling@grid[[1]][-1]<=t_1)
t_1 <- sampling@grid[[1]][-1][pos]
recordTime<-c(0,t_1)
pos0<-0
JUMP[namesContVarJump, pos] <- L[namesContVarJump, pos]
ntot <- sampling@n
dL <- L
dL[object@[email protected],c((pos0+1):pos)]<-JUMP[object@[email protected],c((pos0+1):pos)]
X_mat <- matrix(0, length(object@[email protected]),
ntot)
rownames(X_mat) <- object@[email protected]
dummyX <- simulate(object@model, true.parameter = true.parameter,
increment.W = if(is.matrix(W[,1:pos])){W[,1:pos]}else{t(as.matrix(W[,1:pos]))},
increment.L = if(is.matrix(dL[,1:pos])){dL[,1:pos]}else{t(as.matrix(dL[,1:pos]))},
sampling = setSampling(Terminal = t_1,
n = t_1/sampling@delta))
X_mat[,1:pos] <- t(dummyX@[email protected])[,-1]
t_jump <- t_1
if(length(object@[email protected]@var.dx)==1){
Comulat.dx <- apply(t(X_mat[object@[email protected]@var.dx,
c((pos0+1):pos)]), 1, diff)
}else{
Comulat.dx <- apply(t(X_mat[object@[email protected]@var.dx,
c((pos0+1):pos)]), 2, diff)
}
Index <- matrix(c(1:prod(object@Kernel@Integrand@dimIntegrand)),
nrow = object@Kernel@Integrand@dimIntegrand[1],
ncol = object@Kernel@Integrand@dimIntegrand[2])
assign(object@[email protected]@var.time,
sampling@grid[[1]][c((pos0+1):(pos))],
envir = my.env)
assign(object@gFun@[email protected], t_1, envir = my.env)
for(i in c(1:object@Kernel@Integrand@dimIntegrand[2])){
assign(object@[email protected]@var.dx[i],
as.numeric(Comulat.dx[,i]),
envir = my.env)
}
KernDum <- list()
for(i in c(1:object@Kernel@Integrand@dimIntegrand[1])){
dumKern <- expression()
for(j in c(1:object@Kernel@Integrand@dimIntegrand[2])){
id <- as.numeric(Index[i,j])
dumKern <- c(dumKern,object@Kernel@Integrand@IntegrandList[[id]])
}
KernDum[[i]] <- dumKern
}
udumm <- numeric(length = numbCountVar)
names(udumm) <- object@[email protected]@var.dx
assign("L",dL,envir = my.env)
pos0 <- pos
assign("pos0", pos, envir = my.env)
assign("old_u",t_1, envir = my.env)
while(t_jump<Time){
oldt_1<-t_1
for(i in c(1:numbCountVar)){
assign("gFun", object@gFun@formula[[i]], envir=my.env)
assign("Kern", KernDum[[i]], envir=my.env)
my.env$utrue<-0
while(my.env$utrue<oldt_1){
assign("U",runif(1),envir = my.env)
optim((t_1+2*my.env$dt),Fun2,method = "Nelder-Mead",
env=my.env)$par
u[i] <- as.numeric(my.env$utrue)
}
}
t_1 <- min(u)
condt1<- u%in%t_1
namesContVarJump <- names(u[condt1])
mypos<-sum(sampling@grid[[1]][-1]<=t_1)
if((pos0+1)<mypos){
pos<-sum(sampling@grid[[1]][-1]<=t_1)
t_jump<- t_1
t_1 <- sampling@grid[[1]][-1][pos]
recordTime<-c(recordTime,t_1)
#if(t_1!=sampling@Terminal){
pos <- min(pos,dim(L)[2])
JUMP[namesContVarJump, pos] <- L[namesContVarJump, pos]
dL[object@[email protected],c((pos0+1):pos)]<-JUMP[object@[email protected],c((pos0+1):pos)]
aa<-setSampling(Terminal = (t_1-my.env$old_u),
n = length((pos0+1):pos))
dummyX <- simulate(object@model, true.parameter = true.parameter,
increment.W = if(is.matrix(W[,(pos0+1):pos])){W[,(pos0+1):pos]}else{t(as.matrix(W[,(pos0+1):pos]))},
increment.L = if(is.matrix(dL[,(pos0+1):pos])){dL[,(pos0+1):pos]}else{t(as.matrix(dL[,(pos0+1):pos]))},
sampling = aa,
xinit=X_mat[,(pos0)])
X_mat[,(pos0+1):pos] <- t(dummyX@[email protected])[,-1]
if(length(object@[email protected]@var.dx)==1){
Comulat.dx <- apply(t(X_mat[object@[email protected]@var.dx,
c((pos0+1):pos)]), 1, diff)
}else{
Comulat.dx <- apply(t(X_mat[object@[email protected]@var.dx,
c((pos0+1):pos)]), 2, diff)
}
if(!is.matrix(Comulat.dx)){
Comulat.dx <-t(as.matrix(Comulat.dx))
}
Index <- matrix(c(1:prod(object@Kernel@Integrand@dimIntegrand)),
nrow = object@Kernel@Integrand@dimIntegrand[1],
ncol = object@Kernel@Integrand@dimIntegrand[2])
assign(object@[email protected]@var.time,
sampling@grid[[1]][c((pos0+1):(pos))],
envir = my.env)
assign(object@gFun@[email protected], t_1, envir = my.env)
for(i in c(1:object@Kernel@Integrand@dimIntegrand[2])){
assign(object@[email protected]@var.dx[i],
as.numeric(Comulat.dx[,i]),
envir = my.env)
}
pos0<-pos
assign("pos0", pos, envir = my.env)
assign("old_u",t_1, envir = my.env)
#}
}
assign("L",dL,envir = my.env)
}
X_mat[namesContVarJump,pos]<-X_mat[namesContVarJump,pos]
res.dum <- list(X_mat=X_mat,timeJump = recordTime, grid=sampling)
solve.variable <-unique(c(object@[email protected]))
N.VarPPR<-length(solve.variable)
dummy.mod <- setModel(drift=rep("0",N.VarPPR),
diffusion = NULL, jump.coeff = diag(rep("1",N.VarPPR)),
measure = object@[email protected]$measure,
measure.type = object@[email protected]$type,
solve.variable = solve.variable, xinit=c(object@model@xinit))
mynewincr <- if(is.matrix(res.dum$X_mat)){t(as.matrix(apply(cbind(0,res.dum$X_mat),1,diff)))}else{apply(cbind(0,res.dum$X_mat),1,diff)}
interResMod <- simulate(object = dummy.mod,
true.parameter = true.parameter,
sampling = sampling,
increment.L = mynewincr)
resGfun<-new("yuima.Map",
Output = object@gFun,
yuima=setYuima(model=dummy.mod,sampling = sampling))
interResGfun <- simulate(object = resGfun,
true.parameter = true.parameter,
sampling = sampling,
increment.L = mynewincr)
dummyObject <- object@Kernel
[email protected]@out.var <-object@[email protected]
resInt <- new("yuima.Integral",
Integral = dummyObject,
yuima = setYuima(model=dummy.mod,sampling = sampling))
interResInt <- simulate(object = resInt,
true.parameter = true.parameter,
sampling = sampling,
increment.L = mynewincr)
DataIntensity <- interResGfun@[email protected] + interResInt@[email protected]
InterMDia<-zoo(interResMod@[email protected], order.by = index(DataIntensity))
Alldata <-merge(InterMDia,DataIntensity)
colnames(Alldata)<-c(solve.variable,object@[email protected])
# for(i in c(1:N.VarPPR)){
# assign(solve.variable[i],interRes@[email protected][,i],envir=my.env)
# }
# dummy<-NULL
# for(t in c(1:length(object@[email protected]))){
# dummy <-eval(object@gFun)
# assign(object@[email protected][[]])
# }
object@data<-setData(Alldata)
return(object)
}
# simOzaki.aux<-function(gFun,a,cCoeff, Time, numJump){
# t_k<-0
# N<-0
# S<-1
#
# T_k<-c(t_k)
#
# N_k<-c(N)
# U<-runif(1)
# t_k <- -log(U)/gFun
# if(t_k<Time){
# T_k<-c(T_k,t_k)
# N<-N+numJump
# N_k<-c(N_k, N)
# }
# while(t_k<=Time){
# U<-runif(1)
# optim.env<-new.env()
# assign("U",U,envir=optim.env)
# assign("t_k",t_k,envir=optim.env)
# assign("c",cCoeff,envir=optim.env)
# assign("a",a,envir=optim.env)
# assign("S",S,envir=optim.env)
# assign("gFun",gFun,envir=optim.env)
#
# min<-function(u,env){
# U<-env$U
# t_k<-env$t_k
# c<-env$c
# a<-env$a
# S<-env$S
# gFun<-env$gFun
# y<-(log(U)+gFun*(u-t_k)+c/a*S*(1-exp(-a*(u-t_k))))^2
# }
# y<-optim(par=t_k,min, env=optim.env )$par
# S<- exp(-a*(y-t_k))*S+1
# t_k<-y
# T_k<-c(T_k,t_k)
# N<-N+numJump
# N_k<-c(N_k, N)
# }
# return(list(T_k=T_k,N_k=N_k))
# }
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simulateForPpr.R |
#
setMethod("simulate", "yuima.multimodel",
function(object, nsim=1, seed=NULL, xinit, true.parameter,
space.discretized=FALSE, increment.W=NULL, increment.L=NULL, method="euler",
hurst, methodfGn="WoodChan",
sampling, subsampling,
#Initial = 0, Terminal = 1, n = 100, delta,
# grid, random = FALSE, sdelta=as.numeric(NULL),
# sgrid=as.numeric(NULL), interpolation="none"
...){
tmpsamp <- NULL
if(missing(sampling)){
tmpsamp <- setSampling(...)
# tmpsamp <- setSampling(Initial = Initial, Terminal = Terminal, n = n,
# delta = delta, grid = grid, random = random, sdelta=sdelta,
# sgrid=sgrid, interpolation=interpolation)
} else {
tmpsamp <- sampling
}
tmpyuima <- setYuima(model=object, sampling=tmpsamp)
out <- simulate(tmpyuima, nsim=nsim, seed=seed, xinit=xinit,
true.parameter=true.parameter,
space.discretized=space.discretized,
increment.W=increment.W, increment.L=increment.L,
method=method,
hurst=hurst,methodfGn=methodfGn, subsampling=subsampling)
return(out)
})
aux.simulate.multimodel<-function(object, nsim, seed, xinit, true.parameter,
space.discretized, increment.W, increment.L,method,
hurst=0.5,methodfGn,
sampling, subsampling,
Initial, Terminal, n, delta,
grid, random, sdelta,
sgrid, interpolation){
##:: errors checks
if(is(object@model@measure$df,"yuima.law")&& is.null(increment.L)){
samp<- object@sampling
randomGenerator<-object@model@measure$df
if(samp@regular){
tForMeas<-samp@delta
NumbIncr<-samp@n[1]
if(missing(true.parameter)){
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}else{
measureparam<-true.parameter[object@model@parameter@measure]
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}
Noise<- rand(object = randomGenerator, n=NumbIncr, param=measureparam)
}else{
# Just For Irregular Grid
tForMeas<-diff(samp@grid[[1]]-samp@Initial)
my.InternalFunforLevy<-function(tForMeas,
randomGenerator,
true.parameter,object){
if(missing(true.parameter)){
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}else{
measureparam<-true.parameter[object@model@parameter@measure]
eval(parse(text= paste0("measureparam$",
object@[email protected]," <- tForMeas",collapse="")))
}
Noise<- rand(object = randomGenerator, n=samp@n[1], param=measureparam)
}
Noise<-sapply(X=tForMeas, FUN=my.InternalFunforLevy,
randomGenerator=randomGenerator,
true.parameter=true.parameter,
object=object)
}
increment.L=Noise
if(is(object@model@measure$df,"yuima.law")&&!is.null(increment.L)){
dummy<-object
dummy@model@measure$df <- expression()
if(missing(xinit)){
res<- aux.simulate.multimodel(object=dummy, nsim, xinit=object@model@xinit ,seed, true.parameter,
space.discretized, increment.W,
increment.L = t(increment.L), method, hurst, methodfGn,
sampling=object@sampling)
}else{
res<- aux.simulate.multimodel(object=dummy, nsim, xinit, seed, true.parameter,
space.discretized, increment.W,
increment.L = increment.L, method, hurst, methodfGn,
sampling=object@sampling)
}
res@model <- object@model
if(missing(subsampling))
return(res)
return(subsampling(res, subsampling))
}
}
##:1: error on yuima model
yuima <- object
samp <- object@sampling
if(missing(yuima)){
yuima.warn("yuima object is missing.")
return(NULL)
}
tmphurst<-yuima@model@hurst
if(!missing(hurst)){
yuima@model@hurst=hurst
}
if (is.na(yuima@model@hurst)){
yuima.warn("Specify the hurst parameter.")
return(NULL)
}
tmpsamp <- NULL
if(is.null(yuima@sampling)){
if(missing(sampling)){
tmpsamp <- setSampling(Initial = Initial,
Terminal = Terminal, n = n, delta = delta,
grid = grid, random = random, sdelta=sdelta,
sgrid=sgrid, interpolation=interpolation)
} else {
tmpsamp <- sampling
}
} else {
tmpsamp <- yuima@sampling
}
yuima@sampling <- tmpsamp
sdeModel <- yuima@model
Terminal <- yuima@sampling@Terminal[1]
n <- yuima@sampling@n[1]
r.size <- [email protected]
d.size <- [email protected]
##:2: error on xinit
if(missing(xinit)){
xinit <- sdeModel@xinit
} else {
if(length(xinit) != d.size){
if(length(xinit)==1){
xinit <- rep(xinit, d.size)
} else {
yuima.warn("Dimension of xinit variables missmatch.")
return(NULL)
}
}
}
xinit <- as.expression(xinit) # force xinit to be an expression
par.len <- length(sdeModel@parameter@all)
if(missing(true.parameter) & par.len>0){
true.parameter <- vector(par.len, mode="list")
for(i in 1:par.len)
true.parameter[[i]] <- 0
names(true.parameter) <- sdeModel@parameter@all
}
yuimaEnv <- new.env()
if(par.len>0){
for(i in 1:par.len){
pars <- sdeModel@parameter@all[i]
for(j in 1:length(true.parameter)){
if( is.na(match(pars, names(true.parameter)[j]))!=TRUE){
assign(sdeModel@parameter@all[i], true.parameter[[j]],yuimaEnv)
}
}
#assign(sdeModel@parameter@all[i], true.parameter[[i]], yuimaEnv)
}
}
if(space.discretized){
if(r.size>1){
warning("Space-discretized EM cannot be used for multi-dimentional models. Use standard method.")
space.discretized <- FALSE
}
if(length([email protected])){
warning("Space-discretized EM is for only Wiener Proc. Use standard method.")
space.discretized <- FALSE
}
}
##:: Error check for increment specified version.
if(!missing(increment.W) & !is.null(increment.W)){
if(space.discretized == TRUE){
yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
return(NULL)
}else if(dim(increment.W)[1] != r.size){
yuima.warn("Length of increment's row must be same as yuima@[email protected].")
return(NULL)
}else if(dim(increment.W)[2] != n){
yuima.warn("Length of increment's column must be same as sampling@n[1].")
return(NULL)
}
}
##:: Error check for increment specified version.
if(!missing(increment.L) & !is.null(increment.L)){
if(space.discretized == TRUE){
yuima.warn("Parameter increment must be invalid if space.discretized=TRUE.")
return(NULL)
}else if(dim(increment.L)[1] != length(yuima@[email protected][[1]]) ){ #r.size){
yuima.warn("Length of increment's row must be same as yuima@[email protected].")
return(NULL)
}else if(dim(increment.L)[2] != n){
yuima.warn("Length of increment's column must be same as sampling@n[1].")
return(NULL)
}
}
yuimaEnv$dL <- increment.L
if(space.discretized){
##:: using Space-discretized Euler-Maruyama method
yuima@data <- space.discretized(xinit, yuima, yuimaEnv)
yuima@model@hurst<-tmphurst
return(yuima)
}
##:: using Euler-Maruyama method
delta <- samp@delta
if(missing(increment.W) | is.null(increment.W)){
if( sdeModel@hurst!=0.5 ){
grid<-sampling2grid(yuima@sampling)
isregular<-yuima@sampling@regular
if((!isregular) || (methodfGn=="Cholesky")){
dW<-CholeskyfGn(grid, sdeModel@hurst,r.size)
yuima.warn("Cholesky method for simulating fGn has been used.")
} else {
dW<-WoodChanfGn(grid, sdeModel@hurst,r.size)
}
} else {
delta<-samp@delta
if(!is.Poisson(sdeModel)){ # if pure CP no need to setup dW
dW <- rnorm(n * r.size, 0, sqrt(delta))
dW <- matrix(dW, ncol=n, nrow=r.size,byrow=TRUE)
} else {
dW <- matrix(0,ncol=n,nrow=1) # maybe to be fixed
}
}
} else {
dW <- increment.W
}
# if(is.Poisson(sdeModel)){
# yuima@data <- simCP(xinit, yuima, yuimaEnv)
# } else {
# yuima@data <- euler(xinit, yuima, dW, yuimaEnv)
# }
if(is(sdeModel,"yuima.multimodel")&&!is(sdeModel@measure$df,"yuima.law")){
if(length([email protected])==1){
if([email protected]=="CP"){
intens <- as.character(sdeModel@measure$intensity)
dens <- as.character(sdeModel@measure$df$expr)
dumCP <- setPoisson(intensity = intens, df = dens,
dimension = length([email protected][[1]]))
dummSamp <- yuima@sampling
samp <- setSampling(Initial = dummSamp@Initial,
Terminal = dummSamp@Terminal,
n = dummSamp@n)
traj <- simulate(object = dumCP,
sampling = samp,
true.parameter = true.parameter)
Incr.levy <- diff(traj@[email protected][[1]])
if(length(traj@[email protected])>1){
for(i in c(2:length(traj@[email protected]))){
Incr.levy<-cbind(Incr.levy,diff(traj@[email protected][[i]]))
}
}
}else{
dummSamp <- yuima@sampling
samp <- setSampling(Initial = dummSamp@Initial,
Terminal = dummSamp@Terminal,
n = dummSamp@n)
xinitCode <- yuima@model@xinit
dimJumpCoeff <- length(yuima@[email protected][[1]])
dumjumpCoeff <- matrix(as.character(diag(rep(1,dimJumpCoeff))),dimJumpCoeff,dimJumpCoeff)
Dumsolve.variable<-paste0("MyLevyDum",c(1:dimJumpCoeff))
if(!is(sdeModel@measure$df,"yuima.law")){
LevyMod <- setMultiModel(drift=rep("0",dimJumpCoeff),
diffusion = NULL,
jump.coeff = dumjumpCoeff,
df = as.character(sdeModel@measure$df$expr),
measure.type = [email protected],
solve.variable = Dumsolve.variable)
}else{
LevyMod <- setModel(drift=rep("0",dimJumpCoeff),
diffusion = NULL,
jump.coeff = dumjumpCoeff,
measure = sdeModel@measure,
measure.type = [email protected],
solve.variable = Dumsolve.variable)
}
yuimaLevy <- setYuima(model=LevyMod, sampling = samp)
yuimaLevy@model@dimension <- dimJumpCoeff
traj<- simCode(xinit=xinitCode,yuima = yuimaLevy, env=yuimaEnv)
Incr.levy <- diff(traj@[email protected][[1]])
if(length(traj@[email protected])>1){
for(i in c(2:length(traj@[email protected]))){
Incr.levy<-cbind(Incr.levy,diff(traj@[email protected][[i]]))
}
}
#yuima.stop("code multivariate Levy will be implemented as soon as possible")
}
}else{
if(any([email protected]=="CP")){
intens <- as.character(sdeModel@measure$intensity)
dens <- as.character(sdeModel@measure$df$expr)
# If we consider independence between CP and the Other Levy
# we have:
numbLev <- length([email protected])
posCPindex <- c(1:numbLev)[[email protected]%in%"CP"]
CPmeasureComp <- paste0(dens,"[,c(",toString(posCPindex),")]")
intens <- as.character(sdeModel@measure$intensity)
dumCP <- setPoisson(intensity = intens, df = CPmeasureComp,
dimension = length(posCPindex))
# Simulation CP part
dummSamp <- yuima@sampling
samp <- setSampling(Initial = dummSamp@Initial,
Terminal = unique(dummSamp@Terminal),
n = unique(dummSamp@n))
trajCP <- simulate(object = dumCP, sampling = samp,
true.parameter = true.parameter)
dimJumpCoeff <- length(yuima@[email protected])
dumjumpCoeff <- matrix(as.character(diag(rep(1,dimJumpCoeff))),dimJumpCoeff,dimJumpCoeff)
Dumsolve.variable <- paste0("MyLevyDum",c(1:dimJumpCoeff))
dummy.measure.code <- as.character(sdeModel@measure$df$expr)
LevyMod <- setMultiModel(drift=rep("0",dimJumpCoeff),
diffusion = NULL,
jump.coeff = dumjumpCoeff,
df = dummy.measure.code,
measure.type = "code",
solve.variable = Dumsolve.variable)
yuimaLevy <- setYuima(model=LevyMod, sampling = samp)
yuimaLevy@model@dimension <- dimJumpCoeff
trajcode<- simCode(xinit=rep("0",length=dimJumpCoeff),
yuima = yuimaLevy, env=yuimaEnv)
countCP <- 0
countcode <- 0
if(yuima@[email protected][1]=="CP"){
Incr.levy <- as.matrix(as.numeric(diff(trajCP@[email protected][[1]])))
countcode <- countcode+1
}else{
if(yuima@[email protected][1]=="code"){
Incr.levy <- as.matrix(as.numeric(diff(trajcode@[email protected][[1]])))
countCP <- countCP+1
}
}
if(length(yuima@[email protected])>1){
for(i in c(2:length(yuima@[email protected]))){
if(yuima@[email protected][i]=="CP"){
Incr.levy<-cbind(Incr.levy,as.numeric(diff(trajCP@[email protected][[(i-countCP)]])))
countcode <- countcode+1
}else{
if(yuima@[email protected][i]=="code"){
Incr.levy <- cbind(Incr.levy,as.numeric(diff(trajcode@[email protected][[i]])))
countCP <- countCP+1
}
}
}
}
# yuima.stop("Levy with CP and/or code")
}
}
}
if(!is.null(increment.L))
Incr.levy<-t(increment.L)
assign("dL",t(Incr.levy),envir=yuimaEnv)
sim <- Multi.Euler(xinit,yuima,dW,env=yuimaEnv)
yuima@[email protected]<-as.list(numeric(length=length([email protected]))) #LM nov2016
# yuima@[email protected]<[email protected]
for(i in 1:length(yuima@[email protected])){
yuima@[email protected][[i]]<[email protected][[i]]
index(yuima@[email protected][[i]]) <- yuima@sampling@grid[[1]]
}## to be fixed
yuima@[email protected] <- [email protected]
yuima@model@xinit <- xinit
yuima@model@hurst <-tmphurst
if(missing(subsampling))
return(yuima)
subsampling(yuima, subsampling)
}
simCode <- function(xinit,yuima,env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
Terminal <- yuima@sampling@Terminal[1]
Initial <- yuima@sampling@Initial[1]
dimension <- yuima@model@dimension
dummy.val <- numeric(dimension)
if(length(xinit) != dimension)
xinit <- rep(xinit, dimension)[1:dimension]
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],envir=env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, envir=env)
if(length(dummy.val)==1){
dummy.val<-rep(dummy.val,dimension)
}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,envir=env)
}
} else {
for(i in 1:dimension){
dummy.val[i] <- eval(xinit[i], envir=env)
}
}
### Simulation of CP using Lewis' method
##:: Levy
JP <- eval([email protected][[1]], envir=env)
mu.size <- length(JP)
# print(str(JP))
#assign(sdeModel@measure$intensity, env) ## intensity param
# .CPintensity <- function(.t) {
# assign(modeltime, .t, envir=env)
# eval(sdeModel@measure$intensity, envir=env)
# }
dummyList<-as.list(env)
lgth.meas<-length(yuima@model@parameter@measure)
if(lgth.meas>1){
for(i in c(2:lgth.meas)){
idx.dummy<-yuima@model@parameter@measure[i]
assign(idx.dummy,as.numeric(dummyList[idx.dummy]))
}
}
# we use Lewis' acceptance/rejection method
#if(grep("^[dexp|dnorm|dgamma|dconst]", sdeModel@measure$df$expr)){
##:: e.g. dnorm(z,1,1) -> rnorm(mu.size*N_sharp,1,1)
#gsub("^d(.+?)\\(.+?,", "r\\1(mu.size*N_sharp,", sdeModel@measure$df$expr, perl=TRUE)
#} else{
#stop("Sorry. CP only supports dconst, dexp, dnorm and dgamma yet.")
#}
if(!is(sdeModel@measure$df,"yuima.law")){
dumStringMeas <- toString(sdeModel@measure$df$expr)
dumStringMeas1 <- substr(x=dumStringMeas, start=2,stop=nchar(x = dumStringMeas))
dumStringMeas2 <- paste0("r",dumStringMeas1)
tmpMeas2 <- strsplit(x=dumStringMeas2,split="")
posMeas2 <- match("(" , tmpMeas2[[1]])[1]
dumStringMeas3 <- substr(x=dumStringMeas2, start=1,stop=(posMeas2-1))
a<-deparse(args(eval(parse(text=dumStringMeas3))))[1]
b<-gsub("^function (.+?)","(",a)
b1 <- substr(x=b,start =1, stop=(nchar(b)-1))
FinalMeasRandn<-paste0(dumStringMeas3,b1)
dummyvarMaes <- all.vars(parse(text=FinalMeasRandn))
posDum<- match(c([email protected],sdeModel@parameter@measure),dummyvarMaes)
if(length(posDum)+1!=length(dummyvarMaes)){
yuima.stop("too input variables in the random number function")
}
deltaVar <- dummyvarMaes[-posDum]
# ell <- optimize(f=.CPintensity, interval=c(Initial, Terminal), maximum = TRUE)$objective
# ellMax <- ell * 1.01
F <- suppressWarnings(parse(text=gsub("^r(.+?)\\(.+?,", "r\\1(mu.size*N_sharp,", parse(text=FinalMeasRandn), perl=TRUE)))
F.env <- new.env(parent=env)
N_sharp <- unique(yuima@sampling@n)
TrueDelta <- unique(yuima@sampling@delta)
assign(deltaVar, TrueDelta, envir=F.env)
assign("mu.size", mu.size, envir=F.env)
assign("N_sharp", N_sharp, envir=F.env)
randJ <- eval(F, envir=F.env) ## this expression is evaluated in the F.env
randJ <- rbind(dummy.val, randJ)
}else{
TrueDelta <- unique(yuima@sampling@delta)
randJ<- env$dL
}
RANDLevy <- apply(randJ,2,cumsum)
tsX <- zoo(x=RANDLevy)
yuimaData <- setYuima(data=setData(tsX, delta=TrueDelta))
#yuimaData <- subsampling(yuimaData, sampling=yuima@sampling)
return(yuimaData)
}
Multi.Euler<-function(xinit,yuima,dW,env){
sdeModel<-yuima@model
modelstate <- [email protected]
modeltime <- [email protected]
V0 <- sdeModel@drift
V <- sdeModel@diffusion
r.size <- [email protected]
d.size <- [email protected]
Terminal <- yuima@sampling@Terminal[1]
n <- yuima@sampling@n[1]
dL <- env$dL
# dX <- xinit
# 06/11 xinit is an expression: the structure is equal to that of V0
if(length(unique(as.character(xinit)))==1 &&
is.numeric(tryCatch(eval(xinit[1],env),error=function(...) FALSE))){
dX_dummy<-xinit[1]
dummy.val<-eval(dX_dummy, env)
if(length(dummy.val)==1){dummy.val<-rep(dummy.val,length(xinit))}
for(i in 1:length(modelstate)){
assign(modelstate[i],dummy.val[i] ,env)
}
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(xinit)){
dX[i] <- dummy.val[i]
}
}else{
dX_dummy <- xinit
if(length(modelstate)==length(dX_dummy)){
for(i in 1:length(modelstate)) {
if(is.numeric(tryCatch(eval(dX_dummy[i],env),error=function(...) FALSE))){
assign(modelstate[i], eval(dX_dummy[i], env),env)
}else{
assign(modelstate[i], 0, env)
}
}
}else{
yuima.warn("the number of model states do not match the number of initial conditions")
return(NULL)
}
# 06/11 we need a initial variable for X_0
dX<-vector(mode="numeric",length(dX_dummy))
for(i in 1:length(dX_dummy)){
dX[i] <- eval(dX_dummy[i], env)
}
}
##:: set time step
delta <- Terminal/n
##:: check if DRIFT and/or DIFFUSION has values
has.drift <- sum(as.character(sdeModel@drift) != "(0)")
var.in.diff <- is.logical(any(match(unlist(lapply(sdeModel@diffusion, all.vars)), [email protected])))
#print(is.Poisson(sdeModel))
##:: function to calculate coefficients of dW(including drift term)
##:: common used in Wiener and CP
p.b <- function(t, X=numeric(d.size)){
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
##:: solve diffusion term
if(has.drift){
tmp <- matrix(0, d.size, r.size+1)
for(i in 1:d.size){
tmp[i,1] <- eval(V0[i], env)
for(j in 1:r.size){
tmp[i,j+1] <- eval(V[[i]][j],env)
}
}
} else { ##:: no drift term (faster)
tmp <- matrix(0, d.size, r.size)
if(!is.Poisson(sdeModel)){ # we do not need to evaluate diffusion
for(i in 1:d.size){
for(j in 1:r.size){
tmp[i,j] <- eval(V[[i]][j],env)
} # for j
} # foh i
} # !is.Poisson
} # else
return(tmp)
}
X_mat <- matrix(0, d.size, n+1)
X_mat[,1] <- dX
if(has.drift){ ##:: consider drift term to be one of the diffusion term(dW=1)
dW <- rbind( rep(1, n)*delta , dW)
}
if(!length([email protected])){ ##:: Wiener Proc
##:: using Euler-Maruyama method
if(var.in.diff & (!is.Poisson(sdeModel))){ ##:: diffusions have state variables and it is not Poisson
##:: calcurate difference eq.
for( i in 1:n){
dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i]
X_mat[,i+1] <- dX
}
}else{ ##:: diffusions have no state variables (not use p.b(). faster)
sde.tics <- seq(0, Terminal, length=(n+1))
sde.tics <- sde.tics[2:length(sde.tics)]
X_mat[, 1] <- dX
##:: assign names of variables
for(i in 1:length(modelstate)){
assign(modelstate[i], dX[i])
}
assign(modeltime, sde.tics)
t.size <- length(sde.tics)
##:: solve diffusion term
if(has.drift){
pbdata <- matrix(0, d.size*(r.size+1), t.size)
for(i in 1:d.size){
pbdata[(i-1)*(r.size+1)+1, ] <- eval(V0[i], env)
for(j in 1:r.size){
pbdata[(i-1)*(r.size+1)+j+1, ] <- eval(V[[i]][j], env)
}
}
dim(pbdata)<-(c(r.size+1, d.size*t.size))
}else{
pbdata <- matrix(0, d.size*r.size, t.size)
if(!is.Poisson(sdeModel)){
for(i in 1:d.size){
for(j in 1:r.size){
pbdata[(i-1)*r.size+j, ] <- eval(V[[i]][j], env)
} # for j
} # for i
} # !is.Poisson
dim(pbdata)<-(c(r.size, d.size*t.size))
} # else
pbdata <- t(pbdata)
##:: calcurate difference eq.
for( i in 1:n){
if(!is.Poisson(sdeModel))
dX <- dX + pbdata[((i-1)*d.size+1):(i*d.size), ] %*% dW[, i]
X_mat[, i+1] <- dX
}
}
tsX <- ts(data=t(X_mat), deltat=delta , start=0)
}else{ ##:: Levy
JP <- [email protected]
mu.size <- length(JP)
# cat("\n Levy\n")
##:: function to solve c(x,z)
p.b.j <- function(t, X=numeric(d.size)){
for(i in 1:length(modelstate)){
assign(modelstate[i], X[i], env)
}
assign(modeltime, t, env)
# tmp <- numeric(d.size)
j.size <- length(JP[[1]])
tmp <- matrix(0, mu.size, j.size)
# cat("\n inside\n")
#print(dim(tmp))
for(i in 1:mu.size){
for(j in 1:j.size){
tmp[i,j] <- eval(JP[[i]][j],env)
}
# tmp[i] <- eval(JP[i], env)
}
return(tmp)
}
# print(ls(env))
### WHY I AM DOING THIS?
# tmp <- matrix(0, d.size, r.size)
#
#for(i in 1:d.size){
# for(j in 1:r.size){
# cat("\n here\n")
# tmp[i,j] <- eval(V[[i]][j],env)
# } # for j
# }
###
# if([email protected] == "CP" ){ ##:: Compound-Poisson type
#
# ##:: delete 2010/09/13 for simulate func bug fix by s.h
# ## eta0 <- eval(sdeModel@measure$intensity)
#
# ##:: add 2010/09/13 for simulate func bug fix by s.h
# eta0 <- eval(sdeModel@measure$intensity, env) ## intensity param
#
# ##:: get lambda from nu()
# tmp.expr <- function(my.x){
# assign([email protected],my.x)
# return(eval(sdeModel@measure$df$expr))
# }
# #lambda <- integrate(sdeModel@measure$df$func, 0, Inf)$value * eta0
# #lambda <- integrate(tmp.expr, 0, Inf)$value * eta0 ##bug:2013/10/28
#
# dummyList<-as.list(env)
# # print(str(dummyList))
# #print(str(idx.dummy))
# lgth.meas<-length(yuima@model@parameter@measure)
# if(lgth.meas>1){
# for(i in c(2:lgth.meas)){
# idx.dummy<-yuima@model@parameter@measure[i]
# #print(i)
# #print(yuima@model@parameter@measure[i])
# assign(idx.dummy,as.numeric(dummyList[idx.dummy]))
# }
# }
#
#
# #lambda <- integrate(tmp.expr, -Inf, Inf)$value * eta0
#
# ##:: lambda = nu() (p6)
# N_sharp <- rpois(1,Terminal*eta0) ##:: Po(Ne)
# if(N_sharp == 0){
# JAMP <- FALSE
# }else{
# JAMP <- TRUE
# Uj <- sort( runif(N_sharp, 0, Terminal) )
# ij <- NULL
# for(i in 1:length(Uj)){
# Min <- min(which(c(1:n)*delta > Uj[i]))
# ij <- c(ij, Min)
# }
# }
#
# ##:: make expression to create iid rand J
# if(grep("^[dexp|dnorm|dgamma|dconst]", sdeModel@measure$df$expr)){
# ##:: e.g. dnorm(z,1,1) -> rnorm(mu.size*N_sharp,1,1)
# F <- suppressWarnings(parse(text=gsub("^d(.+?)\\(.+?,", "r\\1(mu.size*N_sharp,", sdeModel@measure$df$expr, perl=TRUE)))
# }else{
# stop("Sorry. CP only supports dconst, dexp, dnorm and dgamma yet.")
# }
#
# ##:: delete 2010/09/13 for simulate func bug fix by s.h
# ## randJ <- eval(F) ## this expression is evaluated locally not in the yuimaEnv
#
# ##:: add 2010/09/13 for simulate func bug fix by s.h
# F.env <- new.env(parent=env)
# assign("mu.size", mu.size, envir=F.env)
# assign("N_sharp", N_sharp, envir=F.env)
#
# randJ <- eval(F, F.env) ## this expression is evaluated in the F.env
#
# j <- 1
# for(i in 1:n){
# if(JAMP==FALSE || sum(i==ij)==0){
# Pi <- 0
# }else{
# if(is.null(dL)){
# J <- eta0*randJ[j]/lambda
# j <- j+1
# ##cat(paste(J,"\n"))
# ##Pi <- zeta(dX, J)
# assign([email protected], J, env)
#
# if([email protected]){
# J <- 1
# }
#
# Pi <- p.b.j(t=i*delta,X=dX) * J
# }else{# we add this part since we allow the user to specify the increment of CP LM 05/02/2015
# Pi <- p.b.j(t=i*delta,X=dX) %*% dL[, i]
# }
# ##Pi <- p.b.j(t=i*delta, X=dX)
# }
# dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i] + Pi
# X_mat[, i+1] <- dX
# }
# tsX <- ts(data=t(X_mat), deltat=delta, start=0)
# ##::end CP
# }else if([email protected]=="code"){ ##:: code type
# ##:: Jump terms
# code <- suppressWarnings(sub("^(.+?)\\(.+", "\\1", sdeModel@measure$df$expr, perl=TRUE))
# args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", sdeModel@measure$df$expr, perl=TRUE)), ","))
# #print(args)
# dZ <- switch(code,
# rNIG=paste("rNIG(n, ", args[2], ", ", args[3], ", ", args[4], "*delta, ", args[5], "*delta, ", args[6],")"),
# rIG=paste("rIG(n,", args[2], "*delta, ", args[3], ")"),
# rgamma=paste("rgamma(n, ", args[2], "*delta, ", args[3], ")"),
# rbgamma=paste("rbgamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], "*delta, ", args[5], ")"),
# ## rngamma=paste("rngamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], ", ", args[5], "*delta, ", args[6], ")"),
# rngamma=paste("rngamma(n, ", args[2], "*delta, ", args[3], ", ", args[4], ", ", args[5], "*delta,", args[6],")"),
# ## rstable=paste("rstable(n, ", args[2], ", ", args[3], ", ", args[4], ", ", args[5], ", ", args[6], ")")
# rstable=paste("rstable(n, ", args[2], ", ", args[3], ", ", args[4], "*delta^(1/",args[2],"), ", args[5], "*delta)")
# )
# dummyList<-as.list(env)
# #print(str(dummyList))
# lgth.meas<-length(yuima@model@parameter@measure)
# #print(lgth.meas)
# if(lgth.meas!=0){
# for(i in c(1:lgth.meas)){
# #print(i)
# #print(yuima@model@parameter@measure[i])
# idx.dummy<-yuima@model@parameter@measure[i]
# #print(str(idx.dummy))
# assign(idx.dummy,dummyList[[idx.dummy]])
# #print(str(idx.dummy))
# #print(str(dummyList[[idx.dummy]]))
# #print(get(idx.dummy))
# }
# }
# if(is.null(dZ)){ ##:: "otherwise"
# cat(paste("Code \"", code, "\" not supported yet.\n", sep=""))
# return(NULL)
# }
# if(!is.null(dL))
dZ <- dL
# else
# dZ <- eval(parse(text=dZ))
##:: calcurate difference eq.
#print(str(dZ))
# if(is.null(dim(dZ)))
# dZ <- matrix(dZ,nrow=1)
# print(dim(dZ))
# print(str([email protected]))
for(i in 1:n){
assign([email protected], dZ[,i], env)
if([email protected]){
dZ[,i] <- 1
}
# cat("\np.b.j call\n")
tmp.j <- p.b.j(t=i*delta, X=dX)
#print(str(tmp.j))
#cat("\np.b.j cback and dZ\n")
# print(str(dZ[,i]))
# print(sum(dim(tmp.j)))
#print(str(tmp.j))
#print(str(p.b(t = i * delta, X = dX) %*% dW[, i]))
dX <- dX + p.b(t=i*delta, X=dX) %*% dW[, i] +tmp.j %*% dZ[,i]
X_mat[, i+1] <- dX
}
tsX <- ts(data=t(X_mat), deltat=delta, start=0)
##::end code
# }else{
# cat(paste("Type \"", [email protected], "\" not supported yet.\n", sep=""))
# return(NULL)
# }
}##::end levy
yuimaData <- setData(original.data=tsX)
return(yuimaData)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/simulateMultiProcess.R |
# library(KernSmooth)
setMethod("show", "yuima.snr",
function (object){
cat("\nCall:\n")
print(object@call)
cat("\nCoefficients:\n")
print(object@coef)
}
)
snr<-function(yuima,start,lower,upper,withdrift=FALSE){
if(yuima@[email protected] == 1){
data <- get.zoo.data(yuima)
s.size<-yuima@sampling@n
modelstate<-yuima@[email protected]
modeltime<-yuima@[email protected]
DRIFT<-yuima@model@drift
DIFFUSION<-yuima@model@diffusion
PARLENGS<-length(yuima@model@parameter@drift)+length(yuima@model@parameter@diffusion)
XINIT<-yuima@model@xinit
X<-as.numeric(data[[1]])
dX <- abs(diff(X))
odX <- sort(dX, decreasing=TRUE)
plot(yuima,main="Original path")
abline(0,0,lty=5)
pX<-X[1:(s.size-1)]
sY<-as.numeric(data[[1]])
derDIFFUSION<-D(DIFFUSION[[1]],modelstate)
tmp.env<-new.env()
inc<-double(s.size-1)
inc<-X[2:(s.size)]-pX
preservedinc<-inc
ainc<-abs(inc)
oinc<-sort(ainc,decreasing=TRUE)
yuima@[email protected] <- list()
yuima@model@measure <- list()
yuima@[email protected] <- character(0)
yuima@[email protected] <- character(0)
yuima@model@parameter@jump <- character(0)
yuima@model@parameter@measure <- character(0)
#yuima@model@drift<-expression((0))
qmle<-qmle(yuima, start = start, lower = lower, upper = upper) # initial estimation
parameter<-yuima@model@parameter@all
mp<-match(names(qmle@coef),parameter)
esort <- qmle@coef[order(mp)]
for(i in 1:length(qmle@coef))
{
assign(parameter[i],esort[[i]],envir=tmp.env)
}
resi<-double(s.size-1)
derv<-double(s.size-1)
total<-c()
j.size<-c()
assign(modeltime,yuima@sampling@delta,envir=tmp.env)
h<-yuima@sampling@delta
assign(modelstate,pX,envir=tmp.env)
diff.term<-eval(DIFFUSION[[1]],envir=tmp.env)
drif.term<-eval(DRIFT,envir=tmp.env)
if(length(diff.term)==1){
diff.term <- rep(diff.term, s.size)
}
if(length(drif.term)==1){
drif.term <- rep(drif.term, s.size)
} # vectorization (note. if an expression type object does not include state.variable, the length of the item after "eval" operation is 1.)
for(s in 1:(s.size-1)){
nova<-sqrt((diff.term)^2) # normalized variance
resi[s]<-(1/(nova[s]*sqrt(h)))*(inc[s]-h*withdrift*drif.term[s])
}
# assign(modelstate,pX,envir=tmp.env)
# derv<-eval(derDIFFUSION,envir=tmp.env)
# if(length(derv)==1){
# derv<-rep(derv,s.size) # vectorization
# }
mresi<-mean(resi)
vresi<-mean((resi-mresi)^2)
snr<-(resi-mresi)/sqrt(vresi)
plot(snr,main="Raw self-normalized residual")
# h <- dpih(snr)
# bins <- seq(min(snr)-0.1, max(snr)+0.1+h, by=h)
# hist(snr, prob=1, breaks=bins,xlim=c(-3,3),ylim=c(0,1))
hist(snr, freq = FALSE, breaks = "freedman-diaconis", xlim=c(min(snr),max(snr)),ylim=c(0,1))
xval <- seq(min(snr),max(snr),0.01) # to add plot the corresponding density
lines(xval,dnorm(xval,0,1),xlim=c(min(snr),max(snr)),ylim=c(0,1),col="red",main="Jump removed self-normalized residual")
call <- match.call()
final_res <- new("yuima.snr", call = call, coef = qmle@coef[order(mp)], snr = snr, model = yuima@model)
return(final_res)
}else{
yuima.stop("This function currently works only for one-dimensional case.")
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/snr.R |
## Local Method of Moments estimator
lmm <- function(x, block = 20, freq = 50, freq.p = 10, K = 4, interval = c(0, 1),
Sigma.p = NULL, noise.var = "AMZ", samp.adj = "direct", psd = TRUE){
Data <- get.zoo.data(x)
d.size <- length(Data)
N <- sapply(Data,"length") - 1
n <- min(N)
interval <- as.numeric(interval)
freq.p <- min(freq.p,freq)
if(d.size > 1){
# Constructing the stpectral statistics and noise level estimates
Spec <- array(0,dim=c(d.size,block,freq))
H <- matrix(0,d.size,block)
cH <- array(0,dim=c(d.size^2,block,freq))
zmat <- diag(d.size^2)
phi.br <- (block*pi*(1:freq))^2
for(p in 1:d.size){
dY <- diff(as.numeric(Data[[p]]))
Time <- (as.numeric(time(Data[[p]])) - interval[1])/(interval[2] - interval[1])
if((min(Time) < 0) || (max(Time) > 1)){
print("lmm:invalid interval")
return(NULL)
}
Time2 <- (Time[1:N[p]]+Time[-1])/2
k.idx <- findInterval(Time2,vec=seq(0,1,by=1/block))
# Noise variance
if(is.numeric(noise.var)){
sq.eta <- noise.var[p]
}else{
sq.eta <- switch(noise.var,
"BR" = mean(dY^2)/2, # Bandi and Russel (2006)
"O" = -sum(dY[-N[p]] * dY[-1])/N[p], # Oomen (2006)
"AMZ" = {tmp <- arima0(dY,order=c(0,0,1),include.mean=FALSE);
-tmp$coef * tmp$sigma2} # Ait-Sahalia et al. (2005)
)
}
# Effect of irregularity
if(is.numeric(samp.adj)){
EOI <- samp.adj[p, ]
}else{
EOI <- switch(samp.adj,
"QVT" = block * rowsum(diff(Time)^2,group = findInterval(Time[-1],vec=seq(0,1,by=1/block),
rightmost.closed = TRUE)),
"direct" = block * rowsum((diff(Time,lag=2)/2)^2, group = k.idx[-N[p]]))
}
Spec[p,,] <- sqrt(2*block)*
rowsum(sin(tcrossprod(pi*(block*Time2-(k.idx-1)),1:freq))*dY,
group = k.idx)
H[p, ] <- sq.eta * EOI
cH[d.size*(p-1)+p,,] <- tcrossprod(H[p,],phi.br)
for(q in 1:d.size){
tmp.mat <- diag(0, d.size)
tmp.mat[p, q] <- 1
zmat <- zmat + tmp.mat %x% t(tmp.mat)
}
}
#summand <- apply(Spec,c(2,3),FUN="tcrossprod") - cH
summand <- array(.C("krprod",
as.double(Spec),
as.integer(d.size),
as.integer(block*freq),
result=double(d.size^2*block*freq))$result,
dim=c(d.size^2,block,freq)) - cH
if(is.null(Sigma.p)){
# Pilot estimation of Sigma
# Using K adjacent blocks following Bibinger et al. (2014a)
#Sigma.p <- rollmean(t(rowMeans(array(summand[,,1:freq.p],
# dim=c(d.size^2,block,freq.p)),
# dims = 2)),k = K, fill = "e")
# Recent version of Bibinger et al. (2014b)
Sigma.p <- rollapply(t(rowMeans(array(summand[,,1:freq.p],
dim=c(d.size^2,block,freq.p)),
dims = 2)), width = 2*K + 1,
FUN="mean", partial = TRUE)
}
# Local method of moments
Sigma <- matrix(0,d.size^2,block)
inv.Ik <- array(0,dim=c(d.size^2,d.size^2,block))
for(k in 1:block){
tmp <- double(d.size^2)
H.inv <- diag(1/H[,k])
tmp.eigen <- eigen(matrix(Sigma.p[k,], d.size, d.size) %*% H.inv,
symmetric=FALSE)
inv.V <- solve(tmp.eigen$vectors)
iHV <- H.inv %*% tmp.eigen$vectors
iHVxiHV <- iHV %x% iHV
inv.VxV <- inv.V %x% inv.V
#Lambda <- apply(1/outer(tmp.eigen$values, phi.br, FUN="+"),
# 2,FUN="tcrossprod")
Lambda <- matrix(.C("krprod",
as.double(1/outer(tmp.eigen$values, phi.br, FUN="+")),
as.integer(d.size),
as.integer(freq),
result=double(d.size^2*freq))$result,d.size^2,freq)
#Lambda <- KhatriRao(1/outer(tmp.eigen$values, phi.br, FUN="+"))
inv.Ik[,,k] <- solve(iHVxiHV %*% diag(rowSums(Lambda)) %*% inv.VxV)
Sigma[,k] <- inv.Ik[,,k] %*% iHVxiHV %*%
rowSums(Lambda * inv.VxV %*% summand[,k,])
}
cmat <- matrix(rowMeans(Sigma), d.size, d.size)
vcov <- rowMeans(inv.Ik, dims = 2) %*% zmat/block
if(psd){
r <- eigen(cmat, symmetric = TRUE)
cmat <- r$vectors %*% abs(diag(r$values)) %*% t(r$vectors)
r <- eigen(vcov, symmetric = TRUE)
vcov <- r$vectors %*% abs(diag(r$values)) %*% t(r$vectors)
}
result <- list(covmat = cmat, vcov = vcov, Sigma.p = Sigma.p)
class(result) <- "yuima.specv"
}else{
# Constructing the stpectral statistics and noise level estimates
dY <- diff(as.numeric(Data[[1]]))
Time <- (as.numeric(time(Data[[1]])) - interval[1])/(interval[2] - interval[1])
if((min(Time) < 0) || (max(Time) > 1)){
print("lmm:invalid interval")
return(NULL)
}
Time2 <- (Time[1:N]+Time[-1])/2
k.idx <- findInterval(Time2,vec=seq(0,1,by=1/block))
# Noise variance
if(is.numeric(noise.var)){
sq.eta <- noise.var
}else{
sq.eta <- switch(noise.var,
"BR" = mean(dY^2)/2, # Bandi and Russel (2006)
"O" = -sum(dY[-N] * dY[-1])/N, # Oomen (2006)
"AMZ" = {tmp <- arima0(dY,order=c(0,0,1),include.mean=FALSE);
-tmp$coef * tmp$sigma2} # Ait-Sahalia et al. (2005)
)
}
phi.br <- (block*pi*(1:freq))^2
# Effect of irregularity
if(is.numeric(samp.adj)){
EOI <- samp.adj
}else{
EOI <- switch(samp.adj,
"QVT" = block * rowsum(diff(Time)^2,group = findInterval(Time[-1],vec=seq(0,1,by=1/block),
rightmost.closed = TRUE)),
"direct" = block * rowsum((diff(Time,lag=2)/2)^2, group = k.idx[-N]))
}
Spec <- sqrt(2*block)*
rowsum(sin(tcrossprod(pi*(block*Time2-(k.idx-1)),1:freq))*dY,
group = k.idx)
H <- sq.eta * EOI
cH <- tcrossprod(H,phi.br)
summand <- Spec^2 - cH
if(is.null(Sigma.p)){
# Pilot estimation of Sigma
# Using K adjacent blocks following Bibinger et al. (2014a)
#Sigma.p <- rollmean(rowMeans(matrix(summand[,1:freq.p],block,freq.p)),
# k = K, fill = "e")
# Recent version of Bibinger et al. (2014b)
Sigma.p <- rollapply(rowMeans(matrix(summand[,1:freq.p], block,freq.p)),
width = 2*K + 1, FUN="mean", partial = TRUE)
}
Ijk <- 1/(Sigma.p + cH)^2
inv.Ik <- 1/rowSums(Ijk)
if(psd){
cmat <- abs(sum(Ijk*inv.Ik*summand)/block)
}else{
cmat <- sum(Ijk*inv.Ik*summand)/block
}
result <- list(covmat = as.matrix(cmat), vcov = as.matrix(2 * mean(inv.Ik)/block), Sigma.p = Sigma.p)
class(result) <- "yuima.specv"
}
return(result)
}
# print method for yuima.specv-class
print.yuima.specv <- function(x, ...){
cat("Estimated covariance matrix\n")
print(x$covmat, ...)
cat("Standard Error\n")
print(matrix(sqrt(diag(x$vcov)), ncol = ncol(x$covmat)), ...)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/spectralcov.R |
##:: function subsampling
##:: takes any yuima object with data or a yuima.data object and
##:: performs subsampling according to some method
# poisson.random.sampling
# returns sample of data using poisson sampling
setGeneric("subsampling",
function(x, sampling, ...)
standardGeneric("subsampling")
)
setMethod("subsampling","yuima",
function(x, sampling, ...){
obj <- NULL
if(missing(sampling)){
obj <- subsampling(x@data, setSampling(...))
} else {
obj <- subsampling(x@data, sampling=sampling)
}
obj@model <- x@model
return(obj)
}
)
setMethod("subsampling", "yuima.data",
function(x, sampling=sampling){
#tmpsamp <- NULL
#if(missing(sampling)){
# tmpsamp <- setSampling(Initial = Initial, Terminal = Terminal,
# delta = delta, grid = grid, random = random, sdelta=sdelta,
# sgrid=sgrid, interpolation=interpolation)
#} else {
tmpsamp <- sampling
#}
Data <- get.zoo.data(x)
n.data <- length(Data)
tmpgrid <- vector(n.data, mode="list")
tmpsamp@grid <- rep(tmpsamp@grid, n.data)[1:n.data]
# prepares a grid of times
if(is.logical(tmpsamp@random)){
if(tmpsamp@random)
stop("wrong random sampling specification")
if(length(tmpsamp@delta) < n.data)
tmpsamp@delta <- rep( tmpsamp@delta, n.data)[1:n.data]
for(i in 1:n.data){
tmpgrid[[i]] <- tmpsamp@grid[[i]] #seq(start(Data[[i]]), end(Data[[i]]), by=tmpsamp@delta[i])
tmpsamp@regular[i] <- TRUE
tmpsamp@random[i] <- FALSE
}
}
# random sampling
if(is.list(tmpsamp@random)){
rdist <- c(tmpsamp@random$rdist)
if(is.null(rdist))
stop("provide at least `rdist' argument for random sampling")
n.rdist <- length(rdist)
r.gen <- rep( rdist, n.data) # eventually reciclying arguments
r.gen <- r.gen[1:n.data]
for(i in 1:n.data){
tmptime <- start(Data[[i]])
T <- end(Data[[i]])
while( sum( tmptime ) < T )
tmptime <- c(tmptime, r.gen[[i]](1))
tmpgrid[[i]] <- cumsum(tmptime)
if(tail(tmpgrid[[i]],1)>T)
tmpgrid[[i]] <- tmpgrid[[i]][-length(tmpgrid[[i]])]
}
}
# prepares original index slot
oindex <- vector(n.data, mode="list")
# checks for interpolation method, if not in the list uses "pt"
interpolation <- tmpsamp@interpolation
int.methods <- c("previous", "pt", "next", "nt", "none", "exact",
"lin", "linear")
if(! (interpolation %in% int.methods) )
interpolation <- "pt"
for(i in 1:n.data){
oindex[[i]] <- time(Data[[i]])
idx <- numeric(0)
newData <- NULL
lGrid <- length(tmpgrid[[i]])
if( interpolation %in% c("previous", "pt")){
idx <- as.numeric(sapply(tmpgrid[[i]], function(x) max(which(oindex[[i]] <= x))))
newData <- sapply(1:lGrid, function(x) as.numeric(Data[[i]][idx[x]]))
oindex[[i]] <- sapply(1:lGrid, function(x) time(Data[[i]])[idx[x]])
}
if( interpolation %in% c("next", "nt")){
idx <- as.numeric(sapply(tmpgrid[[i]], function(x) min(which(oindex[[i]] >= x))))
newData <- sapply(1:lGrid, function(x) as.numeric(Data[[i]][idx[x]]))
oindex[[i]] <- sapply(1:lGrid, function(x) time(Data[[i]])[idx[x]])
}
if( interpolation %in% c("none", "exact")){
idx <- match(tmpgrid[[i]], oindex[[i]])
newData <- sapply(1:lGrid, function(x) as.numeric(Data[[i]][idx[x]]))
oindex[[i]] <- sapply(1:lGrid, function(x) time(Data[[i]])[idx[x]])
}
if( interpolation %in% c("lin", "linear")){
idx.l <- as.numeric(sapply(tmpgrid[[i]], function(x) max(which(oindex[[i]] <= x))))
idx.r <- as.numeric(sapply(tmpgrid[[i]], function(x) min(which(oindex[[i]] >= x))))
f.int <- function(u)
(as.numeric(Data[[i]][idx.r[u]])+as.numeric(Data[[i]][idx.l[u]]))/2
newData <- sapply(1:lGrid, f.int )
oindex[[i]] <- sapply(1:lGrid, function(u) time(Data[[i]])[idx.l[u]])
}
Data[[i]] <- zoo(newData, order.by=tmpgrid[[i]])
tmpsamp@Terminal[i] <- end(Data[[i]])
tmpsamp@Initial[i] <- start(Data[[i]])
tmpsamp@n[i] <- length(Data[[i]])
}
tmpsamp@oindex <- oindex
tmpsamp@grid <- tmpgrid
tmpsamp@regular <- sapply(1:n.data, function(x) sum(abs(diff(diff(tmpgrid[[x]]))))<1e-3)
tmpsamp@delta <- sapply(1:n.data, function(x) ifelse(tmpsamp@regular[x], diff(tmpgrid[[x]])[1], numeric(0)))
obj <- NULL
tmpsamp@interpolation <- interpolation
[email protected] <- Data
obj <- setYuima(data=x, sampling=tmpsamp)
return(obj)
} ### end method
)
| /scratch/gouwar.j/cran-all/cranData/yuima/R/subsampling.R |
toLatex.yuima <- function (object, ...)
{
mod <- NULL
#if (class(object) == "yuima.model")
if (inherits(object, "yuima.model")) # fixed by YK
mod <- object
#if (class(object) == "yuima.carma")
if (inherits(object, "yuima.carma")) # fixed by YK
mod <- object
#if (class(object) == "yuima.cogarch")
if (inherits(object, "yuima.cogarch")) # fixed by YK
mod <- object
#if (class(object) == "yuima")
if (inherits(object, "yuima")) # fixed by YK
mod <- object@model
#if(class(mod) =="yuima.carma" && length(mod@[email protected])==0 )
#if((class(mod) =="yuima.carma") || (class(mod) =="yuima.cogarch") )
if(inherits(mod, "yuima.carma") || inherits(mod, "yuima.cogarch")) # fixed by YK
{
# yuima.warn("")
mysymb <- c("*", "alpha", "beta", "gamma", "delta", "rho",
"theta","sigma","mu", "sqrt")
# myrepl <- c(" \\cdot ", "\\alpha ", "\\beta ", "\\gamma ",
# "\\delta ", "\\rho ", "\\theta ", "\\sqrt ")
myrepl <- c(" \\cdot ", "\\alpha ", "\\beta ", "\\gamma ",
"\\delta ", "\\rho ", "\\theta ","\\sigma","\\mu", "\\sqrt ")
ns <- length(mysymb)
n.eq <- [email protected]
info <- mod@info
noise.var<-"W"
# We construc the system that describes the CARMA(p,q) process
if (!length([email protected])==0){noise.var <- [email protected]}
dr <- paste("\\left\\{\\begin{array}{l} \n")
main.con <- [email protected]
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
if(length([email protected])==0 && !length([email protected])==0){
main.con<-paste([email protected],"* \\ ", [email protected])
}
if(!length([email protected])==0 && length([email protected])==0){
main.con<-paste([email protected],"+ \\ ", [email protected])
}
if(!length([email protected])==0 && !length([email protected])==0){
main.con<-paste([email protected],"+ \\ ",[email protected],"* \\ ", [email protected])
}
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
main.con<-paste([email protected],"+ \\ ", [email protected])
}
}
#if((class(mod) =="yuima.carma")){
if(inherits(mod, "yuima.carma")){ # fixed by YK
dr <- paste(dr, [email protected],
"\\left(", sprintf("%s", [email protected]),"\\right) = ",main.con, "'" ,
[email protected],"\\left(", sprintf("%s", [email protected]),"\\right) \\\\ \n")
}else{
#if((class(mod) =="yuima.cogarch")){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
dr <- paste(dr, sprintf("d%s", [email protected]),
"\\left(", sprintf("%s", [email protected]),"\\right) = \\ sqrt{",[email protected],
"\\left(", sprintf("%s", [email protected]),"\\right)} \\ ",
sprintf("d%s", noise.var),"\\left(", sprintf("%s", [email protected]),"\\right) \\\\ \n")
dr <- paste(dr, [email protected],
"\\left(", sprintf("%s", [email protected]),"\\right) = ",main.con, "'" ,
[email protected],"\\left(", sprintf("%s", [email protected]),"\\right) \\\\ \n")
}
}
#if((class(mod) =="yuima.carma")){
if(inherits(mod, "yuima.carma")){ # fixed by YK
noise.latent.var <- noise.var
}else{
#if((class(mod) =="yuima.cogarch")){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
noise.latent.var <- paste0("\\left[",noise.var,",",noise.var,"\\right]^{q}")
}
}
dr <- paste(dr, sprintf("d%s", [email protected]),
"\\left(", sprintf("%s", [email protected]),"\\right)",
"=","A",[email protected],
"\\left(", sprintf("%s", [email protected]),"\\right)",
sprintf("d%s", [email protected]),
"+ e",sprintf("d%s", noise.latent.var),"\\left(",
[email protected], "\\right) \\\\ \n")
dr<- paste(dr, "\\end{array}\\right.")
#11/12
for (i in 1:ns) {
dr <- gsub(mysymb[i], myrepl[i], dr, fixed = "TRUE")
}
body <- paste("%%% Copy and paste the following output in your LaTeX file")
body <- c(body, paste("$$"))
body <- c(body, dr)
body <- c(body, paste("$$"))
# Vector Latent Variable.
body <- c(body, paste("$$"))
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
latent.lab0<-paste([email protected],0:(info@p-1),sep="_")
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
latent.lab0<-paste([email protected],1:info@q,sep="_")
}
}
if(length(latent.lab0)==1){latent.lab<-latent.lab0}
if(length(latent.lab0)==2){
latent.lab0[1]<-paste(latent.lab0[1],"(",[email protected],")",",\\ ",sep="")
latent.lab0[2]<-paste(latent.lab0[2],"(",[email protected],")",sep="")
latent.lab<-latent.lab0
}
if(length(latent.lab0)>2){
latent.lab<-paste(latent.lab0[1],"(",[email protected],")",
",\\ ","\\ldots \\ ",
",\\ ",tail(latent.lab0,n=1),
"(",[email protected],")")
}
latent.lab<-paste(latent.lab,collapse="")
X<-paste([email protected],"(",[email protected],")",
"=\\left[",latent.lab,
"\\right]'")
for (i in 1:ns) {
X <- gsub(mysymb[i], myrepl[i], X, fixed = "TRUE")
}
body <- c(body, X)
body <- c(body, paste("$$"))
# Vector Moving Average Coefficient.
body <- c(body, paste("$$"))
#b.nozeros <-c(0:info@q)
# ma.lab0<-paste(paste([email protected],0:(info@q),sep="_"),collapse=", \\ ")
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
ma.lab0<-paste([email protected],0:(info@q),sep="_")
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
ma.lab0<-paste([email protected],1:(info@p),sep="_")
}
}
#if(length(ma.lab0)==1){ma.lab1<-ma.lab0}
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
if(info@q>=0 && info@q<=1){
ma.lab1<-paste(ma.lab0,collapse=", \\ ")}
#if(length(ma.lab0)==2){
# if(info@q==1){
# ma.lab0[1]<-paste(ma.lab0[1],",\\ ",sep="")
# # ma.lab0[2]<-paste(ma.lab0[2],"(",[email protected],")",sep="")
# ma.lab1<-ma.lab0
# }
#if(length(ma.lab0)>2){
if(info@q>1){
ma.lab1<-paste(ma.lab0[1],
",\\ ","\\ldots",
" \\ , \\ ",tail(ma.lab0,n=1))
}
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
if(info@p>=0 && info@p<=2){
ma.lab1<-paste(ma.lab0,collapse=", \\ ")
}
if(info@p>2){
ma.lab1<-paste(ma.lab0[1],
",\\ ","\\ldots",
" \\ , \\ ",tail(ma.lab0,n=1))
}
}
}
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
numb.zero<-(info@p-(info@q+1))
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
numb.zero<-(info@q-info@p)
}
}
if (numb.zero==0){ma.lab <- ma.lab1}
if (numb.zero>0&&numb.zero<=2){
zeros<- 0*c(1:numb.zero)
zero.el <- paste(zeros, collapse=", \\ ")
ma.lab <- paste(ma.lab1," ,\\ ", zero.el)
}
if (numb.zero>2 ){
ma.lab <- paste(ma.lab1," ,\\ 0, \\ \\ldots \\ , \\ 0")
}
Vector.ma <- paste([email protected],"=","\\left[",ma.lab,"\\right]'")
for (i in 1:ns) {
Vector.ma <- gsub(mysymb[i], myrepl[i], Vector.ma, fixed = "TRUE")
}
body <- c(body, Vector.ma)
body <- c(body, paste("$$"))
# e vector
body <- c(body, paste("$$"))
noise.coef<-mod@diffusion
vect.e0 <- substr(tail(noise.coef,n=1), 13, nchar(tail(noise.coef,n=1)) -2)
vect.e1 <- substr(vect.e0, 2, nchar(vect.e0) -1)
dummy.e0<-strsplit(vect.e1,split="+",fixed=TRUE)
if (!length([email protected])==0){
noise.coef <- [email protected]
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
vect.e0 <- substr(tail(noise.coef,n=1), 18, nchar(tail(noise.coef,n=1)) -2)
}else{
vect.e0 <- substr(tail(noise.coef,n=1), 18, nchar(tail(noise.coef,n=1)) -2)
}
#vect.e0 <- substr(tail(noise.coef,n=1), 2, nchar(tail(noise.coef,n=1)) -1)
} else{
if(length([email protected]) != 0){
if ([email protected] != [email protected]){
dummy.e0<-c(dummy.e0[[1]][1], paste(dummy.e0[[1]][(2:length(dummy.e0[[1]]))],
"(",[email protected],")",sep=""))
dummy.e0<-gsub([email protected],paste0([email protected],"_",collapse=""),dummy.e0)
dummy.e0<-gsub([email protected],paste0([email protected],"_",collapse=""),dummy.e0)
if(length(dummy.e0)>3){
vect.e0<-paste(dummy.e0[1],"+",dummy.e0[2],
"+ \\ldots +",tail(dummy.e0,n=1))
vect.e0<-paste("(",vect.e0,")")
} else{vect.e0<-paste("(",paste(dummy.e0,collapse="+"),")")}
}
# else{
# yuima.warm("The case of loc.par and scale.par will be implemented as soon as possible")
# return(NULL)
# }
}
}
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
if (info@p==1){vect.e <- vect.e0}
if (info@p==2){vect.e <- paste("0, \\ ",vect.e0)}
if (info@p==3){vect.e <- paste("0, \\ 0, \\ ",vect.e0)}
if (info@p>3){vect.e <- paste("0, \\ \\ldots \\ , \\ 0, \\ ",vect.e0)}
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
if (info@q==1){vect.e <- vect.e0}
if (info@q==2){vect.e <- paste("0, \\ ",vect.e0)}
if (info@q==3){vect.e <- paste("0, \\ 0, \\ ",vect.e0)}
if (info@q>3){vect.e <- paste("0, \\ \\ldots \\ , \\ 0, \\ ",vect.e0)}
}
}
coeff.e<- paste("e","=","\\left[", vect.e , "\\right]'")
for (i in 1:ns) {
coeff.e <- gsub(mysymb[i], myrepl[i], coeff.e, fixed = "TRUE")
}
body <- c(body, coeff.e)
body <- c(body, paste("$$"))
# Matrix A
body <- c(body, paste("$$"))
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
Up.A<-NULL
}
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
if(info@p==1){
cent.col<-"c"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@p:1,sep="_"),collapse=" &")
}
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
if(info@q==1){
cent.col<-"c"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@q:1,sep="_"),collapse=" &")
}
}
}
#if(class(mod)=="yuima.carma"){
if(inherits(mod, "yuima.carma")){ # fixed by YK
if(info@p==2){
cent.col<-"cc"
Up.A <-" 0 & 1 \\\\ \n"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@p:1,sep="_"),collapse=" &")
}
if(info@p==3){
cent.col<-"ccc"
Up.A <-" 0 & 1 & 0 \\\\ \n 0 & 0 & 1 \\\\ \n"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@p:1,sep="_"),collapse=" &")
}
if(info@p>3){
cent.col<-"cccc"
Up.A <-" 0 & 1 & \\ldots & 0 \\\\ \n \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n 0 & 0 & \\ldots & 1 \\\\ \n"
dummy.ar<-paste(paste("",[email protected],sep=" -"),info@p:1,sep="_")
last.A <- paste(dummy.ar[1]," & ", dummy.ar[2]," & \\ldots &", tail(dummy.ar,n=1) )
}
}else{
#if(class(mod)=="yuima.cogarch"){
if(inherits(mod, "yuima.cogarch")){ # fixed by YK
if(info@q==2){
cent.col<-"cc"
Up.A <-" 0 & 1 \\\\ \n"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@q:1,sep="_"),collapse=" &")
}
if(info@q==3){
cent.col<-"ccc"
Up.A <-" 0 & 1 & 0 \\\\ \n 0 & 0 & 1 \\\\ \n"
last.A<-paste(paste(paste("",[email protected],sep=" -"),info@q:1,sep="_"),collapse=" &")
}
if(info@q>3){
cent.col<-"cccc"
Up.A <-" 0 & 1 & \\ldots & 0 \\\\ \n \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n 0 & 0 & \\ldots & 1 \\\\ \n"
dummy.ar<-paste(paste("",[email protected],sep=" -"),info@q:1,sep="_")
last.A <- paste(dummy.ar[1]," & ", dummy.ar[2]," & \\ldots &", tail(dummy.ar,n=1) )
}
}
}
matrix.A <-paste(Up.A ,last.A," \\\\ \n",sep="")
array.start<-paste0("\\begin{array}{",cent.col,"}\n",collapse="")
MATR.A<-paste("A ","=","\\left[",array.start, matrix.A, "\\end{array}\\right]" )
for (i in 1:ns) {
MATR.A <- gsub(mysymb[i], myrepl[i], MATR.A, fixed = "TRUE")
}
body <- c(body, MATR.A)
body <- c(body, paste("$$"))
body <- structure(body, class = "Latex")
return(body)
} else{
n.eq <- [email protected]
dr <- NULL
if(n.eq>1)
dr <- paste("\\left(\\begin{array}{c}\n")
for (i in 1:n.eq) {
dr <- paste(dr, substr(mod@drift[i], 2, nchar(mod@drift[i]) -1))
if(n.eq>1)
dr <- paste(dr, "\\\\ \n")
# dr <- paste(dr, substr(mod@drift[i], 3, nchar(mod@drift[i]) - 2), "\\\\ \n")
}
#
if(n.eq>1)
dr <- paste(dr, "\\end{array}\\right)")
dr <- paste(dr, sprintf("d%s", [email protected]))
n.n <- [email protected]
df <- NULL
if(n.eq>1 & n.n>1)
df <- paste(sprintf("\\left[\\begin{array}{%s}\n",paste(rep("c",n.n),sep="",collapse="")))
for (i in 1:n.eq) {
#df <- paste(df, paste(mod@diffusion[[i]], collapse = "&"))
if( n.eq>1 & n.n>1){
df <- paste(df, paste(substr(mod@diffusion[[i]], 2, nchar(mod@diffusion[[i]]) - 1) , collapse = "&"))
df <- paste(df, "\\\\ \n")
} else {
df <- paste(df, paste(substr(mod@diffusion[[i]], 2, nchar(mod@diffusion[[i]]) - 1) , collapse = ""))
}
}
if(n.eq>1 & n.n>1)
df <- paste(df, "\\end{array}\\right]")
# We consider the Jump 6/11
dj <- NULL
if (length([email protected])>=1){
if(n.eq>1)
dj<-paste("\\left(\\begin{array}{c}\n")
for (i in 1:n.eq) {
if(n.eq>1){
dj <- paste(dj, substr([email protected][[i]], 2, nchar([email protected][[i]]) - 1), "\\\\ \n")
} else {
dj <- paste(dj, substr([email protected][[i]], 2, nchar([email protected][[i]]) - 1))
}
#dj <- paste(dj, [email protected][i], "\\\\ \n")
}
if(n.eq>1)
dj <- paste(dj, "\\end{array}\\right)", sprintf("d %s", [email protected]))
}
wn <- NULL
if( n.n>1){
wn <- paste("\\left(\\begin{array}{c}\n")
}
if(n.n>1){
wn <- paste(wn, paste(sprintf("dW%s", 1:n.n), sep = "", collapse = "\\\\ "))
} else {
wn <- paste(wn, "dW1")
}
if( n.n>1){
wn <- paste(wn, "\n \\end{array}\\right)")
}
st <- NULL
if(n.eq>1)
st <- paste("\\left(\\begin{array}{c}\n")
for(i in 1:n.eq){
st <- paste(st, sprintf("d%s", [email protected][i]))
if(n.eq>1)
st <- paste(st, " \\\\ ")
}
if(n.eq>1)
st <- paste(st, "\n \\end{array}\\right)")
mysymb <- c("*", "alpha", "beta", "gamma", "delta", "rho",
"theta","sigma","mu", "sqrt")
# myrepl <- c(" \\cdot ", "\\alpha ", "\\beta ", "\\gamma ",
# "\\delta ", "\\rho ", "\\theta ", "\\sqrt ")
myrepl <- c(" \\cdot ", "\\alpha ", "\\beta ", "\\gamma ",
"\\delta ", "\\rho ", "\\theta ","\\sigma","\\mu", "\\sqrt ")
ns <- length(mysymb)
for (i in 1:ns) {
dr <- gsub(mysymb[i], myrepl[i], dr, fixed = "TRUE")
df <- gsub(mysymb[i], myrepl[i], df, fixed = "TRUE")
# for Jump
if (length([email protected])>=1){
dj <- gsub(mysymb[i], myrepl[i], dj, fixed = "TRUE")
}
}
body <- paste("%%% Copy and paste the following output in your LaTeX file")
body <- c(body, paste("$$"))
body <- c(body, paste(st))
body <- c(body, paste(" = "))
body <- c(body, paste(dr))
body <- c(body, paste(" +"))
body <- c(body, paste(df))
# body <- c(body, paste(""))
body <- c(body, paste(wn))
# For Jump 6/11
if (length([email protected])>=1){
body <- c(body, paste(" +"))
body <- c(body, paste(dj))
body <- c(body, "dZ")
}
body <- c(body, paste("$$"))
body <- c(body, paste("$$"))
# For Initial Conditions
numb.solve.var <- length([email protected])
bodyaus <- NULL
if(numb.solve.var >1){
bodyaus <- "\\left(\\begin{array}{c}\n"
}
for (i in 1:numb.solve.var) {
bodyaus <- paste(bodyaus, paste(paste([email protected][i],"(0)",sep=""),substr(mod@xinit[i], 2, nchar(mod@xinit[i]) - 1),sep="="))
if(numb.solve.var>1)
bodyaus <-paste(bodyaus, "\\\\ \n")
# paste(bodyaus, paste(paste([email protected][i],"(0)",sep=""),substr(mod@xinit[i], 3, nchar(mod@xinit[i]) - 2),sep="="), "\\\\ \n")
# paste(bodyaus, paste(paste([email protected][i],"(0)",sep=""),substr(mod@xinit[i], 2, nchar(mod@xinit[i]) - 1),sep="="), "\\\\ \n")
}
if(numb.solve.var >1){
bodyaus <- paste(bodyaus, "\\end{array}\\right)")
}
for (i in 1:ns) {
bodyaus <- gsub(mysymb[i], myrepl[i], bodyaus, fixed = "TRUE")
}
body<-c(body,paste(bodyaus))
# body <- c(body, paste(sprintf("%s(0)=%f,\\quad", [email protected],
# mod@xinit)))
body <- c(body, paste("$$"))
structure(body, class = "Latex")
}
}
toLatex.yuima.model <- toLatex.yuima
toLatex.yuima.carma <- toLatex.yuima
toLatex.yuima.cogarch <- toLatex.yuima
| /scratch/gouwar.j/cran-all/cranData/yuima/R/toLatex.R |
# Scale-by-scale lead-lag estimation by wavelets
## function to compute Daubechies' extremal phase wavelet filter
## The function is based on implementation of wavethresh package
daubechies.wavelet <- function(N){
switch (N,
"1" = {
H <- rep(0, 2)
H[1] <- 1/sqrt(2)
H[2] <- H[1]
},
"2" = {
H <- rep(0, 4)
H[1] <- 0.482962913145
H[2] <- 0.836516303738
H[3] <- 0.224143868042
H[4] <- -0.129409522551
},
"3" = {
H <- rep(0, 6)
H[1] <- 0.33267055295
H[2] <- 0.806891509311
H[3] <- 0.459877502118
H[4] <- -0.13501102001
H[5] <- -0.085441273882
H[6] <- 0.035226291882
},
"4" = {
H <- rep(0, 8)
H[1] <- 0.230377813309
H[2] <- 0.714846570553
H[3] <- 0.63088076793
H[4] <- -0.027983769417
H[5] <- -0.187034811719
H[6] <- 0.030841381836
H[7] <- 0.032883011667
H[8] <- -0.010597401785
},
"5" = {
H <- rep(0, 10)
H[1] <- 0.160102397974
H[2] <- 0.603829269797
H[3] <- 0.724308528438
H[4] <- 0.138428145901
H[5] <- -0.242294887066
H[6] <- -0.032244869585
H[7] <- 0.07757149384
H[8] <- -0.006241490213
H[9] <- -0.012580752
H[10] <- 0.003335725285
},
"6" = {
H <- rep(0, 12)
H[1] <- 0.11154074335
H[2] <- 0.494623890398
H[3] <- 0.751133908021
H[4] <- 0.315250351709
H[5] <- -0.226264693965
H[6] <- -0.129766867567
H[7] <- 0.097501605587
H[8] <- 0.02752286553
H[9] <- -0.031582039318
H[10] <- 0.000553842201
H[11] <- 0.004777257511
H[12] <- -0.001077301085
},
"7" = {
H <- rep(0, 14)
H[1] <- 0.077852054085
H[2] <- 0.396539319482
H[3] <- 0.729132090846
H[4] <- 0.469782287405
H[5] <- -0.143906003929
H[6] <- -0.224036184994
H[7] <- 0.071309219267
H[8] <- 0.080612609151
H[9] <- -0.038029936935
H[10] <- -0.016574541631
H[11] <- 0.012550998556
H[12] <- 0.000429577973
H[13] <- -0.001801640704
H[14] <- 0.0003537138
},
"8" = {
H <- rep(0, 16)
H[1] <- 0.054415842243
H[2] <- 0.312871590914
H[3] <- 0.675630736297
H[4] <- 0.585354683654
H[5] <- -0.015829105256
H[6] <- -0.284015542962
H[7] <- 0.000472484574
H[8] <- 0.12874742662
H[9] <- -0.017369301002
H[10] <- -0.044088253931
H[11] <- 0.013981027917
H[12] <- 0.008746094047
H[13] <- -0.004870352993
H[14] <- -0.000391740373
H[15] <- 0.000675449406
H[16] <- -0.000117476784
},
"9" = {
H <- rep(0, 18)
H[1] <- 0.038077947364
H[2] <- 0.243834674613
H[3] <- 0.60482312369
H[4] <- 0.657288078051
H[5] <- 0.133197385825
H[6] <- -0.293273783279
H[7] <- -0.096840783223
H[8] <- 0.148540749338
H[9] <- 0.030725681479
H[10] <- -0.067632829061
H[11] <- 0.000250947115
H[12] <- 0.022361662124
H[13] <- -0.004723204758
H[14] <- -0.004281503682
H[15] <- 0.001847646883
H[16] <- 0.000230385764
H[17] <- -0.000251963189
H[18] <- 3.934732e-05
},
"10" = {
H <- rep(0, 20)
H[1] <- 0.026670057901
H[2] <- 0.188176800078
H[3] <- 0.527201188932
H[4] <- 0.688459039454
H[5] <- 0.281172343661
H[6] <- -0.249846424327
H[7] <- -0.195946274377
H[8] <- 0.127369340336
H[9] <- 0.093057364604
H[10] <- -0.071394147166
H[11] <- -0.029457536822
H[12] <- 0.033212674059
H[13] <- 0.003606553567
H[14] <- -0.010733175483
H[15] <- 0.001395351747
H[16] <- 0.001992405295
H[17] <- -0.000685856695
H[18] <- -0.000116466855
H[19] <- 9.358867e-05
H[20] <- -1.3264203e-05
}
)
return(H)
}
## function to compute autocorrelation wavelets
autocorrelation.wavelet <- function(J, N){
h <- daubechies.wavelet(N)
Phi1 <- convolve(h, h, type = "o")
out <- vector(mode = "list", J)
out[[1]] <- (-1)^((-2*N+1):(2*N-1)) * Phi1
if(J > 1){
Phi1 <- Phi1[seq(1,4*N-1,by=2)]
for(j in 2:J){
Lj <- (2^j - 1) * (2 * N - 1) + 1
out[[j]] <- double(2*Lj - 1)
out[[j]][seq(2*N,2*Lj-2*N,by=2)] <- out[[j-1]]
out[[j]][seq(1,2*Lj-1,by=2)] <-
convolve(out[[j-1]], Phi1, type = "o")
}
}
return(out)
}
## main function
wllag <- function(x, y, J = 8, N = 10, #family = "DaubExPhase",
tau = 1e-3, from = -to, to = 100,
verbose = FALSE, in.tau = FALSE, tol = 1e-6){
time1 <- as.numeric(time(x))
time2 <- as.numeric(time(y))
grid <- seq(from, to, by = 1) * tau
Lj <- (2^J - 1) * (2 * N - 1) + 1
#Lj <- (2^J - 1) * (length(wavethresh::filter.select(N, family)$H) - 1) + 1
grid2 <- seq(from - Lj + 1, to + Lj - 1, by = 1) * tau
dx <- diff(as.numeric(x))
dy <- diff(as.numeric(y))
tmp <- .C("HYcrosscov2",
as.integer(length(grid2)),
as.integer(length(time2)),
as.integer(length(time1)),
as.double(grid2/tol),
as.double(time2/tol),
as.double(time1/tol),
as.double(dy),
as.double(dx),
value=double(length(grid2)),
PACKAGE = "yuima")$value
#if(missing(J)) J <- floor(log2(length(grid)))
#if(J < 2) stop("J must be larger than 1")
#acw <- wavethresh::PsiJ(-J, filter.number = N, family = "DaubExPhase")
#acw <- wavethresh::PsiJ(-J, filter.number = N, family = family)
acw <- autocorrelation.wavelet(J, N)
theta <- double(J)
#covar <- double(J)
#LLR <- double(J)
corr <- double(J)
crosscor <- vector("list", J)
for(j in 1:J){
wcov <- try(stats::filter(tmp, filter = acw[[j]], method = "c",
sides = 2)[Lj:(length(grid) + Lj - 1)],
silent = TRUE)
#Mj <- (2^J - 2^j) * (2 * N - 1)
#wcov <- try(convolve(tmp, acw[[j]], conj = FALSE, type = "filter")[(Mj + 1):(length(grid) + Mj)],
# silent = TRUE)
if(inherits(wcov, "try-error")){
theta[j] <- NA
crosscor[[j]] <- NA
corr[j] <- NA
}else{
#tmp.grid <- grid[-attr(wcov, "na.action")]
crosscor[[j]] <- zoo(wcov, grid)
obj <- abs(wcov)
idx1 <- which(obj == max(obj, na.rm = TRUE))
idx <- idx1[which.max(abs(grid[idx1]))]
# if there are multiple peaks, take the lag farthest from zero
theta[j] <- grid[idx]
corr[j] <- crosscor[[j]][idx]
}
}
if(verbose == TRUE){
#obj0 <- tmp[(Lj + 1):(length(grid) + Lj)]
obj0 <- tmp[Lj:(length(grid) + Lj - 1)]/sqrt(sum(dx^2)*sum(dy^2))
obj <- abs(obj0)
idx1 <- which(obj == max(obj, na.rm = TRUE))
idx <- idx1[which.max(abs(grid[idx1]))]
# if there are multiple peaks, the lag farthest from zero
theta.hy <- grid[idx]
corr.hy <- obj0[idx]
if(in.tau == TRUE){
theta <- round(theta/tau)
theta.hy <- round(theta.hy/tau)
}
result <- list(lagtheta = theta, obj.values = corr,
obj.fun = crosscor, theta.hry = theta.hy,
cor.hry = corr.hy, ccor.hry = zoo(obj0, grid))
class(result) <- "yuima.wllag"
}else{
if(in.tau == TRUE){
result <- round(theta/tau)
}else{
result <- theta
}
}
return(result)
}
# print method for yuima.wllag-class
print.yuima.wllag <- function(x, ...){
cat("Estimated scale-by-scale lead-lag parameters\n")
print(x$lagtheta, ...)
cat("Corresponding values of objective functions\n")
print(x$obj.values, ...)
cat("Estimated lead-lag parameter in the HRY sense\n")
print(x$theta.hry, ...)
cat("Corresponding correlation coefficient\n")
print(x$cor.hry, ...)
}
# plot method for yuima.wllag class
plot.yuima.wllag <- function(x, selectJ = NULL, xlab = expression(theta),
ylab = "", ...){
J <- length(x$lagtheta)
if(is.null(selectJ)) selectJ <- 1:J
for(j in selectJ){
#plot(x$ccor[[j]], main=paste("j=",j), xlab=expression(theta),
# ylab=expression(U[j](theta)), type = type, pch = pch, ...)
plot(x$obj.fun[[j]], main = paste("j = ", j, sep =""),
xlab = xlab, ylab = ylab, ...)
abline(0, 0, lty = "dotted")
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/wllag.R |
ybook <- function(chapter){
chapter <- as.integer(chapter[1])
if(chapter %in% 1:7){
file.show(file.path(.libPaths()[1],"yuima","ybook",sprintf("chapter%d.R",chapter)))
} else {
cat("\nPlease choose an integer number within 1 and 7")
}
}
yuima.stop <- function(x)
stop(sprintf("\nYUIMA: %s\n", x))
yuima.warn <- function(x)
warning(sprintf("\nYUIMA: %s\n", x))
# 22/11/2013
# We introduce a new utility yuima.simplify that allows us to simplify
# the expressions in the drift, diffusion and jump terms.
# yuima.Simplify modified from the original code Simplify.R
# by Andrew Clausen <[email protected]> in 2007.
# http://economics.sas.upenn.edu/~clausen/computing/Simplify.R
# This isn't a serious attempt at simplification code. It just does some
# obvious things like 0 + x => x. It was written to support Deriv.R.
yuima.Simplify <- function(expr, yuima.env){
###
Simplify_ <- function(expr)
{
if (is.symbol(expr)) {
expr
} else if (is.language(expr) && is.symbol(expr[[1]])) {
# is there a rule in the table?
sym.name <- as.character(expr[[1]])
# if (class(try(Simplify.rule <-
# get(sym.name, envir=yuima.env,
# inherits=FALSE), silent=TRUE))
# != "try-error")
tmpOutTry <- try(Simplify.rule <-
get(sym.name, envir=yuima.env,
inherits=FALSE), silent=TRUE)
if(!inherits(tmpOutTry,"try-error"))
return(Simplify.rule(expr))
}
expr
}
Simplify.function <- function(f, x=names(formals(f)), env=parent.frame())
{
stopifnot(is.function(f))
as.function(c(as.list(formals(f)),
Simplify_(body(f))),
envir=env)
}
`Simplify.+` <- function(expr)
{
if (length(expr) == 2)
{
if (is.numeric(expr[[2]]))
return(+expr[[2]])
return(expr)
}
a <- Simplify_(expr[[2]])
b <- Simplify_(expr[[3]])
if (is.numeric(a) && all(a == 0)) {
b
} else if (is.numeric(b) && all(b == 0)) {
a
} else if (is.numeric(a) && is.numeric(b)) {
a + b
} else {
expr[[2]] <- a
expr[[3]] <- b
expr
}
}
`Simplify.-` <- function(expr)
{
if (length(expr) == 2)
{
if (is.numeric(expr[[2]]))
return(-expr[[2]])
return(expr)
}
a <- Simplify_(expr[[2]])
b <- Simplify_(expr[[3]])
if (is.numeric(a) && all(a == 0)) {
-b
} else if (is.numeric(b) && all(b == 0)) {
a
} else if (is.numeric(a) && is.numeric(b)) {
a - b
} else {
expr[[2]] <- a
expr[[3]] <- b
expr
}
}
`Simplify.(` <- function(expr)
expr[[2]]
`Simplify.*` <- function(expr)
{
a <- Simplify_(expr[[2]])
b <- Simplify_(expr[[3]])
if (is.numeric(a) && all(a == 0)) {
0
} else if (is.numeric(b) && all(b == 0)) {
0
} else if (is.numeric(a) && all(a == 1)) {
b
} else if (is.numeric(b) && all(b == 1)) {
a
} else if (is.numeric(a) && is.numeric(b)) {
a * b
} else {
expr[[2]] <- a
expr[[3]] <- b
expr
}
}
`Simplify.^` <- function(expr)
{
a <- Simplify_(expr[[2]])
b <- Simplify_(expr[[3]])
if (is.numeric(a) && all(a == 0)) {
0
} else if (is.numeric(b) && all(b == 0)) {
1
} else if (is.numeric(a) && all(a == 1)) {
1
} else if (is.numeric(b) && all(b == 1)) {
a
} else if (is.numeric(a) && is.numeric(b)) {
a ^ b
} else {
expr[[2]] <- a
expr[[3]] <- b
expr
}
}
`Simplify.c` <- function(expr)
{
args <- expr[-1]
args.simplified <- lapply(args, Simplify_)
if (all(lapply(args.simplified, is.numeric))) {
as.numeric(args.simplified)
} else {
for (i in 1:length(args))
expr[[i + 1]] <- args.simplified[[i]]
expr
}
}
assign("+", `Simplify.+`, envir=yuima.env)
assign("-", `Simplify.-`, envir=yuima.env)
assign("*", `Simplify.*`, envir=yuima.env)
assign("(", `Simplify.(`, envir=yuima.env)
assign("c", `Simplify.c`, envir=yuima.env)
assign("^", `Simplify.^`, envir=yuima.env)
###
as.expression(Simplify_(expr[[1]]))
}
## Constructor and Initializer of class 'yuima'
# we convert objects to "zoo" internally
# we should change it later to more flexible classes
setMethod("initialize", "yuima",
function(.Object, data=NULL, model=NULL, sampling=NULL, characteristic=NULL, functional=NULL){
eqn <- NULL
if(!is.null(data)){
.Object@data <- data
eqn <- dim(data)
if(is.null(sampling))
sampling <- setSampling(grid=list(index(get.zoo.data(data)[[1]])))
}
if(!is.null(model)){
if(!is.null(eqn)){
if([email protected]){
yuima.warn("Model's equation number missmatch.")
return(NULL)
}
}else{
eqn <- [email protected]
}
.Object@model <- model
}
if(!is.null(sampling)){
if(!is.null(eqn)){
if(eqn!=length(sampling@Terminal)){
if(length(sampling@Terminal)==1){
sampling@Terminal <- rep(sampling@Terminal, eqn)
sampling@n <- rep(sampling@n, eqn)
}else{
yuima.warn("Sampling's equation number missmatch.")
return(NULL)
}
}
}else{
eqn <- length(sampling@Terminal)
}
.Object@sampling <- sampling
}
if(!is.null(characteristic)){
if(!is.null(eqn)){
if([email protected]){
yuima.warn("Characteristic's equation number missmatch.")
return(NULL)
}
}
.Object@characteristic <- characteristic
}else if(!is.null(eqn)){
characteristic <- new("yuima.characteristic", equation.number=eqn, time.scale=1)
.Object@characteristic <- characteristic
}
if(!is.null(functional)) .Object@functional <- functional
return(.Object)
})
# setter
setYuima <-
function(data=NULL, model=NULL, sampling=NULL, characteristic=NULL, functional=NULL){
if(is.CarmaHawkes(model) && !is.null(data)){
if(is(data,"zoo")){
data <- setData(original.data = data, t0 = index(data)[1])
}
if(is.null(sampling)){
zooData <- get.zoo.data(data)[[1]]
originalgrid <- index(zooData)
samp <- setSampling(Initial=originalgrid[1], Terminal = tail(originalgrid,1L), n = as.integer((tail(originalgrid,1L)-originalgrid[1])/mean(diff(originalgrid))))
gridData <- na.approx(zooData, xout=samp@grid[[1]])
[email protected]<-model@[email protected]
[email protected][[1]] <- gridData
res <- new("yuima",data=NULL, model=model, sampling=NULL, characteristic=characteristic,functional=functional)
res@data <- data
res@sampling <- samp
}else{
zooData <- get.zoo.data(data)
samp <- sampling
gridData <- na.approx(zooData, xout=samp@grid[[1]])
[email protected]<-model@[email protected]
[email protected][[1]] <- gridData
res <- new("yuima",data=NULL, model=model, sampling=NULL, characteristic=characteristic,functional=functional)
res@data <- data
res@sampling <- samp
}
return(res)
}
if(is.CARMA(model)&& !is.null(data)){
if(dim([email protected])[2]==1){
dum.matr<-matrix(0,length([email protected]),
(model@info@p+1))
dum.matr[,1]<-as.numeric([email protected])
data<-setData(zoo(x=dum.matr, order.by=time([email protected][[1]])))
}
}
if(is.COGARCH(model)&& !is.null(data)){
if(dim([email protected])[2]==1){
# data<-setData(zoo(x=matrix(as.numeric([email protected]),length([email protected]),
# (model@info@p+1)), order.by=time([email protected][[1]])))
dum.matr<-matrix(0,length([email protected]),
(model@info@q+2))
dum.matr[,1]<-as.numeric([email protected])
data<-setData(zoo(x=dum.matr, order.by=time([email protected][[1]])))
}
}
# LM 25/04/15
return(new("yuima", data=data, model=model, sampling=sampling, characteristic=characteristic,functional=functional))
}
setMethod("show", "yuima.functional",
function(object){
str(object)
} )
setMethod("show", "yuima.sampling",
function(object){
str(object)
} )
setMethod("show", "yuima.data",
function(object){
show(setYuima(data=object))
} )
setMethod("show", "yuima.model",
function(object){
show(setYuima(model=object))
} )
setMethod("show", "yuima",
function(object){
myenv <- new.env()
mod <- object@model
has.drift <- FALSE
has.diff <- FALSE
has.fbm <- FALSE
has.levy <- FALSE
is.wienerdiff <- FALSE
is.fracdiff <- FALSE
is.jumpdiff <- FALSE
is.carma <- FALSE
is.cogarch <- FALSE
is.poisson <- is.Poisson(mod)
if(length(mod@drift)>0 & !all(as.character(mod@drift) %in% c("(0)","expression((0))"))) { has.drift <- TRUE }
if(length(mod@diffusion)>0 & !all(as.character(mod@diffusion) %in% c("(0)", "expression((0))"))) { has.diff <- TRUE}
if(length([email protected])>0){ has.levy <- TRUE}
if(!is.null(mod@hurst)){
if(!is.na(mod@hurst)){
if(mod@hurst != 0.5){
has.fbm <- TRUE
}
}
}
if( has.diff ) is.wienerdiff <- TRUE
#if( has.drift | has.diff ) is.wienerdiff <- TRUE
if( has.fbm ) is.fracdiff <- TRUE
if( has.levy ) is.jumpdiff <- TRUE
ldif <- 0
if(length(mod@diffusion)>0)
ldif <- length(mod@diffusion[[1]])
if(ldif==1 & (length(mod@diffusion)==0)){
if( as.character(mod@diffusion[[1]]) %in% c("(0)","expression(0)") ){
has.diff <- FALSE
is.wienerdiff <- FALSE
is.fracdiff <- FALSE
}
}
#if( class(mod) == "yuima.carma")
if( inherits(mod, "yuima.carma")) # YK, Mar. 22, 2022
is.carma <- TRUE
#if( class(mod) == "yuima.cogarch"){
if( inherits(mod, "yuima.cogarch")){ # YK, Mar. 22, 2022
is.cogarch <- TRUE
is.wienerdiff <- FALSE
is.fracdiff <- FALSE
}
if( is.wienerdiff | is.fracdiff | is.jumpdiff ){
if( is.wienerdiff & ! is.carma & !is.poisson & !is.cogarch){
cat("\nDiffusion process")
if(!has.drift) cat(", driftless")
if( is.fracdiff ){
if(!is.na(mod@hurst)){
if(mod@hurst!=0.5){
cat(sprintf(" with Hurst index:%.2f", mod@hurst))
}
} else {
cat(" with unknown Hurst index")
}
}
}
if(is.carma)
cat(sprintf("\nCarma process p=%d, q=%d", mod@info@p, mod@info@q))
if(is.cogarch)
cat(sprintf("\nCogarch process p=%d, q=%d", mod@info@p, mod@info@q))
if(is.poisson)
cat("\nCompound Poisson process")
if( (is.jumpdiff & !is.cogarch) ){
if( (is.wienerdiff | is.carma) & !is.poisson ){
cat(" with Levy jumps")
} else {
if(!is.poisson)
cat("Levy process")
}
}else{
if(is.jumpdiff)
cat(" with Levy jumps")
}
cat(sprintf("\nNumber of equations: %d", [email protected]))
if((is.wienerdiff | is.fracdiff) & !is.poisson)
cat(sprintf("\nNumber of Wiener noises: %d", [email protected]))
if(is.jumpdiff & !is.poisson)
cat(sprintf("\nNumber of Levy noises: %d", 1))
if(is.cogarch)
cat(sprintf("\nNumber of quadratic variation: %d", 1))
if(length(mod@parameter@all)>0){
cat(sprintf("\nParametric model with %d parameters",length(mod@parameter@all)))
}
}
if(length(object@[email protected])>0){
n.series <- 1
if(!is.null(dim(object@[email protected]))){
n.series <- dim(object@[email protected])[2]
n.length <- dim(object@[email protected])[1]
} else {
n.length <- length(object@[email protected])
}
cat(sprintf("\n\nNumber of original time series: %d\nlength = %d, time range [%s ; %s]", n.series, n.length, min(time(object@[email protected])), max(time(object@[email protected]))))
}
if(length(object@[email protected])>0){
n.series <- length(object@[email protected])
n.length <- unlist(lapply(object@[email protected], length))
t.min <- unlist(lapply(object@[email protected], function(u) as.character(round(time(u)[which.min(time(u))],3))))
t.max <- unlist(lapply(object@[email protected], function(u) as.character(round(time(u)[which.max(time(u))],3))))
delta <- NULL
is.max.delta <- rep("", n.series)
have.max.delta <- FALSE
for(i in 1:n.series){
tmp <- length(table(round(diff(time(object@[email protected][[i]])),5)))
if(tmp>1){
tmp <- max(diff(time(object@[email protected][[i]])), na.rm=TRUE)
is.max.delta[i] <- "*"
have.max.delta <- TRUE
#tmp <- NULL
} else {
tmp <- diff(time(object@[email protected][[i]]))[1]
}
if(is.null(tmp)){
delta <- c(delta, NA)
} else {
delta <- c(delta, tmp)
}
}
cat(sprintf("\n\nNumber of zoo time series: %d\n", n.series))
tmp <- data.frame(length=n.length, time.min = t.min, time.max =t.max, delta=delta)
if(have.max.delta)
tmp <- data.frame(tmp, note=is.max.delta)
nm <- names(object@[email protected])
if(is.null(nm)){
rownames(tmp) <- sprintf("Series %d",1:n.series)
} else {
rownames(tmp) <- nm
}
print(tmp)
if(have.max.delta)
cat("================\n* : maximal mesh")
}
})
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.R |
setMethod("initialize", "yuima.characteristic",
function(.Object, equation.number, time.scale){
if(equation.number==length(time.scale)){
[email protected] <- equation.number
[email protected] <- time.scale
}else if(length(time.scale)==1){
time.scale <- rep(time.scale, equation.number)
[email protected] <- equation.number
[email protected] <- time.scale
}else{
yuima.warn("Dimension missmatch")
return(NULL)
}
return(.Object)
})
setCharacteristic <-
function(equation.number=1, time.scale=1){
return(new("yuima.characteristic", equation.number, time.scale=time.scale))
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.characteristic.R |
##Constructor and Initializer of class 'yuima.data'
# we convert objects to "zoo" internally
setMethod("initialize", "yuima.data",
function(.Object, original.data, delta=NULL, t0=0){
[email protected] <- original.data
if(is.list(original.data) && is.zoo(original.data[[1]])) {
[email protected] <- original.data
} else {
[email protected] <- as.list(as.zoo([email protected]))
}
if(!is.null(delta)){
delta <- rep(delta, length([email protected]))
for(i in 1:length([email protected])){
n <- length([email protected][[i]])
t <- t0 + (0:(n-1))*delta[i]
# t<-seq(0, n*delta[i], length=n)+t0
## L.M. Using this mod we get the same result on JSS
index([email protected][[i]]) <- t
}
}
return(.Object)
})
# utils
onezoo <- function(ydata) {
dat <- get.zoo.data(ydata)
dats <- dat[[1]]
if(length(dat)>1) {
for(i in 2:(length(dat))) {
dats <- merge(dats,dat[[i]])
}
}
if(!is.null(dim(dats))){
#if(class(ydata)=="yuima")
if(inherits(ydata, "yuima")) # YK, Mar. 22, 2022
colnames(dats) <- colnames(ydata@[email protected])
#if(class(ydata)=="yuima.data")
if(inherits(ydata, "yuima.data")) # YK, Mar. 22, 2022
colnames(dats) <- colnames([email protected])
}
return(dats)
}
# accessors
setData <-
function(original.data, delta=NULL, t0=0){
return(new("yuima.data", original.data=original.data, delta=delta, t0=t0 ))
}
setGeneric("get.zoo.data",
function(x)
standardGeneric("get.zoo.data")
)
setMethod("get.zoo.data", signature(x="yuima.data"),
function(x){
return([email protected])
})
# following funcs are basic generic funcs
setGeneric("plot",
function(x,y,...)
standardGeneric("plot")
)
setMethod("plot",signature(x="yuima.data"),
function(x,y,main="",xlab="index",ylab=names([email protected]),...){
plot(onezoo(x),main=main,xlab=xlab,ylab=ylab,...)
}
)
#setGeneric("time",
# function(x,...)
# standardGeneric("time")
# )
#setMethod("time", signature(x="yuima.data"),
# function(x,...){
# return(time([email protected]))
# })
#setGeneric("end",
# def = function(x,...) standardGeneric("end")
# )
#setMethod("end", signature(x="yuima.data"),
# function(x,...){
# return(end([email protected]))
# })
#setGeneric("start",
# function(x,...)
# standardGeneric("start")
# )
#setMethod("start", signature(x="yuima.data"),
# function(x,...){
# return(start([email protected]))
# })
# length is primitive, so no standardGeneric should be defined
setMethod("length", signature(x= "yuima.data"),
function(x){
# if(is.null(dim([email protected])))
# return(length([email protected]))
# else
# return(dim([email protected])[1])
result <- numeric()
for(i in 1:(length([email protected]))) result <- c(result,length([email protected][[i]]))
return(result)
}
)
setMethod("dim", signature(x = "yuima.data"),
function(x){
return(length([email protected]))
}
)
# same methods for 'yuima'. Depend on same methods for 'data'
setMethod("get.zoo.data", "yuima",
function(x){
return(get.zoo.data(x@data))
})
setMethod("length", "yuima",
function(x){
return(length(x@data))
})
setMethod("dim", "yuima",
function(x){
return(dim(x@data))
})
setMethod("plot","yuima",
function(x,y,xlab=x@[email protected],ylab=x@[email protected],...){
if(length(x@[email protected])==0) {
plot(x@data,...)
} else {
plot(x@data,xlab=xlab,ylab=ylab,...)
}
})
##:: yuima.data obj cbind ( implementation 08/18 )
setGeneric("cbind.yuima",
function(x, ...)
standardGeneric("cbind.yuima")
)
setMethod("cbind.yuima", signature(x="yuima"),
function(x, ...){
##:: init
y.list <- list(x, ...)
y.num <- length(y.list)
##:: bind yuima.data in yuima
yd.tmp <- y.list[[1]]@data
for(idx in 2:y.num){
##:: error check
##if( class(y.list[[idx]])!="yuima"){
if( !inherits(y.list[[idx]],"yuima")){
stop("arg ", idx, " is not yuima-class")
}
##:: bind
yd.tmp <- cbind.yuima(yd.tmp, y.list[[idx]]@data)
}
##:: substitute yuima.data
x@data <- yd.tmp
##:: return result
return(x)
}
)
setMethod("cbind.yuima", signature(x="yuima.data"),
function(x, ...){
##:: init
yd.list <- list(x, ...)
yd.num <- length(yd.list)
##:: bind yuima.data (original.data)
od.tmp <- yd.list[[1]]@original.data
for(idx in 2:yd.num){
##:: error check
##if( class(yd.list[[idx]])!="yuima.data" ){
if( !inherits(yd.list[[idx]],"yuima.data") ){
stop("arg ", idx, " is not yuima.data-class.")
}
##:: bind
od.tmp <- cbind(od.tmp, yd.list[[idx]]@original.data)
}
##:: return result
return(new("yuima.data", original.data=od.tmp))
}
)
##:: END ( yuima.data obj cbind )
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.data.R |
# constructor and initializer of class 'yuima.functional'
setMethod("initialize", "yuima.functional",
function(.Object, F, f, xinit, e){
.Object@F <- F
.Object@f <- f
.Object@xinit <- xinit
#.Object@Terminal <- Terminal
#.Object@division <- division
.Object@e <- e
return(.Object)
})
# setter
setGeneric("setFunctional",
function(model, F, f, xinit, e)
standardGeneric("setFunctional")
)
setMethod("setFunctional", "yuima",
function(model, F, f, xinit, e){
model@functional <- setFunctional(model@model,F,f,xinit,e)@functional
return(model)
})
setMethod("setFunctional", "yuima.model",
function(model, F, f, xinit, e){
# error check
if( missing(model)){
yuima.warn("yuima.model is missing.")
return(NULL)
}
if( missing(xinit)){
yuima.warn("Initial value of state variable is missing.")
return(NULL)
}
if( missing(f) || missing(F) || missing(e)){
yuima.warn("Functional specification is incomplete.")
return(NULL)
}
r.size <- [email protected]
d.size <- [email protected]
k.size <- length(F)
if( length(f) != (r.size + 1)){
yuima.warn("Functional needs r+1 f_alphas.")
return(NULL)
}
if( length(f[[1]]) != k.size){
yuima.warn("Missmatch in dimension of functional.")
return(NULL)
}
if( length(xinit) != d.size){
yuima.warn("Missmatch in dimension of functional and state variables.")
return(NULL)
}
# instanciate
return(setYuima(model = model,functional = new("yuima.functional", F=F, f=f, xinit=xinit, e=e )))
})
# getter of each variables
setGeneric("getF",
function(x)
standardGeneric("getF")
)
setMethod("getF", "yuima.functional",
function(x){
return(x@F)
})
setGeneric("getf",
function(x)
standardGeneric("getf")
)
setMethod("getf", "yuima.functional",
function(x){
return(x@f)
})
setGeneric("getxinit",
function(x)
standardGeneric("getxinit")
)
setMethod("getxinit", "yuima.functional",
function(x){
return(x@xinit)
})
setGeneric("gete",
function(x)
standardGeneric("gete")
)
setMethod("gete", "yuima.functional",
function(x){
return(x@e)
})
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.functional.R |
## Distribution Law
setClass("yuima.law",representation(rng = "function",
density = "function",
cdf = "function",
quantile = "function",
characteristic = "function",
param.measure = "character",
time.var = "character",
dim = "numLike")
)
setMethod("initialize", "yuima.law",
function(.Object,
rng = function(n,...){},
density = function(x,...){},
cdf = function(q,...){},
quantile = function(p,...){},
characteristic = function(u,...){},
param.measure = character(),
time.var = character(),
dim = NA
){
.Object@rng <- rng
.Object@density <- density
.Object@cdf <- cdf
.Object@quantile <- quantile
.Object@characteristic <- characteristic
[email protected] <- param.measure
[email protected] <- time.var
.Object@dim <- dim
return(.Object)
}
)
setMethod("rand","yuima.law",
function(object, n, param, ...){
res <- aux.rand(object, n, param, ...)
return(res)
}
)
setMethod("dens","yuima.law",
function(object, x, param, log = FALSE, ...){
res <- aux.dens(object, x, param, log, ...)
return(res)
}
)
setMethod("cdf","yuima.law",
function(object, q, param, ...){
res <- aux.cdf(object, q, param, log, ...)
return(res)
}
)
setMethod("quant","yuima.law",
function(object, p, param, ...){
res <- aux.quant(object, p, param, ...)
return(res)
}
)
setMethod("char","yuima.law",
function(object, u, param, ...){
res <- aux.char(object, u, param, ...)
return(res)
}
)
# Constructor
setLaw <- function(rng = function(n,...){NULL},
density = function(x,...){NULL},
cdf = function(q,...){NULL},
quant = function(p,...){NULL},
characteristic = function(u,...){NULL},
time.var="t",
dim = NA){
param <- NULL
param.rng <- extrapParam(myfun = rng, time.var = time.var, aux.var = "n" )
CondRng<- FALSE
if(all(param.rng %in% "...")){
# yuima.warn("rng is not defined")
}else{
CondRng <- TRUE
param <- param.rng
}
param.dens <- extrapParam(myfun = density, time.var = time.var, aux.var = "x" )
CondDens<- FALSE
if(all(param.dens %in% "...")){
# yuima.warn("density is not defined")
}else{
CondDens <- TRUE
param <- param.dens
}
if(CondDens){
if(CondRng){
if(!all(param.dens %in% param.rng)){
yuima.stop("dens and rng have different parameters")
}
}
}
param.cdf <- extrapParam(myfun = cdf, time.var = time.var, aux.var = "q" )
#Condcdf<- FALSE
if(all(param.cdf %in% "...")){
#yuima.warn("cdf is not defined")
}else{
# Condcdf <- TRUE
if(is.null(param)){
param <- param.cdf
}else{
if(!all(param %in% param.cdf)){
yuima.stop("cdf has different parameters")
}
}
}
param.quant <- extrapParam(myfun = quant, time.var = time.var, aux.var = "p" )
# Condquant<- FALSE
if(all(param.quant %in% "...")){
#yuima.warn("cdf is not defined")
}else{
# Condquant <- TRUE
if(is.null(param)){
param <- param.quant
}else{
if(!all(param %in% param.quant)){
yuima.stop("quantile has different parameters")
}
}
}
param.char <- extrapParam(myfun = characteristic,
time.var = time.var,
aux.var = "u" )
if(all(param.char %in% "...")){
# yuima.warn("char is not defined")
}else{
if(is.null(param)){
param <- param.char
}else{
if(!all(param %in% param.char)){
yuima.stop("quantile has different parameters")
}
}
}
if(is.null(param)){
param<-character()
}
res <- new("yuima.law",
rng = rng,
density = density,
cdf = cdf,
characteristic = characteristic,
quantile = quant,
param.measure = param,
time.var = time.var,
dim = NA)
return(res)
}
extrapParam <- function(myfun, time.var, aux.var){
dummy <- names(as.list(args(myfun)))
dummy <- dummy[-length(dummy)]
if(dummy[1] != aux.var){
yuima.stop("Change rand.var or charac.var ...")
}
cond <- dummy %in% time.var
# if(!any(cond)){
# yuima.warn("The yuima.law is the distribution
# of the jump size, in a CP process")
# }
dummy.par <- dummy[!cond]
dummy <- dummy.par[!dummy.par%in%aux.var]
return(dummy)
}
### From a Characteristic Function To yuima.law object
InternalDensity <- function(x, param, mynames, time.names, time.var,
up, low, N_grid, method , myfun, N_Fourier){
myenv <- new.env()
for(i in c(1:length(param))){
assign(mynames[i], param[i] , envir =myenv )
}
assign(time.names, time.var, envir =myenv)
x_old <- x
x_new <-unique(sort(c(low, x_old, up)))
x_new <- unique(sort(c(seq(min(x_new)-0.1,max(x_new)+0.1, length.out =N_grid+1),x)))
alim <- min(x_new)
blim <- max(x_new)
i <- 0:(N_Fourier - 1)
dx <- (blim - alim)/N_Fourier
x <- alim + i * dx
dt <- 2 * pi/(N_Fourier * dx)
c <- -N_Fourier/2 * dt
d <- N_Fourier/2 * dt
u <- c + i * dt
assign("u",u,myenv)
#dummyphy <- eval(parse(text=myfun))
#phi <- as.numeric(eval(parse(text=myfun), envir =myenv))
phi <- eval(parse(text=myfun), envir =myenv)
#plot(u,phi, type="l")
X <- exp(-(0 + (0+1i)) * i * dt * alim) * phi
Y <- fft(X)
density <- dt/(2 * pi) * exp(-(0 + (0+1i)) * c * x) * Y
invFFT<-data.frame(i = i, u = u, characteristic_function = phi,
x = x, density = Re(density))
#dens <- na.approx(zoo(x=invFFT$density, order.by= invFFT$x), xout=x_old)
na <- is.na(invFFT$density)
dens <- approx(x=invFFT$x[!na], y=invFFT$density[!na], xout=x_old)$y
return(dens)
}
InternalCdf <- function(q, param, mynames, time.names,
time.var, up, low, N_grid, method, myfun, N_Fourier){
x_old <- q
x_new <-unique(sort(c(low, x_old, up)))
x_new <- unique(sort(c(seq(min(x_new)-0.1,max(x_new)+0.1, length.out =N_grid+1),x_old)))
dens <- InternalDensity(x_new, param, mynames, time.names, time.var, up, low, N_grid, method , myfun, N_Fourier)
cdf <- c(0,cumsum(as.numeric(dens)[-1]*diff(x_new)))
res <- na.approx(zoo(cdf, order.by=sort(x_new)), xout = q)
return(res)
}
InternalRnd <- function(n, param, mynames, time.names,
time.var, up, low, N_grid, method, myfun, N_Fourier){
x_new <- seq(low-0.1,up+0.1, length.out =N_grid+1)
cdf<-as.numeric(InternalCdf(q=x_new, param, mynames, time.names,time.var, up, low, N_grid, method, myfun, N_Fourier))
cdf0 <- cdf[cdf>0 & cdf<1]
x_new0 <- x_new[cdf>0 & cdf<1]
rndUn0 <- runif(n, min = min(cdf0), max(cdf0))
res <- approx(y=x_new0, x = cdf0, xout=rndUn0, ties = mean, rule =2)
if(length(res$y)==n){
return(as.numeric(res$y))
}else{
m <- n-length(res$y)
rndUn1 <- runif(m, min = min(cdf0), max(cdf0))
res1 <- approx(y=x_new0, x = cdf0, xout=rndUn1)
res_new <- c(res$y,res1$y)
if(length(res_new)==n){
return(res_new)
}else{
m<-n-length(res_new)
return(c(res_new, sample(res_new, m)))
}
}
}
InternalQnt <- function(p, param, mynames, time.names,
time.var, up, low, N_grid, method, myfun, N_Fourier){
x_new <- seq(low-0.1,up+0.1, length.out =N_grid+1)
cdf<-as.numeric(InternalCdf(q=x_new, param, mynames, time.names,time.var, up, low, N_grid, method, myfun, N_Fourier))
cdf0 <- cdf[cdf>0 & cdf<1]
x_new0 <- x_new[cdf>0 & cdf<1]
res <- approx(y=x_new0, x = cdf0, xout=p)
return(res$y)
}
FromCF2yuima_law <- function(myfun, time.names = "t", var_char = "u",
up = 45, low = -45, N_grid = 50001, N_Fourier=2^10){
method <- "FFT"
if(!myfun %in% names(globalenv())){
yuima.stop("the characteristi function is not defined in the global enviromnent: check arg myfun")
}
dumEval <- parse(text = paste0("dumFun <- ", myfun))
lawpar<-extrapParam(eval(dumEval),time.names, var_char)
# if(!all(names(true.par) %in% lawpar)){
# stop("error massage")
# }
true.par<- lawpar
mynames <- lawpar
nametime <- time.names
dumm1 <- paste0("(", paste0(c("u",lawpar,time.names), collapse=", "),")")
mystring <- paste0(paste(paste0(lawpar, collapse=", "), time.names, sep =", " ),"){")
mystring <- paste(mystring, paste0(" up <- ",up) ,sep="\n")
mystring <- paste(mystring, paste0(" low <- ", low) ,sep="\n")
mystring <- paste(mystring, paste0(" N_grid <- ", N_grid) ,sep="\n")
mystring <- paste(mystring, paste0(" param <- c(", paste0(lawpar, collapse=" ,"), ")") ,sep="\n")
mystring <- paste(mystring, paste0(" mynames <- c('", paste0(mynames, collapse="' ,'"), "')") ,sep="\n")
mystring <- paste(mystring, paste0(" time.var <- c(", paste0(nametime, collapse=" ,"), ")") ,sep="\n")
mystring <- paste(mystring, paste0(" time.names <- c('", paste0(nametime, collapse="' ,"), "')") ,sep="\n")
mystring <- paste(mystring, paste0(" method <- c('", paste0(method, collapse="' ,"), "')") ,sep="\n")
mystring <- paste(mystring, paste0(" myfun <- '", paste0(paste0(myfun, collapse="' ,"),dumm1), "'") ,sep="\n")
mystring <- paste(mystring, paste0(" N_Fourier <- ", N_Fourier) ,sep="\n")
mystring_dens_in <- "dmyLaw <- function(x, "
mystring_dens_fin <- paste(mystring, paste(" res <- InternalDensity(x", "param", "mynames", "time.names",
"time.var", "up", "low", "N_grid",
"method ",
"myfun",
"N_Fourier",
sep = ", " ) ,sep="\n")
mystring_dens <- paste0(mystring_dens_in,mystring_dens_fin)
mystring_dens <- paste(mystring_dens,")", sep="")
mystring_dens <- paste(mystring_dens, " return(res)","}" ,sep="\n")
mystring_cdf_in <- "pmyLaw <- function(q, "
mystring_cdf_fin <- paste(mystring, paste(" res <- InternalCdf(q", "param", "mynames", "time.names",
"time.var", "up", "low", "N_grid",
"method ",
"myfun",
"N_Fourier",
sep = ", " ) ,sep="\n")
#cat(mystring)
mystring_cdf <- paste0(mystring_cdf_in,mystring_cdf_fin)
mystring_cdf <- paste(mystring_cdf,")", sep="")
mystring_cdf <- paste(mystring_cdf, " return(res)","}" ,sep="\n")
mystring_rnd_in <- "rmyLaw <- function(n, "
mystring_rnd_fin <- paste(mystring, paste(" res <- InternalRnd(n", "param", "mynames", "time.names",
"time.var", "up", "low", "N_grid",
"method ",
"myfun",
"N_Fourier",
sep = ", " ) ,sep="\n")
#cat(mystring)
mystring_rnd <- paste0(mystring_rnd_in,mystring_rnd_fin)
mystring_rnd <- paste(mystring_rnd,")", sep="")
mystring_rnd <- paste(mystring_rnd, " return(res)","}" ,sep="\n")
mystring_qnt_in <- "qmyLaw <- function(p, "
mystring_qnt_fin <- paste(mystring, paste(" res <- InternalQnt(p", "param", "mynames", "time.names",
"time.var", "up", "low", "N_grid",
"method ",
"myfun",
"N_Fourier",
sep = ", " ) ,sep="\n")
#cat(mystring)
mystring_qnt <- paste0(mystring_qnt_in,mystring_qnt_fin)
mystring_qnt <- paste(mystring_qnt,")", sep="")
mystring_qnt <- paste(mystring_qnt, " return(res)","}" ,sep="\n")
dmyLaw <- NULL
pmyLaw <- NULL
rmyLaw <- NULL
qmyLaw <- NULL
eval(parse(text=mystring_dens))
eval(parse(text=mystring_cdf))
eval(parse(text=mystring_rnd))
eval(parse(text=mystring_qnt))
res<-setLaw(density = dmyLaw)
# res@characteristic<-myChar
res@cdf <- pmyLaw
res@rng <- rmyLaw
res@quantile <- qmyLaw
return(res)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.law.r |
# setMethod("initialize", "model.parameter",
# function(.Object,
# all,
# common,
# diffusion,
# drift,
# jump,
# measure,
# xinit){
# .Object@all <- all
# .Object@common <- common
# .Object@diffusion <- diffusion
# .Object@drift <- drift
# .Object@jump <- jump
# .Object@measure <- measure
# .Object@xinit <- xinit
# return(.Object)
# })
setMethod("initialize", "model.parameter",
function(.Object,
all = character(),
common = character(),
diffusion = character(),
drift = character(),
jump = character(),
measure = character(),
xinit = character()){
.Object@all <- all
.Object@common <- common
.Object@diffusion <- diffusion
.Object@drift <- drift
.Object@jump <- jump
.Object@measure <- measure
.Object@xinit <- xinit
return(.Object)
})
# setMethod("initialize", "yuima.model",
# function(.Object,
# drift ,
# diffusion,
# hurst,
# jump.coeff,
# measure,
# measure.type,
# parameter,
# state.variable,
# jump.variable,
# time.variable,
# noise.number,
# equation.number,
# dimension,
# solve.variable,
# xinit,
# J.flag){
# .Object@drift <- drift
# .Object@diffusion <- diffusion
# .Object@hurst <- hurst
# [email protected] <- jump.coeff
# .Object@measure <- measure
# [email protected] <- measure.type
# .Object@parameter <- parameter
# [email protected] <- state.variable
# [email protected] <- jump.variable
# [email protected] <- time.variable
# [email protected] <- noise.number
# [email protected] <- equation.number
# .Object@dimension <- dimension
# [email protected] <- solve.variable
# .Object@xinit <- xinit
# [email protected] <- J.flag
# return(.Object)
# })
# 23/11 we need to provide the default values for the yuima.model object class
# in order to construct a new class that inherits from yuima.model class
setMethod("initialize", "yuima.model",
function(.Object,
drift = expression() ,
diffusion = list() ,
hurst = 0.5,
jump.coeff = list(),
#jump.coeff = expression(),
measure=list(),
measure.type=character(),
parameter = new("model.parameter"),
state.variable = "x",
jump.variable = "z",
time.variable = "t",
noise.number = numeric(),
equation.number = numeric(),
dimension = numeric(),
solve.variable = character(),
xinit = expression(),
J.flag = logical()){
.Object@drift <- drift
.Object@diffusion <- diffusion
.Object@hurst <- hurst
[email protected] <- jump.coeff
.Object@measure <- measure
[email protected] <- measure.type
.Object@parameter <- parameter
[email protected] <- state.variable
[email protected] <- jump.variable
[email protected] <- time.variable
[email protected] <- noise.number
[email protected] <- equation.number
.Object@dimension <- dimension
[email protected] <- solve.variable
.Object@xinit <- xinit
[email protected] <- J.flag
return(.Object)
})
## setModel
## setter of class 'yuima.model'
## set yuima model from SDE
setModel <- function(drift=NULL,
diffusion=NULL,
hurst=0.5,
jump.coeff=NULL,
measure=list(),
measure.type=character(),
state.variable="x",
jump.variable="z",
time.variable="t",
solve.variable,
xinit=NULL){
## we need a temp env for simplifications
mylengdumMeas<-length(measure)
if(mylengdumMeas>0){
for(i in c(1:mylengdumMeas)){
if(is(measure[[i]],"yuima.law")){
res<- aux.setModelLaw(drift,diffusion,
hurst, jump.coeff, measure, measure.type,
state.variable, jump.variable, time.variable,
solve.variable, xinit, posyuimalaw=i)
res@measure[[i]]<-measure[[i]]
return(res)
}
}
}
yuimaENV <- new.env()
##::measure and jump term #####################################
##::initialize objects ########
MEASURE <- list()
##::end initialize objects ########
##::make type function list ####
CPlist <- c("dnorm", "dgamma", "dexp", "dconst")
codelist <- c("rIG", "rNIG", "rgamma", "rbgamma", "rvgamma", "rstable","rpts","rnts")
## added "rpts" and "rnts" by YU (2016/10/4)
##::end make type function list ####
## Multivariate YUIMA model
if(!is.null(jump.coeff)){
if(is.matrix(jump.coeff)){
if(dim(jump.coeff)[2]!=1){
intensity <- NULL
#if(is.null(names(measure)) || names(measure)=="df"){
if(is.null(names(measure)) || all(names(measure)%in%"df")){
names(measure) <- "df"
}
df <- as.list(measure[["df"]])
if(any(measure.type=="CP")){
intensity <- measure[["intensity"]]
}
my.cond <- TRUE
tmp <- regexpr("\\(", measure$df)[1]
measurefunc <- substring(measure$df, 1, tmp-1)
if(!is.na(match(measurefunc, codelist))){
my.cond <- FALSE
}
if(my.cond){
res <- setMultiModel(drift = drift, diffusion = diffusion,
hurst = hurst, jump.coeff = jump.coeff,
intensity = intensity, df = df,
measure.type = measure.type, state.variable = state.variable,
jump.variable = jump.variable, time.variable = time.variable,
solve.variable = solve.variable, xinit= xinit)
return(res)
}
}
}
}
if(!length(measure.type)){
if( length(jump.coeff) || length(measure) ){
yuima.warn("measure type does not match with jump term.")
return(NULL)
}
jump.variable <- character()
measure.par <- character()
}else{
if( !length(jump.coeff) || !length(measure) ){
yuima.warn("measure type isn't matched with jump term.")
return(NULL)
# }else
# if(length(jump.coeff)!=1){
# yuima.warn("multi dimentional jump term is not supported yet.")
#
# return(NULL)
# }
} else if(measure.type=="CP"){ ##::CP
# if(length(measure)!=2){
# yuima.warn(paste("length of measure must be two on type", measure.type, "."))
# return(NULL)
#}
if(!is.list(measure)){
measure <- list(intensity=measure[1], df=measure[2],dimension=measure[3])
} else {
#if(length(measure[[1]])!=1 || length(measure[[2]])!=1){
# yuima.warn("multi dimentional jump term is not supported yet.")
# return(NULL)
# }
##::naming measure list ########
tmpc <- names(measure)
if(is.null(tmpc)){
names(measure) <- c("intensity", "df","dimension")
}else{
whichint <- match("intensity", tmpc)
whichdf <- match("df", tmpc)
if(!is.na(whichint)){
if(names(measure)[-whichint]=="" || names(measure)[-whichint]=="df"){
names(measure)[-whichint] <- "df"
}else{
yuima.warn("names of measure are incorrect.")
return(NULL)
}
}else if(!is.na(whichdf)){
if(names(measure)[-whichdf]=="" || names(measure)[-whichdf]=="intensity"){
names(measure)[-whichdf] <- "intensity"
}else{
yuima.warn("names of measure are incorrect.")
return(NULL)
}
}else{
yuima.warn("names of measure are incorrect.")
return(NULL)
}
}
##::end naming measure list ########
}
##::check df name ####################
tmp <- regexpr("\\(", measure$df)[1]
measurefunc <- substring(measure$df, 1, tmp-1)
if(!is.na(match(measurefunc, codelist))){
yuima.warn(paste("distribution function", measurefunc, "should be defined as type code."))
return(NULL)
}#else if(is.na(match(measurefunc, CPlist))){
# warning(paste("\ndistribution function", measurefunc, "is not officialy supported as type CP.\n"))
#}
MEASURE$df$func <- eval(parse(text=measurefunc)) #LM 15/05/2017
MEASURE$df$expr <- parse(text=measure$df)
MEASURE$intensity <- parse(text=measure$intensity)
measure.par <- unique( c( all.vars(MEASURE$intensity), all.vars(MEASURE$df$expr) ) )
##measure.par$intensity <- unique(all.vars(MEASURE$intensity))
##::end check df name ####################
##::end CP
} else if(measure.type=="code"){ ##::code
if(length(measure)!=1){
yuima.warn(paste("length of measure must be one on type", measure.type, "."))
return(NULL)
}
if(!is.list(measure)){
measure <- list(df=measure)
}else{
if(length(measure[[1]])!=1){
yuima.warn("multi dimentional jump term is not supported yet.")
return(NULL)
}
##::naming measure list #############
if(is.null(names(measure)) || names(measure)=="df"){
names(measure) <- "df"
}else{
yuima.warn("name of measure is incorrect.")
return(NULL)
}
##::end naming measure list #############
}
##::check df name ####################
tmp <- regexpr("\\(", measure$df)[1]
measurefunc <- substring(measure$df, 1, tmp-1)
if(!is.na(match(measurefunc, CPlist))){
yuima.warn(paste("\ndistribution function", measurefunc, "should be defined as type CP."))
return(NULL)
}else if(is.na(match(measurefunc, codelist))){
warning(paste("\ndistribution function", measurefunc, "is not officialy supported as type code.\n"))
}
##MEASURE$df$func <- eval(parse(text=measurefunc))
MEASURE$df$expr <- parse(text=measure$df)
measure.par <- unique(all.vars(MEASURE$df$expr))
##::end check df name ####################
##::end code
}else if(measure.type=="density"){ ##::density
if(length(measure)!=1){
yuima.warn(paste("length of measure must be one on type", measure.type, "."))
return(NULL)
}
if(!is.list(measure)){
measure <- list(df=measure)
}else{
if(length(measure[[1]])!=1){
yuima.warn("multi dimentional jump term is not supported yet.")
return(NULL)
}
##::naming measure list #############
if(is.null(names(measure))){
names(measure) <- "df"
}else if(names(measure)!="density" && names(measure)!="df"){
yuima.warn("name of measure is incorrect.")
return(NULL)
}
##::end naming measure list #############
}
##::check df name ####################
tmp <- regexpr("\\(", measure[[names(measure)]])[1]
measurefunc <- substring(measure[[names(measure)]], 1, tmp-1)
if(!is.na(match(measurefunc, CPlist))){
yuima.warn(paste("distribution function", measurefunc, "should be defined as type CP."))
return(NULL)
}else if(!is.na(match(measurefunc, codelist))){
yuima.warn(paste("distribution function", measurefunc, "should be defined as type code."))
return(NULL)
}
MEASURE[[names(measure)]]$func <- eval(parse(text=measurefunc))
MEASURE[[names(measure)]]$expr <- parse(text=measure[[names(measure)]])
measure.par <- unique(all.vars(MEASURE[[names(measure)]]$expr))
##::end check df name ####################
##::end density
}else{ ##::else
yuima.warn(paste("measure type", measure.type, "isn't supported."))
return(NULL)
}
n.eqn3 <- 1
n.jump <- 1
}
##::end measure and jump term #####################################
##:: check for errors and reform values
if(any(time.variable %in% state.variable)){
yuima.warn("time and state(s) variable must be different.")
return(NULL)
}
if(is.null(dim(drift))){ # this is a vector
n.eqn1 <- length(drift)
n.drf <- 1
}else{ # it is a matrix
n.eqn1 <- dim(drift)[1]
n.drf <- dim(drift)[2]
}
if(is.null(dim(diffusion))){ # this is a vector
n.eqn2 <- length(diffusion)
n.noise <- 1
}else{ # it is a matrix
n.eqn2 <- dim(diffusion)[1]
n.noise <- dim(diffusion)[2]
}
if(is.null(diffusion)){
diffusion <- rep("0", n.eqn1)
n.eqn2 <- n.eqn1
n.noise <- 1
}
## TBC
n.eqn3 <- n.eqn1
if(!length(measure)){
n.eqn3 <- n.eqn1
}
if(n.eqn1 != n.eqn2 || n.eqn1 != n.eqn3){
yuima.warn("Malformed model, number of equations in the drift and diffusion do not match.")
return(NULL)
}
n.eqn <- n.eqn1
if(is.null(xinit)){
# xinit <- numeric(n.eqn)
xinit <- character(n.eqn)
}else if(length(xinit) != n.eqn){
if(length(xinit)==1){
xinit <- rep(xinit, n.eqn)
}else{
yuima.warn("Dimension of xinit variables missmatch.")
return(NULL)
}
}
if(missing(solve.variable)){
yuima.warn("Solution variable (lhs) not specified. Trying to use state variables.")
solve.variable <- state.variable
}
if(n.eqn != length(solve.variable)){
yuima.warn("Malformed model, number of solution variables (lhs) do no match number of equations (rhs).")
return(NULL)
}
loc.drift <- matrix(drift, n.eqn, n.drf)
loc.diffusion <- matrix(diffusion, n.eqn, n.noise)
# Modification starting point 6/11
loc.xinit<-matrix(xinit,n.eqn,n.drf)
##:: allocate vectors
DRIFT <- vector(n.eqn, mode="expression")
DIFFUSION <- vector(n.eqn, mode="list")
# Modification starting point 6/11
XINIT<-vector(n.eqn, mode = "expression")
##:: function to make expression from drift characters
pre.proc <- function(x){
for(i in 1:length(x)){
if(length(parse(text=x[i]))==0){
x[i] <- "0"
}
}
parse(text=paste(sprintf("(%s)", x), collapse="+"))
}
##22/11:: function to simplify expression in drift, diffusion, jump and xinit characters
yuima.Simplifyobj<-function(x){
dummy<-yuima.Simplify(x, yuima.env=yuimaENV)
dummy1<-yuima.Simplify(dummy, yuima.env=yuimaENV)
dummy2<-as.character(dummy1)
res<-parse(text=paste0("(",dummy2,")",collapse=NULL))
return(res)
}
##:: make expressions of drifts and diffusions and jump
for(i in 1:n.eqn){
DRIFT[i] <- pre.proc(loc.drift[i,])
# 22/11 Simplify expressions
DRIFT[i] <- yuima.Simplifyobj(DRIFT[i])
# Modification starting point 6/11
XINIT[i]<-pre.proc(loc.xinit[i, ])
XINIT[i]<- yuima.Simplifyobj(XINIT[i])
for(j in 1:n.noise){
expr <- parse(text=loc.diffusion[i,j])
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
# DIFFUSION[[i]][j] <- expr
#22/11
DIFFUSION[[i]][j] <- yuima.Simplifyobj(expr)
}
#22/11
#if (length(JUMP)>0){
# JUMP[i] <- parse(text=jump.coeff[i])
# JUMP[i] <- yuima.Simplifyobj(JUMP[i])
#}
}
#print(length(jump.coeff))
#if (length(jump.coeff)==0){
# JUMP <- list(parse(text=jump.coeff))
#}else{
# # JUMP <- vector(n.eqn, mode="expression")
# JUMP <- vector(n.eqn, mode="list")
#}
if(length(jump.coeff)==0){
JUMP <- list()
} else {
if(length(jump.coeff)==1 & !is.matrix(jump.coeff)){ # is a scalar
expr <- parse(text=jump.coeff)
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
JUMP <- list(yuima.Simplifyobj(expr))
} else { # must be matrix, n.col = dimension of Levy noise
jump.coeff <- as.matrix(jump.coeff)
c.j <- ncol(jump.coeff)
r.j <- nrow(jump.coeff)
#print(c.j)
#print(r.j)
#print(jump.coeff)
JUMP <- vector(r.j, mode="list")
for(i in 1:r.j){
for(j in 1:c.j){
#cat(sprintf("\ni=%d,j=%d\n",i,j))
expr <- parse(text=jump.coeff[i,j])
if(length(expr)==0){
expr <- expression(0) # expr must have something
}
JUMP[[i]][j] <- yuima.Simplifyobj(expr)
}
}
}
}
#print(str(JUMP))
#
##:: get parameters in drift expression
drift.par <- unique(all.vars(DRIFT))
# Modification starting point 6/11
xinit.par <- unique(all.vars(XINIT))
drift.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), drift.par)))
if(length(drift.idx)>0){
drift.par <- drift.par[-drift.idx]
}
##:: get parameters in diffusion expression
diff.par <- unique(unlist(lapply(DIFFUSION, all.vars)))
diff.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), diff.par)))
if(length(diff.idx)>0){
diff.par <- diff.par[-diff.idx]
}
##:: get parameters in jump expression
J.flag <- FALSE
# jump.par <- unique(all.vars(JUMP))
jump.par <- unlist(lapply(JUMP,all.vars))
if(is.null(jump.par))
jump.par <- character()
if(length(na.omit(match(jump.par, jump.variable)))){
J.flag <- TRUE
}
jump.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), jump.par)))
if(length(jump.idx)>0){
jump.par <- jump.par[-jump.idx]
}
##:: get parameters in measure expression
measure.idx <- as.numeric(na.omit(match(c(state.variable, time.variable, jump.variable, solve.variable), measure.par)))
if(length(measure.idx)>0){
measure.par <- measure.par[-measure.idx]
}
##:: order parameters for 'yuima.pars'
##id1 <- which(diff.par %in% drift.par)
##id2 <- which(drift.par %in% diff.par)
##common <- unique(c(diff.par[id1], drift.par[id2]))
common <- c(drift.par, diff.par)
common <- common[duplicated(common)]
common1<-common
# modification 06/11 common1 contains only
# parameters that appear in both drift and diffusion terms.
# Modification 06/11 common contains only parameters that appear
# in drift, diff, Jump and xinit
if (length(xinit)) {
common <- c(common, xinit.par)
common <- common[duplicated(common)]
common <- c(common, xinit.par)
common <- common[duplicated(common)]
}
if(length(measure)){
common <- c(common, jump.par)
common <- common[duplicated(common)]
common <- c(common, measure.par)
common <- common[duplicated(common)]
}
# all.par <- unique(c(drift.par, diff.par, jump.par, measure.par))
all.par <- unique(c(drift.par, diff.par, jump.par, measure.par, xinit.par))
##:: instanciate class
tmppar <- new("model.parameter",
all= all.par,
# common= common,
common= common1,
diffusion= diff.par,
drift= drift.par,
jump= jump.par,
measure= measure.par,
xinit=xinit.par)
tmp <- new("yuima.model",
drift= DRIFT,
diffusion= DIFFUSION,
hurst=as.numeric(hurst),
jump.coeff=JUMP,
measure= MEASURE,
measure.type= measure.type,
parameter= tmppar,
state.variable= state.variable,
jump.variable= jump.variable,
time.variable= time.variable,
noise.number= n.noise,
equation.number= n.eqn,
dimension= c(
length(tmppar@all),
length(tmppar@common),
length(tmppar@diffusion),
length(tmppar@drift),
length(tmppar@jump),
length(tmppar@measure)
),
solve.variable= solve.variable,
xinit= XINIT,
J.flag <- J.flag)
return(tmp)
}
aux.setModelLaw <- function(drift,diffusion,
hurst, jump.coeff, measure, measure.type,
state.variable, jump.variable, time.variable,
solve.variable, xinit, posyuimalaw){
dummyMeasure <- paste0(c("yuima.law(",
paste0(measure[[posyuimalaw]]@param.measure,collapse=", ")
,")"), collapse="")
auxmeasure <- measure
auxmeasure[[posyuimalaw]]<-dummyMeasure
names(auxmeasure[posyuimalaw]) <- "df"
setModel(drift = drift,diffusion = diffusion,
hurst = hurst, jump.coeff = jump.coeff, measure = auxmeasure,
measure.type = measure.type,
state.variable = state.variable,
jump.variable = jump.variable, time.variable,
solve.variable, xinit)
}
# yuima.model rbind
# setGeneric("rbind.yuima",
# function(x, ...)
# standardGeneric("rbind.yuima")
# )
# setMethod("cbind.yuima", signature(x="yuima"),
# function(x, ...){
# ##:: init
# y.list <- list(x, ...)
# y.num <- length(y.list)
#
# ##:: bind yuima.data in yuima
#
# ##:: return result
# return(NULL)
# }
# )
# setMethod("rbind.yuima", signature(x="yuima.model"),
# function(x, ...){
# y.list <- list(x, ...)
# y.num <- length(y.list)
# res <- aux.rbind.model(y.list,y.num)
# return(res)
# }
# )
rbind.yuima.model <- function(x, ...){
y.list <- list(x, ...)
# y.list1 <- lapply(y.list, FUN = only.yuima.model)
y.num <- length(y.list)
new.list <- list()
for(i in (1:y.num)){
if(is(y.list[[i]],"yuima.model"))
new.list[i] <- y.list[[i]]
}
new.y.num <- length(new.list)
res <- aux.rbind.model(y.list = new.list,
y.num = new.y.num, mycall = y.list)
return(res)
}
aux.rbind.model<-function(y.list,y.num, mycall=list()){
lapply(y.list, FUN = check.yuima.model)
check.lev <- lapply(y.list, FUN = check.yuima.levy)
check.lev <- unlist(check.lev)
drift <- lapply(y.list, FUN = extract.model, type = "drift")
diffusion <- lapply(y.list, FUN = extract.model, type = "diffusion")
solve.variable <- lapply(y.list, FUN = extract.model, type = "solve.variable")
state.variable <- lapply(y.list, FUN = extract.model, type = "state.variable")
xinit <- lapply(y.list, FUN = extract.model, type = "xinit")
noise.number <- lapply(y.list, FUN = extract.model, type = "noise.number")
equation.number <- lapply(y.list, FUN = extract.model, type = "equation.number")
#Until Here only diffusion process
drift <- lapply(drift, FUN = ExpToString)
drift <- unlist(drift)
# drift
nrow.diff <- sum(unlist(equation.number))
ncol.diff <- sum(unlist(noise.number))
matr.diff <- matrix("0", nrow = nrow.diff, ncol = ncol.diff)
extrinf <- 1
extrsup <- noise.number[[1]]
j <- 1
cond.eq <- equation.number[[1]]
cond.eq1 <- 0
for(i in c(1:nrow.diff)){
if(i <= cond.eq){
dum <- ExpToString(diffusion[[j]][[i-cond.eq1]])
matr.diff[i,extrinf:extrsup] <- dum
if(i == equation.number[[j]]){
extrinf <- extrsup+1
j <- j+1
if(j <= nrow.diff){
extrsup <- extrsup + equation.number[[j]]
cond.eq1 <- i
cond.eq <- cond.eq + equation.number[[j]]
}
}
}
}
solve.variable <- lapply(solve.variable, FUN = ExpToString, cond = FALSE)
solve.variable <- unlist(solve.variable)
state.variable <- lapply(state.variable, FUN = ExpToString, cond = FALSE)
state.variable <- unlist(state.variable)
xinit <- lapply(xinit, FUN = ExpToString, cond = FALSE)
xinit <- unlist(xinit)
if(!any(check.lev)){
mod <- setModel(drift = drift, diffusion = matr.diff,
solve.variable = solve.variable, state.variable = state.variable,
xinit = xinit)
}else{
MultiLevy <- y.list[check.lev]
jump.coeff <- lapply(MultiLevy,
FUN = extract.model, type = "jump.coeff")
ncol.jump <- lapply(jump.coeff, FUN = numb.jump)
dum.ncolj <- unlist(ncol.jump)
ncol.jump <- sum(unlist(dum.ncolj))
jump.coeff <- lapply(y.list,
FUN = extract.model, type = "jump.coeff")
#ncol.jump1 <- lapply(jump.coeff, FUN = numb.jump)
matr.jump <- matrix("0",nrow = nrow.diff,
ncol = ncol.jump)
j <- 1
h <- 0
cond.eqa <- equation.number[[j]]
cond.eqb <- 0
extrinf <- 1
extrsup <- 1
if(check.lev[j])
extrsup <- dum.ncolj[j]
else{
h <- h+1
}
for(i in c(1:nrow.diff)){
if(i <= cond.eqa){
if(check.lev[j]){
dum <- ExpToString(jump.coeff[[j]][[i-cond.eqb]])
matr.jump[i, extrinf:extrsup] <- dum
}else{
# matr.jump[i,] <- matr.jump[i,]
}
if(i == cond.eqa){
cond.eqb <- i
j <- j+1
if(j<=length(equation.number))
cond.eqa <- cond.eqa + equation.number[[j]]
if(check.lev[j-1]){
extrinf <- extrsup + 1
extrsup <- extrsup + dum.ncolj[j-h]
}else{
extrinf <- extrinf
extrsup <- extrsup
h <- h+1
}
}
}
}
# mod <- matr.jump
# measure <- lapply(y.list,
# FUN = extract.model, type = "measure")
# measure
df <- NULL
if("df" %in% names(mycall))
df <- mycall$df
measure.type <- NULL
if("measure.type" %in% names(mycall))
measure.type <- mycall$measure.type
intensity <-NULL
if("intensity" %in% names(mycall))
intensity <- mycall$intensity
time.variable <- "t"
if("time.variable" %in% names(mycall))
time.variable <- mycall$time.variable
mod <- setMultiModel(drift=drift, diffusion = matr.diff,
jump.coeff = matr.jump, solve.variable = solve.variable,
xinit = xinit, time.variable = time.variable, df= df,
intensity = intensity, measure.type = measure.type)
}
return(mod)
}
# only.yuima.model<- function(y.list){
# if(is(y.list,"yuima.model")){
# return(y.list)
# }else{
# NULL
# }
# }
numb.jump <- function(x){length(x[[1]])}
check.yuima.levy <- function(x){
Levy <- FALSE
if(length([email protected])>0){
if(!is(x, "yuima.model")){
yuima.stop("the Levy model have to belong to the yuima.multimodel class")
}
Levy <- TRUE
}
return(Levy)
}
ExpToString <- function(x, cond = TRUE){
dum <- unlist(strsplit(toString(x),split=", "))
if(cond)
dum <- substr(dum, 2, nchar(dum)-1)
return(dum)
}
extract.model <- function(x, type = "drift"){
res<- slot(x,type)
return(res)
}
check.yuima.model <- function(x){
if(is.CARMA(x)){
yuima.warn("The cbind for CARMA will be implemented as soon as possible")
return(NULL)
}
if(is.COGARCH(x)){
yuima.warn("The cbind for COGARCH will be implemented as soon as possible")
return(NULL)
}
if(is.Poisson(x)){
yuima.warn("The cbind for Poisson will be implemented as soon as possible")
return(NULL)
}
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.model.R |
# general behaviour
# if grid is specified, the following are derived from it
# grid -> n, delta, Initial, Terminal, regular, random
# grid is ALWAYS a list, possibly of dimension 1
# if it is not a list, we transform it to a list
# if grid is 1-dim, no problem, but we can have more grids.
# in this case it is better to have a listI replace grid
# with alist
##Constructor and Initializer of class 'sampling'
# we convert objects to "zoo" internally
# to be fixed: the grid should always be prepared unless it is random sampling
# which.delta: check is grid is regular. If regular returns the delta, otherwise NA
which.delta <- function(x) ifelse(length(unique(round(diff(x),7)))==1, diff(x)[1], NA)
setMethod("initialize", "yuima.sampling",
function(.Object, Initial, Terminal, n, delta, grid, random,
regular, sdelta, sgrid, oindex, interpolation){
.Object@sdelta <- as.numeric(NULL)
.Object@sgrid <- as.numeric(NULL)
.Object@oindex <- as.numeric(NULL)
.Object@interpolation <- interpolation
# grid given
if(!is.null(grid)){
if(!is.list(grid)){
yuima.warn("attempting to coerce 'grid' to a list, unexpected results may occur!")
grid <- list(grid)
}
grid <- lapply(grid, sort) # we make sure grids are ordered
.Object@grid <- grid
.Object@Initial <- sapply(grid, min)
.Object@Terminal <- sapply(grid, max)
.Object@n <- sapply(grid, function(x) length(x))
.Object@random <- FALSE
.Object@delta <- as.numeric(sapply(grid, which.delta))
.Object@regular <- !any(is.na( .Object@delta ) )
return(.Object)
}
# grid is missing, but random sampling
if(!is.logical(random)){
.Object@regular <- FALSE
.Object@Terminal <- Terminal
.Object@Initial <- Initial
.Object@n <- numeric(0)
.Object@delta <- numeric(0)
.Object@random <- random
return(.Object)
}
# grid is missing, but non random sampling
nTerm <- 0
if(!missing(Terminal)) nTerm <- length(Terminal)
nInit <- 0
if(!missing(Initial)) nInit <- length(Initial)
nObs <- 0
if(!missing(n)) nObs <- length(n)
nDelta <- 0
if(!any(is.na(delta))) nDelta <- length(delta)
grid <- list()
# Initial + delta + n (+ Terminal: ignored) => Terminal
if(nInit>0 & nDelta>0 & nObs>0){
dims <- c(nInit, nDelta, nObs)
ndim <- dims[ which.max(dims) ]
Initial <- rep(Initial, ndim)[1:ndim]
delta <- rep(delta, ndim)[1:ndim]
n <- rep(n, ndim)[1:ndim]
Terminal <- Initial + n*delta
yuima.warn("'Terminal' (re)defined.")
for(i in 1:ndim)
grid[[i]] <- seq(Initial[i], Terminal[i], by=delta[i])
.Object@Terminal <- Terminal
.Object@Initial <- Initial
.Object@n <- n
.Object@delta <- delta
.Object@grid <- grid
.Object@random <- FALSE
.Object@regular <- TRUE
return(.Object)
}
# Initial + Terminal + n (+ delta: ignored) => delta
if(nInit>0 & nTerm>0 & nObs>0){
dims <- c(nInit, nTerm, nObs)
ndim <- dims[ which.max(dims) ]
Initial <- rep(Initial, ndim)[1:ndim]
Terminal <- rep(Terminal, ndim)[1:ndim]
if( any(Terminal < Initial))
stop("\nYUIMA: 'Terminal' < 'Initial'\n")
n <- as.integer(n)
n <- rep(n, ndim)[1:ndim]
delta <- (Terminal-Initial)/n
yuima.warn("'delta' (re)defined.")
for(i in 1:ndim)
grid[[i]] <- seq(Initial[i], Terminal[i], by=delta[i])
}
# Initial + delta + Terminal ( ignored) => n
if(nInit>0 & nTerm>0 & nDelta>0){
dims <- c(nInit, nTerm, nDelta)
ndim <- dims[ which.max(dims) ]
delta <- rep(delta, ndim)[1:ndim]
Initial <- rep(Initial, ndim)[1:ndim]
Terminal <- rep(Terminal, ndim)[1:ndim]
if( any(Terminal < Initial))
stop("\nYUIMA: 'Terminal' < 'Initial'\n")
n <- as.integer((Terminal-Initial)/delta)
n <- rep(n, ndim)[1:ndim]
yuima.warn("'n' (re)defined.")
for(i in 1:ndim)
grid[[i]] <- seq(Initial[i], Terminal[i], by=delta[i])
}
.Object@Terminal <- Terminal
.Object@Initial <- Initial
.Object@n <- n
.Object@delta <- delta
.Object@grid <- grid
.Object@random <- FALSE
.Object@regular <- TRUE
return(.Object)
})
setSampling <- function(Initial=0, Terminal=1, n=100, delta,
grid, random=FALSE, sdelta=as.numeric(NULL),
sgrid=as.numeric(NULL), interpolation="pt" ){
if(missing(delta)) delta <- NA
if(missing(grid)) grid <- NULL
return(new("yuima.sampling", Initial=Initial, Terminal=Terminal,
n=n, delta=delta, grid=grid, random=random,
regular=TRUE, sdelta=sdelta, sgrid=sgrid,
interpolation=interpolation))
}
#setMethod("initialize", "yuima.sampling",
#function(.Object, Initial, Terminal, n, delta, grid, random,
#regular, sdelta, sgrid, oindex, interpolation){
# .Object@sdelta <- as.numeric(NULL)
# .Object@sgrid <- as.numeric(NULL)
# .Object@oindex <- as.numeric(NULL)
# .Object@interpolation <- interpolation
## grid given
# if(length(grid)>0){
# testInitial<-(min(grid)==Initial)
# testTerminal<-(max(grid)==Terminal)
# testn<-(abs(n-diff(range(grid))/mean(diff(grid))+1)<10^(-10))
# testdelta<-(abs(delta-mean(diff(grid)))<10^(-10))
# testregular<-all(abs(diff(diff(grid)))<10^(-10))
#
# if(!testInitial){
# cat("\n Start time has been set with the grid \n")
# }
# if(!testTerminal){
# cat("\n Terminal time has been set with the grid \n")
# }
# if(!testn){
# cat("\n Division has been set with the grid \n")
# }
# if(!testdelta){
# cat("\n delta has been set with the grid \n")
# }
# if(testregular){
# .Object@n <- diff(range(grid))/mean(diff(grid))+1
# .Object@delta <- mean(diff(grid))
# .Object@regular <- TRUE
# }else{
# .Object@n <- length(grid)-1
# .Object@delta <- as.numeric(NULL)
# .Object@regular <- FALSE
# }
# .Object@grid <- grid
# .Object@Initial <- min(grid)
# .Object@Terminal <- max(grid)
# .Object@random <- random
# }else{
## There is no grid
# eqn <- length(Terminal)
# if(length(Terminal)==length(n)){
# .Object@Initial <- Initial
# .Object@Terminal <- Terminal
# .Object@n <- n
# .Object@delta <- (Terminal-Initial)/n
# .Object@grid <- seq(Initial,Terminal,by=.Object@delta)
# .Object@random <- FALSE
# .Object@regular <- regular
# }else if(length(Terminal)==1){
# .Object@Initial <- Initial
# .Object@Terminal <- rep(Terminal, length(n))
# .Object@n <- n
# .Object@delta <- (Terminal-Initial)/n
# .Object@grid <- seq(Initial,Terminal,by=.Object@delta)
# .Object@random <- FALSE
# .Object@regular <- regular
# }else if(length(n)==1){
# .Object@Initial <- Initial
# .Object@Terminal <- Terminal
# .Object@n <- rep(n, length(Terminal))
# .Object@delta <- (Terminal-Initial)/n
# .Object@grid <- seq(Initial,Terminal,by=.Object@delta)
# .Object@random <- FALSE
# .Object@regular <- regular
# }else{
# cat("\nDimension missmatch.\n")
# return(NULL)
# }}
# return(.Object)
#})
# | /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima.sampling.R |
setClass("yuima.th", slots=c(method = "character",
up = "numeric",
low = "numeric",
N = "numeric",
N_grid = "numeric",
regular_par = "ANY",
h = "numeric",
Dof = "character"),
contains= "yuima.law")
setMethod("initialize", "yuima.th",
function(.Object, h=1, method="LAG", up=7, low=-7,
N=180, N_grid=1000, regular_par=NULL){
.Object@density<- dtLevy
.Object@cdf<- ptLevy
.Object@quantile<- qtLevy
.Object@rng<- rtLevy
.Object@method <- method
.Object@up <- up
.Object@low <- low
.Object@N <- N
.Object@N_grid <- N_grid
.Object@regular_par <- regular_par
.Object@h <- h
.Object@Dof <- "nu"
[email protected] <- "nu"
# validObject(.Object)
return(.Object)
}
)
setLaw_th <- function(h=1,method="LAG", up=7, low=-7,
N=180, N_grid=1000, regular_par=NULL, ...) {
initialize(new("yuima.th"), h=h, method=method, up=up, low=low,
N=N, N_grid=N_grid, regular_par=regular_par, ...)
}
setMethod("dens", "yuima.th",
function(object, x, param, log = FALSE, ...){
if(is.list(param)){
nu <- unlist(param)[[email protected]]
}else{
nu <- param[[email protected]]
}
res <- object@density(x = x, nu = nu, h = object@h, method = object@method,
up = object@up, low = object@low, N=object@N,
N_grid = object@N_grid, regular_par = object@regular_par)
if(log){
res <- log(res)
}
return(res)
}
)
setMethod("cdf", "yuima.th",
function(object, q, param, ...){
if(is.list(param)){
nu <- unlist(param)[[email protected]]
}else{
nu <- param[[email protected]]
}
res <- object@cdf(q, nu = nu, h = object@h, method = object@method,
up = object@up, low = object@low, N=object@N,
N_grid = object@N_grid, regular_par = object@regular_par)
return(res)
}
)
setMethod("quant", "yuima.th",
function(object, p, param, ...){
if(is.list(param)){
nu <- unlist(param)[[email protected]]
}else{
nu <- param[[email protected]]
}
res <- object@quantile(p, nu = nu, h = object@h, method = object@method,
up = object@up, low = object@low, N=object@N,
N_grid = object@N_grid, regular_par = object@regular_par)
return(res)
}
)
setMethod("rand", "yuima.th",
function(object, n, param, ...){
if(is.list(param)){
nu <- unlist(param)[[email protected]]
}else{
nu <- param[[email protected]]
}
res <- numeric(length = n)
res <- object@rng(n, nu = nu, h = object@h, method = object@method,
up = object@up, low = object@low, N=object@N,
N_grid = object@N_grid, regular_par = object@regular_par)
return(res)
}
)
setClass("yuima.LevyRM",
slots=c(unit_Levy ="yuima.th",
regressors ="character",
LevyRM = "character",
paramRM = "character",
paramAll = "character",
solve.varRM = "character",
coeff = "character"),
contains = "yuima")
setMethod("initialize", "yuima.LevyRM",
function(.Object, unit_Levy, yuima_regressors, LevyRM="Y", coeff=c("mu","sigma0"),
data, sampling, characteristic, functional ){
.Object@coeff <- c(paste0(coeff[1],1:length([email protected])),coeff[2])
.Object@paramRM <- c(.Object@coeff,[email protected])
.Object@paramAll <- c(yuima_regressors@parameter@all, .Object@paramRM)
[email protected] <- c([email protected],LevyRM)
.Object@LevyRM <- LevyRM
.Object@regressors <- [email protected]
.Object@unit_Levy <- unit_Levy
test <- new("yuima", data = data, model = yuima_regressors, sampling = sampling,
characteristic = characteristic, functional = functional)
.Object@data <- test@data
.Object@sampling <- test@sampling
.Object@model <- yuima_regressors
.Object@characteristic <- test@characteristic
.Object@functional <- test@functional
#validObject(.Object)
return(.Object)
}
)
setLRM <- function(unit_Levy, yuima_regressors, LevyRM="Y", coeff=c("mu","sigma0"),
data=NULL, sampling = NULL, characteristic = NULL,
functional = NULL, ...) {
new("yuima.LevyRM", unit_Levy=unit_Levy, yuima_regressors=yuima_regressors,
LevyRM = LevyRM, coeff = coeff, data = data, sampling = sampling,
characteristic = characteristic, functional = functional)
}
#setMethod("simulate","yuima.LevyRM",
aux.simulateLevyRM <- function(object, nsim=1, seed=NULL, xinit, true.parameter, space.discretized = FALSE,
increment.W = NULL, increment.L = NULL, method = "euler", hurst, methodfGn = "WoodChan",
sampling=sampling, subsampling=subsampling, ...){
Samp <- sampling
param <- true.parameter
if(length(param[object@model@parameter@all])==0){
traj <- simulate(object = object@model, nsim=nsim, seed=seed,
xinit=xinit, space.discretized = space.discretized,
increment.W = increment.W, increment.L = increment.L,
method = method, hurst=hurst, methodfGn = methodfGn,
sampling=Samp, subsampling=subsampling)
}else{
traj <- simulate(object = object@model, nsim=nsim, seed=seed,
true.parameter = param[object@model@parameter@all],
xinit=xinit, space.discretized = space.discretized,
increment.W = increment.W, increment.L = increment.L,
method = method, hurst=hurst, methodfGn = methodfGn,
sampling=Samp, subsampling=subsampling)
}
myint <- object@unit_Levy
myint@h <- sampling@delta
param <- unlist(param)
# defaultW <- getOption("warn")
# options(warn = -1)
deltaLt <- rand(myint,n=sampling@n,param[[email protected]])
process <- c(0,cumsum(deltaLt))
test<- traj@[email protected]%*%param[object@paramRM[1:length(object@regressors)]]
test<- test+param[object@paramRM[length(object@regressors)+1]]*process/sqrt(param[[email protected]])
#plot(x=sampling@grid[[1]],y=as.numeric(test),type="l")
res<-cbind(traj@[email protected],test)
colnames(res) <- [email protected]
mydata <- setData(as.matrix(res),delta=sampling@delta, t0=sampling@Initial)
object@data <- mydata
object@unit_Levy@h<-1
# res<-setLRM(object@unit_Levy, object@yuima_regressors, LevyRM="Y", coeff=c("mu","sigma0"),
# data=mydata, sampling = Samp, characteristic = characteristic,
# functional = functional)
# res<-new("yuima.LevyRM", unit_Levy=object@unit_Levy,
# yuima_regressors=object@yuima_regressors,
# LevyRM = object@LevyRM, coeff = c("mu","sigma"),
# data = mydata, sampling = Samp,
# characteristic = object@characteristic, functional = object@functional)
return(object)
}
#)
estimation_LRM <- function(start, model, data, upper, lower, PT=500, n_obs1=NULL){
mydata <- data
if(is.null(n_obs1)){
n_obs1 <- floor((dim([email protected])[1]-1)/diff(range(index([email protected]))))
}
Term <- tail(index([email protected][[1]]),1L)
NofW <- n_obs1*Term # the number of the whole data
m <- n_obs1*PT # the number of the data for the estimation of mu and sigma
labY <- model@LevyRM
Y <- as.numeric([email protected][,labY])
regrlab <- model@regressors
X <- as.matrix([email protected][,regrlab])
dY <- Y[2:NofW] - Y[1:(NofW-1)]
dX <- X[2:NofW,] - X[1:(NofW-1),]
# first stage estimation
# minus Cauchy quasi-likelihood
h<-1/n_obs1
day <- n_obs1
Term <- floor((dim([email protected])[1]-1)*h)
if(length(regrlab)==1){
mcql <- function(par, model, h, m){
mu <- par[model@paramRM[1:length(model@regressors)]]
sigma <- par[model@paramRM[length(model@regressors)+1]]
#sum(log(sigma) + log(1 + (dY[1:m] - mu1*dX1[1:m] - mu2*dX2[1:m])^2/(h*sigma)^2))
sum(log(sigma) + log(1 + (dY[1:m] - dX[1:m]*mu)^2/(h*sigma)^2))
}
}else{
mcql <- function(par, model, h, m){
mu <- par[model@paramRM[1:length(model@regressors)]]
sigma <- par[model@paramRM[length(model@regressors)+1]]
#sum(log(sigma) + log(1 + (dY[1:m] - mu1*dX1[1:m] - mu2*dX2[1:m])^2/(h*sigma)^2))
sum(log(sigma) + log(1 + (dY[1:m] - dX[1:m,]%*%mu)^2/(h*sigma)^2))
}
}
# estimation of mu and sigma (partial data)
fres <- optim(par = unlist(start), fn = mcql, lower = lower, upper = upper,
method = "L-BFGS-B", model=model, h=h, m=m)
esig <- fres$par[model@paramRM[length(model@regressors)+1]]
emu <- fres$par[model@paramRM[1:length(model@regressors)]]
# second stage estimation
# unit-time increments
# if(is.null(data)){
# Term <- floor(tail(index(model@[email protected][[1]]),1L))
# }else{
# Term <- floor(tail(index([email protected][[1]]),1L))
# }
duY <- numeric(Term)
duX <- matrix(NA, Term, length(model@regressors))
#duX2 <- numeric(Term)
duY[1] <- Y[day]
duX[1,] <- X[day,]
for(i in 2:Term){
duY[i] <- Y[i*day] - Y[(i-1)*day + 1]
duX[i,] <- X[i*day,] - X[(i-1)*day + 1,]
}
# unit-time residuals
ures <- numeric(Term)
for(i in 1:Term){
ures[i] <- esig^(-1)*(duY[i] - duX[i,]%*%emu )
}
# hres <- numeric(m)
# for(i in 1:m){
# hres[i] <- esig^(-1)/h*(dY[i] - dX[i,]%*%emu)
# }
# minus (scaled) student-t likelihood function (unit time)
stl <- function(nu){
sum(-log(gamma((nu + 1)/2)) + log(gamma(nu/2)) + (nu + 1)/2*log(1 + (ures)^2))
#-sum(log(dt(ures, nu)))
}
#sres <- optimize(stl, c(0.01, 10))
sres<-optim(1,fn=stl,method="Brent",upper=100,lower=0.00001)
enu <- sres$par
names(enu)<-"nu"
est<-c(emu, esig, enu)
dFPos <- length(model@paramRM)
#
scalepos <- length(model@paramRM)-1
mycoef_a <- est[model@paramRM[-c(scalepos,dFPos)]]
X_data <- [email protected][,model@regressors]
if(length(model@regressors)==1){
VarX_data<- as.matrix(diff(X_data))
}else{
VarX_data<-apply(X_data,2,"diff")
}
#Nn <- dim(VarX_data)[1]
Sn <- as.matrix(VarX_data[1,])%*%t(as.matrix(VarX_data[1,]))
#m <- n_obs1*PT
for(i in c(2:m)){
Sn <- Sn+as.matrix(VarX_data[i,])%*%t(as.matrix(VarX_data[i,]))
}
Sn <- 1/(h^2*m)*Sn
esig <- est[model@paramRM[scalepos]]
GAM_a <- matrix(0, dim(Sn)[1]+1,dim(Sn)[2]+1)
GAM_a[1:dim(Sn)[1],1:dim(Sn)[1]]<-Sn/(2*esig^2)
GAM_a[dim(Sn)[1]+1,dim(Sn)[2]+1] <- 1/(2*esig^2)
Vcov1 <- 1/m*solve(GAM_a)
enu <- est[model@paramRM[dFPos]]
GAM_nu <- 1/4*(trigamma(enu/2)-trigamma((enu+1)/2))
Vcov2 <- 1/Term*solve(GAM_nu)
Vcov <- matrix(0 , dim(Vcov1)[1]+1,dim(Vcov1)[2]+1)
Vcov[1:dim(Vcov1)[1],1:dim(Vcov1)[1]]<- Vcov1
Vcov[dim(Vcov1)[1]+1,dim(Vcov1)[1]+1]<- Vcov2
call <- match.call()
colnames(Vcov)<-names(est)
rownames(Vcov)<-names(est)
min <- c(fres$value,sres$value)
final_res <- new("yuima.qmle", call = call, coef = est, fullcoef = est,
vcov = Vcov, min = min, details = list(), minuslogl = function(){NULL},
method = "L-BFGS-B", nobs=as.integer(NofW), model=model@model)
#return(list(est=est,vcov=Vcov))
return(final_res)
} | /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima_t_regression_model_new_class.R |
# Algorithms for the density of the increment of a L\'evy t - student process
## Internal functions:
### internal characteristic function
phi_int <- function(t,lambda,v){
if(t==0){
return(1)
}else{
t=1i*t
my<-(sqrt(v)*abs(t))^lambda*besselK(sqrt(v)*abs(t),lambda)/(gamma(lambda)*2^(lambda-1))
return(my)
}
}
phi <- Vectorize(phi_int, vectorize.args = "t")
### FFT internal function
Inter_dens <-function (n, nu, h, alim, blim) {
v=nu
lambda = nu/2
i <- 0:(n - 1)
dx <- (blim - alim)/n
x <- alim + i * dx
dt <- 2 * pi/(n * dx)
c <- -n/2 * dt
d <- n/2 * dt
t <- c + i * dt
phi_t <- phi(t,lambda,v)^h
X <- exp(-(0 + (0+1i)) * i * dt * alim) * phi_t
Y <- fft(X)
density <- dt/(2 * pi) * exp(-(0 + (0+1i)) * c * x) * Y
data.frame(i = i, t = t, characteristic_function = phi_t,
x = x, density = Re(density))
}
Internal_FTT_dens<-function (x, nu, h, N = 2^10) {
# if (length(x) == 1) {
# alim <- min((-abs(x) - 0.5), setInf)
# blim <- max((abs(x) + 0.5), setSup)
# }
# else {
# xdummy <- na.omit(x[is.finite(x)])
# alim <- min(min(xdummy) - 0.1, setInf)
# blim <- max(max(xdummy) + 0.1, setSup)
# }
alim <- min(x)
blim <- max(x)
invFFT <-Inter_dens(n=N, nu=nu, h=h, alim = alim, blim = blim)
dens <- spline(invFFT$x, invFFT$density, xout=x)
return(dens$y)
}
### COS internal function
inter_fun_cos1 <- function(x,FK,k,low,Dt,w){
C_fun <- cos(k*pi*(x-low)/Dt)
f <- sum(w*FK*C_fun)
return(f)
}
inter_fun_cos_vect <- Vectorize(FUN=inter_fun_cos1,vectorize.args ="x")
cos_method_dens_t <-function(pts, nu, h, N=100){
Dt <- diff(range(pts))
k <- c(0:N)
w <- c(0.5,rep(1,N))
char_h <- phi(t=k*pi/Dt,lambda=nu/2,v=nu)^h
low <- min(pts)
FK <- 2/Dt*Re(char_h*exp(-1i*k*pi*low/Dt))
dens <- inter_fun_cos_vect(x=pts,FK=FK,k=k, low=low,Dt=Dt,w=w)
return(dens)
}
### Laguerre
Internal_LAG_dens <-function(pts,nu,h=1, n=40){
# without tail approx
#if(is.null(regular_par)){
x<- pts/sqrt(nu)
alpha <- nu*h/2
out <- gauss.quad(n,"laguerre",alpha=alpha)
nodes<-out$nodes
myw_1<-out$weights*exp(out$nodes)*(besselK(out$nodes,nu/2))^h
arg <- cos(as.matrix(nodes)%*%t(x))
dens <-((2^(1-nu/2)/gamma(nu/2))^h)/(pi)*t(myw_1)%*%arg
dens <- as.numeric(dens)/sqrt(nu)
return(dens)
}
intern_dens <- function(mydens,x,nu,sigma=1,h=1, n=40){
dens<-mydens
pos_low<-tail(which(dens<0 & x<=0),1L)+1
pos_up<-which(dens<0 & x>0)[1]-1
if(is.na(pos_low)||is.infinite(pos_low)||length(pos_low)==0){
pos_low<-0
}
if(is.na(pos_up)){
pos_up <- +Inf
}
B_nu <- h*gamma(0.5*nu+0.5)/(sqrt(pi)*gamma(0.5*nu))
expnu <- -1/2*(nu+1)
if(pos_low>1&pos_up<length(x)){
dens_low <- B_nu*(1+x[1:pos_low]^2)^expnu #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
dens_up <- B_nu*(1+x[pos_up:length(x)]^2)^expnu # beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[pos_up:length(stddata)])^(1+beta)*pi)
dens <- c(dens_low,dens[(pos_low+1):(pos_up-1)],dens_up)
}else{
if(pos_low>1&!pos_up<length(x)){
dens_low <- B_nu*(1+x[1:pos_low]^2)^expnu #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
dens <- c(dens_low,dens[(pos_low+1):length(x)])
}
if(!pos_low>1&pos_up<length(x)){
dens_up <- B_nu*(1+x[pos_up:length(x)]^2)^expnu
dens <- c(dens[1:(pos_up-1)],dens_up)
}
if(!pos_low>1&!pos_up<length(x)){
dens<-dens
}
}
return(dens)
}
mydtlevy_1vers <-function(x,nu,sigma=1,h=1, n=40){
# without tail approx
#if(is.null(regular_par)){
alpha <- nu*h/2
out <- gauss.quad(n,"laguerre",alpha=alpha)
nodes<-out$nodes
myw_1<-out$weights*exp(out$nodes)*(besselK(out$nodes,nu/2))^h
int <- as.matrix(nodes)%*%t(x/sigma)
#arg <- matrix(0,dim(int)[1],dim(int)[2])
a <- cos(int)
dens <-((2^(1-nu/2)/gamma(nu/2))^h)/(pi*sigma)*t(myw_1)%*%a
dens <- as.numeric(dens)
return(dens)
}
mydtlevy_4vers<- function(x,nu,sigma,h, n, regular_par,ImprovGibbs){
if(is.null(regular_par)){
L<-0
}else{
L<-regular_par
}
if(length(x)>1){
oldx <- x
x<-sort(unique(x))
dens <- as.numeric(mydtlevy_1vers(x,nu,sigma,h, n))
pos_low<-max(tail(which(diff(dens)<0 & x[-1]<=0),1L), tail(which(dens<0 & x<=0),1L))+1
pos_up<-min(which(diff(dens)>0 & x[-1]>0)[1],which(dens<0 & x>0)[1])-1
if(is.na(pos_low)){
pos_low<-0
}
if(is.na(pos_up)){
pos_up <- +Inf
}
if(pos_low>1&pos_up<length(x)){
x_dow <- x[1:pos_low]
dens_low <- (as.numeric(mydtlevy_1vers(x_dow-L,nu,sigma,h, n))+as.numeric(mydtlevy_1vers(x_dow+L,nu,sigma,h, n)))/2 #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
x_up <- x[pos_up:length(x)]
dens_up <- (as.numeric(mydtlevy_1vers(x_up-L,nu,sigma,h, n))+as.numeric(mydtlevy_1vers(x_up+L,nu,sigma,h, n)))/2
dens <- c(dens_low,dens[(pos_low+1):(pos_up-1)],dens_up)
if(ImprovGibbs){
dens<-intern_dens(dens,x,nu,sigma,h, n)
}
}else{
if(pos_low>1&!pos_up<length(x)){
x_dow <- x[1:pos_low]
dens_low <- (as.numeric(mydtlevy_1vers(x_dow-L,nu,sigma,h, n))+as.numeric(mydtlevy_1vers(x_dow+L,nu,sigma,h, n)))/2 #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
dens <- c(dens_low,dens[(pos_low+1):length(x)])
if(ImprovGibbs){
dens<-intern_dens(dens,x,nu,sigma,h, n)
}
}
if(!pos_low>1&pos_up<length(x)){
x_up <- x[pos_up:length(x)]
dens_up <- (as.numeric(mydtlevy_1vers(x_up-L,nu,sigma,h, n))+as.numeric(mydtlevy_1vers(x_up+L,nu,sigma,h, n)))/2
dens <- c(dens[1:(pos_up-1)],dens_up)
if(ImprovGibbs){
dens<-intern_dens(dens,x,nu,sigma,h, n)
}
}
if(!pos_low>1&!pos_up<length(x)){
dens<-dens
}
}
pos <- match(oldx,x)
dens <- dens[pos]
}else{
dens <- as.numeric(mydtlevy_1vers(x,nu,sigma,h, n))
if(dens<0){
B_nu <- h*gamma(0.5*nu+0.5)/(sqrt(pi)*gamma(0.5*nu))
expnu <- -1/2*(nu+1)
dens <- B_nu*(1+x^2)^expnu
}
}
}
rt_Levy<-function(n,nu=1,h=1,n_laguerre=180,up=7,low=-7,
N_grid=500001,
regular_par=NULL,ImprovGibbs=TRUE){
xin<- seq(low,up,length.out=N_grid)
dens_g <- mydtlevy_4vers(xin/sqrt(nu),nu=nu,sigma=1,h=h, n=n_laguerre,
regular_par=regular_par,
ImprovGibbs=ImprovGibbs)/sqrt(nu)
cond <- xin>=low & xin<=up
cdf_g <- cumsum(dens_g*diff(xin)[1])
cdf_g0<-cdf_g[cond]
cond_cdf<-(cdf_g0-cdf_g0[1])/(tail(cdf_g0,1L)-cdf_g0[1])
U=runif(n)
# res<-as.numeric(approx(x=cond_cdf,
# y=xin[cond],xout=U,method="constant",f=1)$y)
res<-as.numeric(spline(x=cond_cdf,
y=xin[cond],xout=U)$y)
return(res)
}
## NO_Approx
tdens_noapprox <- function(x, nu, h, method="COS", N = 2^7){
#pts <- sort(unique(c(x,seq(low,up,length.out=N_grid))))
if(method=="COS"){
dens0<- cos_method_dens_t(pts=x, nu=nu, h=h, N=N)
}else{
if(method=="FFT"){
dens0 <- Internal_FTT_dens(x=x, nu=nu, h=h, N = N)
}else{
if(method=="LAG"){
dens0 <- Internal_LAG_dens(pts=x,nu=nu,h=h, n=N)
}else{
yuima.stop("method must be either 'COS' or 'FFT' or 'LAG')")
}
}
}
#pos <- match(x,pts)
#mydens <- dens0[pos]
return(dens0)
}
## Tail approx
approx_dens1 <- function(mydens,x,nu,h){
dens<-mydens
pos_low<-tail(which(dens<0 & x<=0),1L)+1
pos_up<-which(dens<0 & x>0)[1]-1
# pos_low<-max(tail(which(diff(dens)<0 & x[-1]<=0),1L), tail(which(dens<0 & x<=0),1L))+1
# pos_up<-min(which(diff(dens)>0 & x[-1]>0)[1],which(dens<0 & x>0)[1])-1
#
if(is.na(pos_low)||is.infinite(pos_low)||length(pos_low)==0){
pos_low<-0
}
if(is.na(pos_up)){
pos_up <- +Inf
}
B_nu <- h*gamma(0.5*nu+0.5)/(sqrt(pi)*gamma(0.5*nu))
expnu <- -1/2*(nu+1)
if(pos_low>1&pos_up<length(x)){
dens_low <- B_nu*(1+x[1:pos_low]^2)^expnu #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
dens_up <- B_nu*(1+x[pos_up:length(x)]^2)^expnu # beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[pos_up:length(stddata)])^(1+beta)*pi)
dens <- c(dens_low,dens[(pos_low+1):(pos_up-1)],dens_up)
}else{
if(pos_low>1&!pos_up<length(x)){
dens_low <- B_nu*(1+x[1:pos_low]^2)^expnu #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
dens <- c(dens_low,dens[(pos_low+1):length(x)])
}
if(!pos_low>1&pos_up<length(x)){
dens_up <- B_nu*(1+x[pos_up:length(x)]^2)^expnu
dens <- c(dens[1:(pos_up-1)],dens_up)
}
if(!pos_low>1&!pos_up<length(x)){
dens<-dens
}
}
return(dens)
}
# Inter_mytail2<-function(dens,x,pos, pos2,C_nu){
# t_low <- 1/(1+x[pos]^2)
# t_low_inc <- 1/(1+x[pos2]^2)
# f<- dens[pos]
# delta_t_low <- t_low-t_low_inc
# delta_f_low <- (f-dens[pos2])/(t_low-t_low_inc)
# C_nu_low <- C_nu*t_low^(0.5*nu+.5)
# Mat_low <- matrix(c(2*t_low,-1,-t_low^2,t_low),2,2)/(C_nu_low*t_low^2)
# Coeff_low <- Mat_low%*%(c(f,delta_f_low)-c(C_nu_low,1/2*(nu+1)*t_low^(-1)*f))
# return(Coeff_low)
# }
#
#
# Inter_mytail<-function(dens,x,pos,C_nu){
# t_low <- 1/(1+x[pos]^2)
# f<- dens[pos]
# C_nu_low <- C_nu*t_low^(0.5*nu+.5)
# Coeff_low <- (f/C_nu_low-1)/t_low
# return(Coeff_low)
# }
#
#
# approx_dens2 <- function(mydens,x,nu,h){
# dens<-mydens
# pos_low<-tail(which(dens<0 & x<=0),1L)+1
# pos_up<-which(dens<0 & x>0)[1]-1
# # pos_low<-max(tail(which(diff(dens)<0 & x[-1]<=0),1L), tail(which(dens<0 & x<=0),1L))+1
# # pos_up<-min(which(diff(dens)>0 & x[-1]>0)[1],which(dens<0 & x>0)[1])-1
#
# if(is.na(pos_low)||is.infinite(pos_low)||length(pos_low)==0){
# pos_low<-0
# }
# if(is.na(pos_up)){
# pos_up <- +Inf
# }
# C_nu<- h*gamma(0.5*nu+0.5)/(sqrt(pi)*gamma(0.5*nu))
# if(pos_low>1&pos_up<length(x)){
# #pos_low2<-pos_low+1
# #Coeff_low <- Inter_mytail(dens=dens,x=x,pos=pos_low, pos2=pos_low2,C_nu=C_nu)
# Coeff_low <- Inter_mytail(dens=dens,x=x,pos=pos_low,C_nu=C_nu)
# t_low <- 1/(1+x[1:pos_low]^2)
# #dens_low<- C_nu*t_low^(0.5*nu+0.5)*(1+Coeff_low[1,1]*t_low+Coeff_low[2,1]*t_low^2)
# dens_low<- C_nu*t_low^(0.5*nu+0.5)*(1+Coeff_low*t_low)
#
# # pos_up2<-pos_up-1
# # Coeff_up <- Inter_mytail(dens=dens,x=x,pos=pos_up, pos2=pos_up2,C_nu=C_nu)
# Coeff_up <- Inter_mytail(dens=dens,x=x,pos=pos_up,C_nu=C_nu)
# t_up <- 1/(1+x[pos_up:length(x)]^2)
# #dens_up<- C_nu*t_up^(0.5*nu+0.5)*(1+Coeff_up[1,1]*t_up+Coeff_up[2,1]*t_up^2)
# dens_up<- C_nu*t_up^(0.5*nu+0.5)*(1+Coeff_up*t_up)
# dens <- c(dens_low,dens[(pos_low+1):(pos_up-1)],dens_up)
# }else{
# if(pos_low>1&!pos_up<length(x)){
# #dens_low <- B_nu*(1+x[1:pos_low]^2)^expnu #beta*sin(beta*pi/(2))*gamma(beta)/(abs(stddata[1:pos_low])^(1+beta)*pi)
# # pos_low2<-pos_low+1
# # Coeff_low <- Inter_mytail(dens=dens,x=x,pos=pos_low, pos2=pos_low2,C_nu=C_nu)
# Coeff_low <- Inter_mytail(dens=dens,x=x,pos=pos_low, C_nu=C_nu)
# t_low <- 1/(1+x[1:pos_low]^2)
# #dens_low<- C_nu*t_low^(0.5*nu+0.5)*(1+Coeff_low[1,1]*t_low+Coeff_low[2,1]*t_low^2)
# dens_low<- C_nu*t_low^(0.5*nu+0.5)*(1+Coeff_low*t_low)
# dens <- c(dens_low,dens[(pos_low+1):length(x)])
# }
# if(!pos_low>1&pos_up<length(x)){
# #dens_up <- B_nu*(1+x[pos_up:length(x)]^2)^expnu
# #pos_up2<-pos_up-1
# #Coeff_up <- Inter_mytail(dens=dens,x=x,pos=pos_up, pos2=pos_up2,C_nu=C_nu)
# Coeff_up <- Inter_mytail(dens=dens,x=x,pos=pos_up, C_nu=C_nu)
# t_up <- 1/(1+x[pos_up:length(x)]^2)
# #dens_up<- C_nu*t_up^(0.5*nu+0.5)*(1+Coeff_up[1,1]*t_up+Coeff_up[2,1]*t_up^2)
# dens_up<- C_nu*t_up^(0.5*nu+0.5)*(1+Coeff_up*t_up)
# dens <- c(dens[1:(pos_up-1)],dens_up)
# }
# if(!pos_low>1&!pos_up<length(x)){
# dens<-dens
# }
# }
#
# }
# yuima density function
dtLevy<-function(x, nu, h, method="COS", up = 7, low = -7, N = 2^7, N_grid=1000, regular_par=NULL){
pts <- sort(unique(c(x,seq(low,up,length.out=N_grid))))
if(method == "COS" | method == "FFT"){
approx_tail <- "NO"
}else{
approx_tail <- "Tail_Approx1"
}
if(method=="LAG" & approx_tail=="Tail_Approx1"){
mydens <- mydtlevy_4vers(pts/sqrt(nu),nu=nu,sigma=1,h=h, n=N,
regular_par=regular_par,
ImprovGibbs=TRUE)/sqrt(nu)
pos <- match(x,pts)
mydens1 <-mydens[pos]
return(mydens1)
}
dens0<-tdens_noapprox(x=pts, nu, h, method, N)
#if(approx_tail=="NO"){
pos <- match(x,pts)
mydens <- dens0[pos]
return(mydens)
#}else{
# if(approx_tail=="Tail_Approx1"){
# mydens <- approx_dens1(mydens=dens0,x=pts,nu=nu,h=h)
# pos <- match(x,pts)
# mydens1 <-mydens[pos]
# return(mydens1)
# }
# if(approx_tail=="Tail_Approx2"){
# mydens <- approx_dens2(mydens=dens0,x=pts,nu=nu,h=h)
# pos <- match(x,pts)
# mydens1 <-mydens[pos]
# return(mydens1)
# }
#}
}
# yuima cdf
# cdf
ptLevy<-function(x, nu, h, method="COS", up = 7, low = -7, N = 2^7, N_grid=1000,regular_par=NULL){
pts <- sort(unique(c(x,seq(low,up,length.out=N_grid))))
if(method == "COS" | method == "FFT"){
approx_tail <- "NO"
}else{
approx_tail <- "Tail_Approx1"
}
if(method=="LAG" & approx_tail=="Tail_Approx1"){
dens_g <- mydtlevy_4vers(pts/sqrt(nu),nu=nu,sigma=1,h=h, n=N,
regular_par=regular_par,
ImprovGibbs=TRUE)/sqrt(nu)
}else{
dens_g <- dtLevy(x=pts, nu=nu, h=h, method=method, up=max(pts), low=min(pts), N=N, N_grid=2)
}
cdf_g <- cumsum(dens_g*diff(pts)[1])
cond <- pts>=low & pts<=up
cdf_g0<-cdf_g[cond]
cond_cdf<-(cdf_g0-cdf_g0[1])/(tail(cdf_g0,1L)-cdf_g0[1])
res<-as.numeric(spline(x=pts[cond],
y=cond_cdf,xout=x)$y)
return(res)
}
# yuima quantile
# quantile
qtLevy<-function(p, nu, h, method="COS", up = 7, low = -7, N = 2^7, N_grid=1000,regular_par=NULL){
pts <- seq(low,up,length.out=N_grid)
if(method == "COS" | method == "FFT"){
approx_tail <- "NO"
}else{
approx_tail <- "Tail_Approx1"
}
if(method=="LAG" & approx_tail=="Tail_Approx1"){
dens_g <- mydtlevy_4vers(pts/sqrt(nu),nu=nu,sigma=1,h=h, n=N,
regular_par=regular_par,
ImprovGibbs=TRUE)/sqrt(nu)
}else{
dens_g<-dtLevy(x=pts, nu=nu, h=h, method=method, up=max(pts), low=min(pts), N=N, N_grid=2)
}
cdf_g <- cumsum(dens_g*diff(pts)[1])
cond <- pts>=low & pts<=up
cdf_g0<-cdf_g[cond]
cond_cdf<-(cdf_g0-cdf_g0[1])/(tail(cdf_g0,1L)-cdf_g0[1])
res<-as.numeric(approx(x=cond_cdf,
y=pts[cond],xout=p,method="constant",f=1)$y)
# res<-as.numeric(spline(x=cond_cdf,
# y=pts[cond],xout=p)$y)
return(res)
}
# yuima rng
# Random Number Generator
rtLevy<-function(n, nu, h, method="COS", up = 7, low = -7, N = 2^7, N_grid=1000,regular_par=NULL){
if(method == "COS" | method == "FFT"){
approx_tail <- "NO"
}else{
approx_tail <- "Tail_Approx1"
}
if(method=="LAG" & approx_tail=="Tail_Approx1"){
res<-rt_Levy(n=n,nu=nu,h=h,n_laguerre=N,up=up,low=low,
N_grid=c(N_grid+1),
regular_par=regular_par, ImprovGibbs=TRUE)
return(res)
}
pts <- seq(low,up,length.out=N_grid)
dens_g<-dtLevy(x=pts, nu=nu, h=h, method=method, up=max(pts), low=min(pts), N=N, N_grid=2)
cdf_g <- cumsum(dens_g*diff(pts)[1])
cond <- pts>=low & pts<=up
cdf_g0<-cdf_g[cond]
cond_cdf<-(cdf_g0-cdf_g0[1])/(tail(cdf_g0,1L)-cdf_g0[1])
U <- runif(n)
# res<-as.numeric(approx(x=cond_cdf,
# y=pts[cond],xout=U,method="constant",f=1, ties = mean)$y) # if we use constant we have more zeros
res<-as.numeric(approx(x=cond_cdf,
y=pts[cond],xout=U,method="linear",f=1, ties = mean)$y) # if we use constant we have more zeros
# res<-as.numeric(spline(x=cond_cdf,
# y=pts[cond],xout=U)$y)
return(res)
# newpts <- seq(pts[which(cdf_g0>0.001)[1]], pts[which(cdf_g0>1-0.001)[1]-1],length.out=N_grid)
#
# dens_g<-dtLevy(x=newpts, nu=nu, h=h, method=method,
# up=max(newpts), low=min(newpts), N=N, N_grid=2)
# cdf_g0 <- cumsum(dens_g*diff(pts)[1])
#
# cond_cdf<-(cdf_g0-cdf_g0[1])/(tail(cdf_g0,1L)-cdf_g0[1])
# U <- runif(n)
# # res<-as.numeric(approx(x=cond_cdf,
# # y=newpts,xout=U,method="constant",f=1)$y) # if we use constant we have more zeros
# res<-as.numeric(spline(x=cond_cdf,
# y=newpts,xout=U)$y)
# # U <- runif(n)
# # res<-as.numeric(approx(x=cdf_g,
# # y=pts,xout=U,method="constant",f=1)$y)
#
#
# return(res)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/yuima_t_student_Levy.R |
#.First.lib <- function(lib, pkg) library.dynam("sde", pkg, lib)
#.noGenerics <- TRUE
.onAttach <- function(libname, pkgname)
{
# require(methods)
# require(zoo)
Pver <- read.dcf(file=system.file("DESCRIPTION", package=pkgname),
fields="Version")
packageStartupMessage(rep("#",40))
packageStartupMessage(sprintf("This is YUIMA Project package v.%s", Pver))
packageStartupMessage("Why don't you try yuimaGUI package?")
packageStartupMessage("Visit: http://www.yuima-project.com")
packageStartupMessage(rep("#",40))
# require(KernSmooth, quietly=TRUE)
# library.dynam("yuima", pkgname, libname)
}
| /scratch/gouwar.j/cran-all/cranData/yuima/R/zzz.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
Rver <- paste(version$major,version$minor, collapse="",sep=".")
YUIMAver <- as.character(read.dcf(file=system.file("DESCRIPTION", package="yuima"),
fields="Version"))
## ----eval=FALSE----------------------------------------------------------
## ybook(1)
## ----eval=FALSE----------------------------------------------------------
## ybook(3)
## ------------------------------------------------------------------------
data(cars)
class(cars)
## ------------------------------------------------------------------------
summary(cars)
mod <- lm(dist~speed, data=cars)
summary(mod)
## ------------------------------------------------------------------------
class(mod)
## ------------------------------------------------------------------------
methods(summary)
## ------------------------------------------------------------------------
x <- 1:4
x
class(x)
class(x) <- "lm"
class(x)
## ------------------------------------------------------------------------
require(stats4)
set.seed(123)
y <- rnorm(100, mean=1.5)
f <- function(theta=0) -sum(dnorm(x=y, mean=theta,log=TRUE))
fit <- mle(f)
fit
## ----results='hide'------------------------------------------------------
str(fit)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(fit)),width=60))
## ------------------------------------------------------------------------
fit@coef
## ------------------------------------------------------------------------
showMethods(summary)
## ----eval=FALSE----------------------------------------------------------
## install.packages("yuima")
## ----eval=FALSE----------------------------------------------------------
## install.packages("yuima",repos="http://R-Forge.R-project.org")
## ----eval=FALSE----------------------------------------------------------
## install.packages("yuima",repos="http://R-Forge.R-project.org",
## type="source")
## ----echo=TRUE,eval=TRUE,results='markup',showWarnings=TRUE--------------
library(yuima)
## ----echo=TRUE,results='hide'--------------------------------------------
mod1 <- setModel(drift = "-3*x", diffusion = "1/(1+x^2)")
## ----results='hide'------------------------------------------------------
str(mod1)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(mod1)),width=60))
## ------------------------------------------------------------------------
mod1
## ----echo=TRUE,results='hide'--------------------------------------------
mod1b <- setModel(drift = "-3*s*y", diffusion = "1/(1+y^2)",
state.var="y", time.var="s")
## ------------------------------------------------------------------------
tmp <- capture.output(str(mod1b))
writeLines(strwrap(tmp[c(2,3,4,17,19,23)],width=60))
## ----echo=TRUE,results='hide'--------------------------------------------
mod2 <- setModel(drift = "-mu*x", diffusion = "1/(1+x^gamma)")
## ------------------------------------------------------------------------
tmp <- capture.output(str(mod2))
writeLines(strwrap(tmp[c(2,3,4,9:13,17,19,23)],width=60))
## ------------------------------------------------------------------------
mod2
## ----sim-mod1,echo=TRUE,results='hide'-----------------------------------
set.seed(123)
X <- simulate(mod1)
## ----plot-mod1,echo=TRUE,fig.keep='none',results='hide'------------------
plot(X)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ----plot-mod1bis,echo=TRUE,fig.keep='none',results='hide'---------------
x0 <- 1
set.seed(123)
X <- simulate(mod1, xinit=x0)
plot(X)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1bis.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ----results='hide'------------------------------------------------------
str(X@data,vec.len=2)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(X@data,vec.len=2)),width=60))
## ------------------------------------------------------------------------
str(X@sampling,vec.len=2)
## ------------------------------------------------------------------------
tmp <- capture.output(str(X))
writeLines(strwrap(tmp[c(14:16,29,31,35,36)],width=60))
## ------------------------------------------------------------------------
X
## ----plot-mod1ter,echo=TRUE,fig.keep='none',results='hide'---------------
x0 <- 1
set.seed(123)
X <- simulate(mod1, xinit=x0, Initial=0.5, Terminal=1.2)
X
plot(X)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1ter.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ------------------------------------------------------------------------
str(X@sampling,vec.len=2)
## ----plot-mod1b,echo=TRUE,fig.keep='none',results='hide'-----------------
set.seed(123)
X <- simulate(mod1b, xinit=x0)
X
plot(X)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1b.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ----sim-mod2,echo=TRUE,fig.keep='none',results='hide'-------------------
set.seed(123)
X <- simulate(mod2,true.param=list(mu=1,gamma=3))
plot(X)
## ----plot-mod2,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-mod2.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ------------------------------------------------------------------------
sol <- c("x1","x2") # variable for numerical solution
b <- c("-theta*x1","-x1-gamma*x2") # drift vector
s <- matrix(c("1","x1","0","beta","x2","0"),2,3) # diff. mat.
mymod <- setModel(drift = b, diffusion = s, solve.variable = sol)
## ------------------------------------------------------------------------
samp <- setSampling(Terminal=3, n=3000)
## ----results='hide'------------------------------------------------------
str(samp)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(samp)),width=60))
## ------------------------------------------------------------------------
set.seed(123)
X2 <- simulate(mymod, sampling=samp,
true.param=list(theta=1,gamma=1,beta=1))
X2
## ----results='hide'------------------------------------------------------
str(X2@sampling)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(X2@sampling)),width=60))
## ----sub1----------------------------------------------------------------
newsamp <- setSampling(
random=list(rdist=c( function(x) rexp(x, rate=10),
function(x) rexp(x, rate=20))) )
## ----results='hide'------------------------------------------------------
str(newsamp)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(newsamp)),width=60))
## ----sub2,echo=TRUE, fig.keep='none'-------------------------------------
newdata <- subsampling(X2, sampling=newsamp)
newdata
plot(X2,plot.type="single", lty=c(1,3),ylab="X2")
points(get.zoo.data(newdata)[[1]],col="red")
points(get.zoo.data(newdata)[[2]],col="green",pch=18)
## ----plot-sub2,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-sub2.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X2,plot.type="single", lty=c(1,3),ylab="X2")
points(get.zoo.data(newdata)[[1]],col="red")
points(get.zoo.data(newdata)[[2]],col="green",pch=18)
dev.off()
## ----sub3,echo=TRUE, fig.keep='none'-------------------------------------
newsamp <- setSampling(Terminal=3, delta=c(0.1,0.2), n=NULL)
newsamp
newdata <- subsampling(X2, sampling=newsamp)
newdata
plot(X2,plot.type="single", lty=c(1,3),ylab="X2")
points(get.zoo.data(newdata)[[1]],col="red")
points(get.zoo.data(newdata)[[2]],col="green", pch=18)
## ----plot-sub3,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-sub3.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X2,plot.type="single", lty=c(1,3),ylab="X2")
points(get.zoo.data(newdata)[[1]],col="red")
points(get.zoo.data(newdata)[[2]],col="green", pch=18)
dev.off()
## ------------------------------------------------------------------------
str(newdata@sampling)
## ----sub4,fig.keep='none'------------------------------------------------
set.seed(123)
Y.sub <- simulate(mymod,sampling=setSampling(delta=0.001,n=1000),
subsampling=setSampling(delta=0.01,n=100),
true.par=list(theta=1,beta=1,gamma=1))
set.seed(123)
Y <- simulate(mymod, sampling=setSampling(delta=0.001,n=1000),
true.par=list(theta=1,beta=1,gamma=1))
plot(Y, plot.type="single")
points(get.zoo.data(Y.sub)[[1]],col="red")
points(get.zoo.data(Y.sub)[[2]],col="green",pch=18)
## ----plot-sub4,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-sub4.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(Y, plot.type="single")
points(get.zoo.data(Y.sub)[[1]],col="red")
points(get.zoo.data(Y.sub)[[2]],col="green",pch=18)
dev.off()
## ----sub5,fig.keep='none'------------------------------------------------
plot(Y.sub, plot.type="single")
## ----plot-sub5,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-sub5.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(Y.sub, plot.type="single")
dev.off()
## ------------------------------------------------------------------------
Y
Y.sub
## ----eval=FALSE----------------------------------------------------------
## my.yuima <- setYuima(data=setData(X), model=mod)
## ----echo=TRUE,results='hide',tidy=TRUE----------------------------------
require(quantmod)
getSymbols("IBM", to = "2017-07-31")
## ----echo=TRUE-----------------------------------------------------------
str(IBM)
## ----echo=TRUE-----------------------------------------------------------
head(IBM)
## ------------------------------------------------------------------------
x <- setYuima(data=setData(IBM$IBM.Close))
str(x@data)
## ------------------------------------------------------------------------
y <- setYuima(data=setData(IBM$IBM.Close, delta=1/252))
str(y@data)
## ----setData,fig.keep='none'---------------------------------------------
plot(x, main="data with the original time stamps")
plot(y, main="time stamps of data rescaled")
## ----plot-setData,echo=FALSE, fig.keep='none',results='hide'-------------
pdf("figures/plot-setData.pdf",width=9,height=6)
par(mar=c(4,4,2,1), mfrow=c(2,1))
plot(x, main="data with the original time stamps")
plot(y, main="time stamps of data rescaled")
dev.off()
## ------------------------------------------------------------------------
x
y
## ----quantmod,message=FALSE----------------------------------------------
library(quantmod)
getSymbols("IBM", to = "2017-07-31")
attr(IBM, "src")
## ------------------------------------------------------------------------
getSymbols("IBM", to = "2017-07-31", src="google")
attr(IBM, "src")
## ------------------------------------------------------------------------
getSymbols("DEXUSEU",src="FRED")
attr(DEXUSEU, "src")
getSymbols("EUR/USD",src="oanda")
attr(EURUSD, "src")
str(EURUSD)
## ----results='hide'------------------------------------------------------
library(tseries)
x <- get.hist.quote("IBM")
str(x)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(x)),width=60))
## ------------------------------------------------------------------------
mydat <- get.zoo.data(y)[[1]]
str(mydat)
## ------------------------------------------------------------------------
head(y@[email protected])
## ----results='hide'------------------------------------------------------
str(y@[email protected])
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(y@[email protected])),width=60))
## ------------------------------------------------------------------------
set.seed(123)
some.data <- rnorm(12)
str(some.data)
## ------------------------------------------------------------------------
X <- ts(some.data, frequency = 4, start = c(1961, 2))
X
## ------------------------------------------------------------------------
set.seed(123)
X <- ts(some.data, start = c(1964, 2), frequency = 12)
X
## ------------------------------------------------------------------------
time(X)[1:12]
deltat(X)
start(X)
end(X)
frequency(X)
## ------------------------------------------------------------------------
window(X, frequency=4)
## ------------------------------------------------------------------------
require(zoo)
X <- zoo( some.data )
X
str(X)
## ------------------------------------------------------------------------
index(X)
## ------------------------------------------------------------------------
rtimes <- cumsum(rexp(12,rate=0.2))
rtimes
## ------------------------------------------------------------------------
X <- zoo( rnorm(12), order.by = rtimes)
X
str(X)
## ------------------------------------------------------------------------
Xreg <- zooreg(some.data, start = c(1964, 2), frequency = 12)
time(Xreg)
## ------------------------------------------------------------------------
Y <- as.ts(X)
time(X)
time(Y)
## ------------------------------------------------------------------------
require(xts)
my.time.stamps <- as.Date(rtimes)
my.time.stamps
X <- xts( some.data , order.by = my.time.stamps)
X
str(X)
## ------------------------------------------------------------------------
X.ts <- ts(some.data, start = c(1964, 2), frequency = 12)
X.ts
X.zoo <- as.zoo(X.ts)
X.zoo
X.xts <- as.xts(X.ts)
X.xts
## ----xts,fig.keep='none'-------------------------------------------------
plot(X)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-xts.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(X)
dev.off()
## ------------------------------------------------------------------------
require(tseries)
X <- irts( rtimes, some.data)
X
str(X)
## ------------------------------------------------------------------------
require(timeSeries)
X <- timeSeries( some.data, my.time.stamps)
X
str(X)
## ------------------------------------------------------------------------
d <- ISOdate(2008,7,3)
d
## ----results='hide'------------------------------------------------------
args(ISOdate)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(args(ISOdate)),width=60))
## ------------------------------------------------------------------------
class(d)
## ------------------------------------------------------------------------
names(as.POSIXlt(d))
unlist(as.POSIXlt(d))
## ------------------------------------------------------------------------
format(d,"%a") # week day
format(d,"%A")
format(d,"%b") # month
format(d,"%B")
format(d,"%c") # full date
format(d,"%D") # yy/dd/mm
format(d,"%T") # hh:mm:ss
format(d,"%A %B %d %H:%M:%S %Y")
format(d,"%A %d/%m/%Y")
format(d,"%d/%m/%Y (%A)")
## ------------------------------------------------------------------------
x <- c("10jan1962", "2feb1970", "11jul2011", "27jun1968")
strptime(x, "%d%b%Y")
## ------------------------------------------------------------------------
Sys.getlocale()
Sys.setlocale("LC_ALL", "it_it")
strptime(x, "%d%b%Y")
Sys.setlocale("LC_ALL", "en_GB")
strptime(x, "%d%b%Y")
## ------------------------------------------------------------------------
format(ISOdate(2006,6,9),"%H:%M:%S")
format(as.POSIXct("2006-06-09"),"%H:%M:%S")
## ------------------------------------------------------------------------
holidayNYSE()
holidayNERC()
## ------------------------------------------------------------------------
ISOdate(2006,7,10) - ISOdate(2005, 3, 1)
## ------------------------------------------------------------------------
my.dates <- timeDate(c("2001-01-09", "2001-02-25"))
diff(my.dates)
## ------------------------------------------------------------------------
listFinCenter("America*")[1:50]
## ------------------------------------------------------------------------
dA <- timeDate("2011-02-05", Fin="Europe/Zurich")
dB <- timeDate("2016-01-22", Fin="America/Chicago")
dA
dB
## ------------------------------------------------------------------------
set.seed(123)
mydata <- rnorm(9)
chardata <- sprintf("2010-0%s-01", 9:1)
chardata
## ------------------------------------------------------------------------
X1 <- zoo(mydata, as.Date(chardata))
X2 <- xts(mydata, as.Date(chardata))
X3 <- timeSeries(mydata, chardata)
## ------------------------------------------------------------------------
X1
X2
X3
## ------------------------------------------------------------------------
zA <- zoo(mydata, as.POSIXct(chardata))
zB <- zoo(mydata, ISOdatetime(2016, 9:1, 1, 0,0,0))
zC <- zoo(mydata, ISOdate(2016, 9:1, 1, 0))
zA
zB
zC
## ------------------------------------------------------------------------
set.seed(123)
val1 <- rnorm(9)
val2 <- rnorm(6)
mydate1 <- ISOdate(2016,1:9,1)
mydate2 <- ISOdate(2015,6:11,1)
Z1 <- zoo(val1, mydate1)
Z2 <- zoo(val2, mydate2)
rbind(Z1,Z2)
X1 <- xts(val1, mydate1)
X2 <- xts(val2, mydate2)
rbind(X1,X2)
W1 <- timeSeries(val1, mydate1)
W2 <- timeSeries(val2, mydate2)
rbind(W1,W2)
## ------------------------------------------------------------------------
mydate2 <- ISOdate(2016,4:9,1)
Z2 <- zoo(val2, mydate2)
## ----eval=FALSE----------------------------------------------------------
## rbind(Z1,Z2)
## ----echo=FALSE----------------------------------------------------------
cat(unclass(try(rbind(Z1,Z2))))
## ------------------------------------------------------------------------
X2 <- xts(val2, mydate2)
rbind(X1,X2)
W2 <- timeSeries(val2, mydate2)
rbind(W1,W2)
## ------------------------------------------------------------------------
merge(Z1,Z2)
merge(X1,X2)
## ------------------------------------------------------------------------
merge(W1,W2)
## ------------------------------------------------------------------------
W2 <- timeSeries(val2, mydate2, units="MyData")
merge(W1,W2)
## ------------------------------------------------------------------------
mydate1 <- ISOdate(2016,1:9,1)
mydate2 <- ISOdate(2015,6:11,1)
W1 <- timeSeries(val1, mydate1)
W2 <- timeSeries(val2, mydate2)
## ------------------------------------------------------------------------
rbind(W1,W2)
## ------------------------------------------------------------------------
rbind(W2,W1)
## ------------------------------------------------------------------------
sort( rbind(W2,W1) )
sort( rbind(W2,W1), decr=TRUE)
## ------------------------------------------------------------------------
W2
rev(W2)
## ----results="hide",message=FALSE----------------------------------------
require(sde)
data(quotes)
str(quotes)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(quotes)),width=60))
## ------------------------------------------------------------------------
quotes[2,2:4]
quotes[10:20,"INTEL"]
## ------------------------------------------------------------------------
quotes$INTEL[10:20]
## ------------------------------------------------------------------------
mydate <- as.Date(sprintf("2006-08-%.2d",20:10))
mydate
quotes[mydate, 5:9]
## ------------------------------------------------------------------------
initial <- as.Date("2007-05-15")
terminal <- as.Date("2007-05-21")
quotes[ (time(quotes) >= initial) & (time(quotes)<= terminal), 4:9]
## ------------------------------------------------------------------------
getSymbols("IBM", from="2015-01-01", to = "2016-12-31")
str(IBM)
## ------------------------------------------------------------------------
IBM["2015-01","IBM.Close"]
## ------------------------------------------------------------------------
IBM["2016-02-11/2016-03-05","IBM.Close"]
## ------------------------------------------------------------------------
IBM["/2015-02-11","IBM.Close"]
## ------------------------------------------------------------------------
mod2 <- setModel(drift = "-mu*x", diffusion = "1/(1+x^gamma)")
mod2
## ------------------------------------------------------------------------
toLatex(mod2)
## ----echo=FALSE, results='asis', include=TRUE----------------------------
toLatex(mod2)
## ------------------------------------------------------------------------
sol <- c("x1","x2") # variable for numerical solution
b <- c("-theta*x1","-x1-gamma*x2") # drift vector
s <- matrix(c("1","x1","0","delta","x2","0"),2,3) # diff. mat.
mymod <- setModel(drift = b, diffusion = s, solve.variable = sol)
## ----echo=FALSE, results='asis', include=TRUE----------------------------
toLatex(mymod)
## ----eval=FALSE----------------------------------------------------------
## install.packages("yuimaGUI")
## ----eval=FALSE----------------------------------------------------------
## library(yuimaGUI)
## yuimaGUI()
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter1.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ----results='hide',tidy=FALSE,width.cutoff = 60-------------------------
mod1 <- setModel(drift = "-3*x", diffusion = "1/(1+x^2)",
xinit="rnorm(1)")
str(mod1)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(mod1)),width=60))
## ----plot-mod1diff,echo=TRUE,fig.keep='none',results='hide'--------------
set.seed(123)
x1 <- simulate(mod1)
x2 <- simulate(mod1)
par(mfrow=c(1,2))
plot(x1)
plot(x2)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1diff.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
set.seed(123)
x1 <- simulate(mod1)
x2 <- simulate(mod1)
par(mfrow=c(1,2))
plot(x1)
plot(x2)
dev.off()
## ----results='hide'------------------------------------------------------
mod2 <- setModel(drift = "-3*x", diffusion = "1/(1+x^2)",
xinit="rnorm(1, mean=mu)")
mod2
str(mod2)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(mod2)),width=60))
## ----eval=FALSE----------------------------------------------------------
## x <- simulate(mod2, true.par=list(mu=1))
## ----plot-mod1diff2,echo=TRUE,fig.keep='none',results='hide'-------------
mod1 <- setModel(drift = "-3*x", diffusion = "1/(1+x^2)")
set.seed(123)
x1 <- simulate(mod1, xinit=1)
x2 <- simulate(mod1, xinit=expression(rnorm(1)))
x3 <- simulate(mod2, xinit=3)
par(mfrow=c(1,3))
plot(x1, main="mod1, xinit=1")
plot(x2, main="mod1, xinit=expression(rnorm(1))")
plot(x3, main="mod2, xinit=3")
par(mfrow=c(1,1))
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-mod1diff2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
mod1 <- setModel(drift = "-3*x", diffusion = "1/(1+x^2)")
set.seed(123)
x1 <- simulate(mod1, xinit=1)
x2 <- simulate(mod1, xinit=expression(rnorm(1)))
x3 <- simulate(mod2, xinit=3)
par(mfrow=c(1,3))
plot(x1, main="mod1, xinit=1")
plot(x2, main="mod1, xinit=expression(rnorm(1))")
plot(x3, main="mod2, xinit=3")
dev.off()
## ------------------------------------------------------------------------
ou <- setModel(drift="-theta*x", diffusion=1)
## ------------------------------------------------------------------------
gBm <- setModel(drift="mu*x", diffusion="sigma*x")
## ------------------------------------------------------------------------
vasicek <- setModel(drift="theta1-theta2*x", diffusion="theta3")
## ------------------------------------------------------------------------
cev <- setModel(drift="mu*x", diffusion="sigma*x^gamma")
## ------------------------------------------------------------------------
cir <- setModel(drift="theta1-theta2*x", diffusion="theta3*sqrt(x)")
## ------------------------------------------------------------------------
ckls <- setModel(drift="theta1-theta2*x", diffusion="theta3*x^theta4")
## ------------------------------------------------------------------------
hyper1 <- setModel( diff="sigma",
drift="(sigma^2/2)*(beta-alpha*((x-mu)/(sqrt(delta^2+(x-mu)^2))))")
## ------------------------------------------------------------------------
hyper1
str(hyper1@parameter)
## ------------------------------------------------------------------------
hyper2 <- setModel(drift="0",
diffusion = "sigma*exp(0.5*alpha*sqrt(delta^2+(x-mu)^2)-
beta*(x-mu))")
## ------------------------------------------------------------------------
hyper2
str(hyper2@parameter)
## ------------------------------------------------------------------------
set.seed(123)
modA <- setModel(drift="-0.3*x", diffusion=1)
modB <- setModel(drift="0.3*x", diffusion=1)
## Set the model in an `yuima' object with a sampling scheme.
Terminal <- 1
n <- 500
mod.sampling <- setSampling(Terminal=Terminal, n=n)
yuima1 <- setYuima(model=modA, sampling=mod.sampling)
yuima2 <- setYuima(model=modB, sampling=mod.sampling)
##use original increment
delta <- Terminal/n
my.dW <- matrix( rnorm(n , 0, sqrt(delta)), nrow=1, ncol=n)
## Solve SDEs using Euler-Maruyama method.
y1 <- simulate(yuima1, xinit=1, increment.W=my.dW)
y2 <- simulate(yuima2, xinit=1, increment.W=my.dW)
## ----plot-modAB,echo=TRUE,fig.keep='none',results='hide'-----------------
plot(y1)
lines(get.zoo.data(y2)[[1]], col="red",lty=3)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-modAB.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(y1)
lines(get.zoo.data(y2)[[1]], col="red",lty=3)
dev.off()
## ----echo=TRUE,results='hide'--------------------------------------------
sol <- c("x1","x2") # variable for numerical solution
a <- c("-3*x1","-x1-2*x2") # drift vector
b <- matrix(c("1","x1","0","3","x2","0"),2,3) # diffusion matrix
mod3 <- setModel(drift = a, diffusion = b, solve.variable = sol)
## ----sim-mod3,echo=TRUE,fig.keep='none',results='hide'-------------------
set.seed(123)
X <- simulate(mod3)
plot(X, plot.type="single",lty=1:2)
## ----plot-mod3,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-mod3.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(X, plot.type="single",lty=1:2)
dev.off()
## ----echo=TRUE,results='hide'--------------------------------------------
mu <- 0.1
sig <- 0.2
rho <- -0.7
g <- function(t) {0.4 + (0.1 + 0.2*t)* exp(-2*t)}
f1 <- function(t, x1, x2, x3) {
ret <- 0
if(x1 > 0 && x2 > 0) ret <- x2*exp(log(x1)*2/3)
return(ret)
}
f2 <- function(t, x1, x2, x3) {
ret <- 0
if(x3 > 0) ret <- rho*sig*x3
return(ret)
}
f3 <- function(t, x1, x2, x3) {
ret <- 0
if(x3 > 0) ret <- sqrt(1-rho^2)*sig*x3
return(ret)
}
diff.coef.matrix <- matrix(c("f1(t,x1,x2,x3)",
"f2(t,x1,x2,x3) * g(t)", "f2(t,x1,x2,x3)", "0",
"f3(t,x1,x2,x3)*g(t)", "f3(t,x1,x2,x3)"), 3, 2)
sabr.mod <- setModel(drift = c("0", "mu*g(t)*x3", "mu*x3"),
diffusion = diff.coef.matrix, state.variable = c("x1", "x2", "x3"),
solve.variable = c("x1", "x2", "x3"))
str(sabr.mod@parameter)
## ----echo=TRUE,results='hide'--------------------------------------------
f2 <- function(t, x1, x2, x3, rho, sig) {
ret <- 0
if(x3 > 0) ret <- rho*sig*x3
return(ret)
}
f3 <- function(t, x1, x2, x3, rho, sig) {
ret <- 0
if(x3 > 0) ret <- sqrt(1-rho^2)*sig*x3
return(ret)
}
diff.coef.matrix <- matrix(c("f1(t,x1,x2,x3)",
"f2(t,x1,x2,x3,rho, sig) * g(t)", "f2(t,x1,x2,x3,rho,sig)",
"0", "f3(t,x1,x2,x3,rho,sig)*g(t)", "f3(t,x1,x2,x3,rho,sig)"), 3, 2)
sabr.mod <- setModel(drift = c("0", "mu*g(t)*x3", "mu*x3"),
diffusion = diff.coef.matrix, state.variable = c("x1", "x2", "x3"),
solve.variable = c("x1", "x2", "x3"))
str(sabr.mod@parameter)
## ------------------------------------------------------------------------
Sigma <- matrix(c(0.5, 0.7, 0.7, 2), 2, 2)
C <- chol(Sigma)
C
crossprod(C)
Sigma
## ------------------------------------------------------------------------
set.seed(123)
drift <- c("mu*x1", "kappa*(theta-x2)")
diffusion <- matrix(c("c11*sqrt(x2)*x1", "0",
"c12*sqrt(x2)*x1", "c22*epsilon*sqrt(x2)"),2,2)
heston <- setModel(drift=drift, diffusion=diffusion,
state.var=c("x1","x2"))
X <- simulate(heston, true.par=list(theta=0.5, mu=1.2, kappa=2,
epsilon=0.2, c11=C[1,1], c12=C[1,2], c22=C[2,2]),
xinit=c(100,0.5))
## ----plot-heston,echo=FALSE, fig.keep='none',results='hide'--------------
pdf("figures/plot-heston.pdf",width=9,height=6)
set.seed(123)
par(mar=c(4,4,1,1))
plot(X)
dev.off()
## ----echo=TRUE,results='hide'--------------------------------------------
ymodel <- setModel(drift="(2-theta2*x)", diffusion="(1+x^2)^theta1")
n <- 750
ysamp <- setSampling(Terminal = n^(1/3), n = n)
yuima <- setYuima(model = ymodel, sampling = ysamp)
set.seed(123)
yuima <- simulate(yuima, xinit = 1,
true.parameter = list(theta1 = 0.2, theta2 = 0.3))
## ----echo=TRUE,results='hide'--------------------------------------------
param.init <- list(theta2=0.5,theta1=0.5)
low.par <- list(theta1=0, theta2=0)
upp.par <- list(theta1=1, theta2=1)
mle1 <- qmle(yuima, start = param.init,
lower = low.par, upper = upp.par)
## ----results='hide'------------------------------------------------------
summary(mle1)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(mle1)),width=60))
## ----echo=TRUE-----------------------------------------------------------
prior <- list(theta2=list(measure.type="code",df="dunif(theta2,0,1)"),
theta1=list(measure.type="code",df="dunif(theta1,0,1)"))
## ----echo=TRUE, results='hide'-------------------------------------------
lower <- list(theta1=0,theta2=0)
upper <- list(theta1=1,theta2=1)
bayes1 <- adaBayes(yuima, start=param.init, prior=prior,
lower=lower,upper=upper, method="nomcmc")
## ----echo=TRUE-----------------------------------------------------------
coef(summary(bayes1))
coef(summary(mle1))
## ------------------------------------------------------------------------
n <- 500
ysamp <- setSampling(Terminal = n^(1/3), n = n)
yuima <- setYuima(model = ymodel, sampling = ysamp)
set.seed(123)
yuima <- simulate(yuima, xinit = 1,
true.parameter = list(theta1 = 0.2, theta2 = 0.3))
param.init <- list(theta2=0.5,theta1=0.5)
lower <- list(theta1=0, theta2=0)
upper <- list(theta1=1, theta2=1)
mle2 <- qmle(yuima, start =param.init ,
lower = lower, upper = upper)
bayes2 <- adaBayes(yuima, start=param.init, prior=prior,
lower=lower,upper=upper)
## ------------------------------------------------------------------------
coef(summary(bayes2))
coef(summary(mle2))
## ------------------------------------------------------------------------
ymodel <- setModel(drift="(2-theta2*x)", diffusion="(1+x^2)^theta1")
n <- 100000
ysamp <- setSampling(delta=0.001, n = n)
mod <- setYuima(model = ymodel, sampling=ysamp)
set.seed(123)
yuima <- simulate(mod, xinit = 1,
true.parameter = list(theta1 = 0.2, theta2 = 0.3))
param.init <- list(theta2=0.5,theta1=0.5)
yuima0.01 <- subsampling(yuima,
sampling=setSampling(delta=0.01,n=NULL,Terminal=100))
yuima0.1 <- subsampling(yuima,
sampling=setSampling(delta=0.1,n=NULL,Terminal=100))
yuima1.0 <- subsampling(yuima,
sampling=setSampling(delta=1,n=NULL,Terminal=100))
## ----echo=TRUE, fig.keep='none',results='hide'---------------------------
par(mfrow=c(2,2))
plot(yuima,main="delta=0.001, n=100000")
plot(yuima0.01,main="delta=0.01, n=10000")
plot(yuima0.1,main="delta=0.1, n=1000")
plot(yuima1.0,main="delta=1.0, n=100")
## ----plot-delta,echo=FALSE, fig.keep='none',results='hide'---------------
pdf("figures/plot-delta.pdf",width=9,height=6)
par(mar=c(4,4,3,1))
par(mfrow=c(2,2))
plot(yuima,main="delta=0.001, n=100000")
plot(yuima0.01,main="delta=0.01, n=10000")
plot(yuima0.1,main="delta=0.1, n=1000")
plot(yuima1.0,main="delta=1.0, n=100")
dev.off()
## ------------------------------------------------------------------------
low <- list(theta1=0, theta2=0)
up <- list(theta1=1, theta2=1)
mle0.001 <- qmle(yuima, start = param.init, lower = low, upper = up)
summary(mle0.001)@coef
mle0.01 <- qmle(yuima0.01, start = param.init, lower = low,
upper = up)
summary(mle0.01)@coef
mle0.1 <- qmle(yuima0.1, start = param.init, lower = low, upper = up)
summary(mle0.1)@coef
mle1.0 <- qmle(yuima1.0, start = param.init, lower = low, upper = up)
summary(mle1.0)@coef
## ----echo=FALSE----------------------------------------------------------
est <- rbind( t(summary(mle0.001)@coef), t(summary(mle0.01)@coef),
t(summary(mle0.1)@coef), t(summary(mle1.0)@coef))
## ----message=FALSE-------------------------------------------------------
library(quantmod)
getSymbols("AAPL",to="2016-12-31")
head(AAPL)
S <- AAPL$AAPL.Adjusted
## ------------------------------------------------------------------------
Delta <- 1/252
gBm <- setModel(drift="mu*x", diffusion="sigma*x")
mod <- setYuima(model=gBm, data=setData(S, delta=Delta))
## ----appl,echo=TRUE,fig.keep='none',results='hide'-----------------------
set.seed(123)
plot(S)
## ----plot-aapl,echo=FALSE, fig.keep='none',results='hide'----------------
pdf("figures/plot-aapl.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(S)
dev.off()
## ----results='hide'------------------------------------------------------
fit <- qmle(mod, start=list(mu=1, sigma=1),
lower=list(mu=0.1, sigma=0.1),
upper=list(mu=100, sigma=10))
summary(fit)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit)),width=60))
## ------------------------------------------------------------------------
X <- diff(log(S))
X <- as.numeric(na.omit(diff(log(S))))
alpha <- mean(X)/Delta
sigma <- sqrt(var(X)/Delta)
mu <- alpha +0.5*sigma^2
mu
sigma
coef(fit)
## ------------------------------------------------------------------------
getSymbols("DEXUSEU", src="FRED")
DEXUSEU <- DEXUSEU["/2016"]
head(DEXUSEU)
meanCIR <- mean(DEXUSEU, na.rm=TRUE)
meanCIR
## ----dexuseu,echo=TRUE,fig.keep='none',results='hide'--------------------
set.seed(123)
plot(DEXUSEU)
## ----plot-dexuseu,echo=FALSE, fig.keep='none',results='hide'-------------
pdf("figures/plot-dexuseu.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(DEXUSEU)
dev.off()
## ------------------------------------------------------------------------
cir1 <- setModel(drift="theta1-theta2*x", diffusion="sigma*sqrt(x)")
cir2 <- setModel(drift="kappa*(mu-x)", diffusion="sigma*sqrt(x)")
mod1 <- setYuima(model=cir1, data=setData(na.omit(DEXUSEU),
delta=Delta))
mod2 <- setYuima(model=cir2, data=setData(na.omit(DEXUSEU),
delta=Delta))
## ----results='hide'------------------------------------------------------
fit1 <- qmle(mod1, start=list(theta1=1, theta2=1, sigma=0.5),
lower=list(theta1=0.1, theta2=0.1, sigma=0.1),
upper=list(theta1=10, theta2=10, sigma=100),
method="L-BFGS-B")
summary(fit1)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit1)),width=60))
## ----results='hide'------------------------------------------------------
fit2 <- qmle(mod2, start=list(kappa=1, mu=meanCIR, sigma=0.5),
lower=list(kappa=0.1, mu=0.1, sigma=0.1),
upper=list(kappa=10, mu=10, sigma=100),
method="L-BFGS-B")
summary(fit2)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit2)),width=60))
## ------------------------------------------------------------------------
theta1 <- as.numeric( coef(fit2)["kappa"] * coef(fit2)["mu"] )
theta1
coef(fit1)["theta1"]
## ------------------------------------------------------------------------
mu <- as.numeric( coef(fit1)["theta1"] / coef(fit1)["theta2"] )
mu
coef(fit2)["mu"]
## ------------------------------------------------------------------------
model<- setModel(drift="t1*(t2-x)",diffusion="t3")
## ------------------------------------------------------------------------
T<-300
n<-3000
sampling <- setSampling(Terminal=T, n=n)
yuima<-setYuima(model=model, sampling=sampling)
h00 <- list(t1=0.3, t2=1, t3=0.25)
h01 <- list(t1=0.3, t2=0.2, t3=0.1)
set.seed(123)
X <- simulate(yuima, xinit=1, true.par=h00)
## ------------------------------------------------------------------------
phi1 <- function(x) 1-x+x*log(x)
## ------------------------------------------------------------------------
phi.test(X, H0=h00, phi=phi1, start=h00,
lower=list(t1=0.1, t2=0.1, t3=0.1),
upper=list(t1=2,t2=2,t3=2),method="L-BFGS-B")
## ----echo=FALSE,results='hide'-------------------------------------------
pval <- phi.test(X, H0=h00, phi=phi1, start=h00,
lower=list(t1=0.1, t2=0.1, t3=0.1),
upper=list(t1=2,t2=2,t3=2),method="L-BFGS-B")$pvalue
## ------------------------------------------------------------------------
phi.test(X, H0=h01, phi=phi1, start=h00,
lower=list(t1=0.1, t2=0.1, t3=0.1),
upper=list(t1=2,t2=2,t3=2),method="L-BFGS-B")
## ------------------------------------------------------------------------
library(quantmod)
Delta <- 1/252
getSymbols("DEXUSEU", src="FRED")
DEXUSEU <- DEXUSEU["/2016"]
USEU <- setData(na.omit(DEXUSEU), delta=Delta)
meanCIR <- mean(get.zoo.data(USEU)[[1]])
gBm <- setModel(drift="mu*x", diffusion="sigma*x")
mod <- setYuima(model=gBm, data=USEU)
cir1 <- setModel(drift="theta1-theta2*x", diffusion="sigma*sqrt(x)")
cir2 <- setModel(drift="kappa*(mu-x)", diffusion="sigma*sqrt(x)")
ckls <- setModel(drift="theta1-theta2*x", diffusion="sigma*x^gamma")
mod1 <- setYuima(model=cir1, data=USEU)
mod2 <- setYuima(model=cir2, data=USEU)
mod3 <- setYuima(model=ckls, data=USEU)
gBm.fit <- qmle(mod, start=list(mu=1, sigma=1),
lower=list(mu=0.1, sigma=0.1),
upper=list(mu=100, sigma=10))
cir1.fit <- qmle(mod1, start=list(theta1=1, theta2=1, sigma=0.5),
lower=list(theta1=0.1, theta2=0.1, sigma=0.1),
upper=list(theta1=10, theta2=10, sigma=100),
method="L-BFGS-B")
cir2.fit <- qmle(mod2, start=list(kappa=1, mu=meanCIR, sigma=0.5),
lower=list(kappa=0.1, mu=0.1, sigma=0.1),
upper=list(kappa=10, mu=10, sigma=100),
method="L-BFGS-B")
ckls.fit <- qmle(mod3, start=list(theta1=1, theta2=1, sigma=0.5,
gamma=0.5), lower=list(theta1=0.1, theta2=0.1, sigma=0.1,
gamma=0.1), upper=list(theta1=10, theta2=10, sigma=10,
gamma=2), method="L-BFGS-B")
## ------------------------------------------------------------------------
AIC(gBm.fit,cir1.fit,cir2.fit,ckls.fit)
## ----echo=FALSE----------------------------------------------------------
tmp <- AIC(gBm.fit,cir1.fit,cir2.fit,ckls.fit)
## ------------------------------------------------------------------------
set.seed(123)
S <- simulate(gBm, true.par=list(mu=1, sigma=0.25),
sampling=setSampling(T=1, n=1000), xinit=100)
mod <- setYuima(model=gBm, data=S@data)
mod1 <- setYuima(model=cir1, data=S@data)
mod2 <- setYuima(model=cir2, data=S@data)
mod3 <- setYuima(model=ckls, data=S@data)
gBm.fit <- qmle(mod, start=list(mu=1, sigma=1),
lower=list(mu=0.1, sigma=0.1),
upper=list(mu=100, sigma=10))
cir1.fit <- qmle(mod1, start=list(theta1=1, theta2=1, sigma=0.5),
lower=list(theta1=0.1, theta2=0.1, sigma=0.1),
upper=list(theta1=10, theta2=10, sigma=100),
method="L-BFGS-B")
cir2.fit <- qmle(mod2, start=list(kappa=1, mu=meanCIR, sigma=0.5),
lower=list(kappa=0.1, mu=0.1, sigma=0.1),
upper=list(kappa=10, mu=10, sigma=100),
method="L-BFGS-B")
ckls.fit <- qmle(mod3,
start=list(theta1=1, theta2=1, sigma=0.5, gamma=0.5),
lower=list(theta1=0.1, theta2=0.1, sigma=0.1, gamma=0.1),
upper=list(theta1=10, theta2=10, sigma=10, gamma=2),
method="L-BFGS-B")
## ------------------------------------------------------------------------
AIC(gBm.fit,cir1.fit,cir2.fit,ckls.fit)
## ----echo=FALSE----------------------------------------------------------
tmp <- AIC(gBm.fit,cir1.fit,cir2.fit,ckls.fit)
## ------------------------------------------------------------------------
a <- c("1-mu11*X1+mu12*X2","2+mu21*X1-mu22*X2")
b <- matrix(c("s1*X1","s2*X1", "-s3*X2","s4*X2"),2,2)
mod.est <- setModel(drift=a, diffusion=b,
solve.var=c("X1","X2"),state.variable=c("X1","X2"))
truep <- list(mu11=.9, mu12=0, mu21=0, mu22=0.7,
s1=.3, s2=0,s3=0,s4=.2)
low <- list(mu11=1e-5, mu12=1e-5, mu21=1e-5, mu22=1e-5,
s1=1e-5, s2=1e-5, s3=1e-5,s4=1e-5)
upp <- list(mu11=2, mu12=2, mu21=1, mu22=1,
s1=1, s2=1, s3=1,s4=1)
set.seed(123)
n <- 1000
X <- simulate(mod.est, T=n^(1/3), n=n, xinit=c(1,1),
true.parameter=truep)
## ----results='hide'------------------------------------------------------
myest <- lasso(X, delta=2, start=truep, lower=low, upper=upp,
method="L-BFGS-B")
myest
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(myest),width=60))
## ------------------------------------------------------------------------
fit1 <- qmle(X, start=truep, lower=low, upper=upp, method="L-BFGS-B")
## ------------------------------------------------------------------------
a <- c("1-mu11*X1","2-mu22*X2")
b <- matrix(c("s1*X1","0", "0","s4*X2"),2,2)
mod.est2 <- setModel(drift=a, diffusion=b,
solve.var=c("X1","X2"),state.variable=c("X1","X2"))
truep <- list(mu11=.9, mu22=0.7, s1=.3,s4=.2)
low <- list(mu11=1e-5, mu22=1e-5, s1=1e-5, s4=1e-5)
upp <- list(mu11=2, mu22=2, s1=1, s4=1)
Y <- setYuima(model=mod.est2, data=X@data)
fit2 <- qmle(Y, start=truep, lower=low, upper=upp, method="L-BFGS-B")
summary(fit1)
summary(fit2)
AIC(fit1, fit2)
## ----results='hide',fig.keep='none',message=FALSE------------------------
library("Ecdat")
data("Irates")
rates <- Irates[,"r1"]
plot(rates)
X <- window(rates, start=1964.471, end=1989.333)
mod <- setModel(drift="alpha+beta*x", diffusion="sigma*x^gamma")
yuima <- setYuima(data=setData(X,delta=1/12), model=mod)
start <- list(alpha=1, beta =-.1, sigma =.1, gamma =1)
low <- list(alpha=-5, beta =-5, sigma =-5, gamma =-5)
upp <- list(alpha=8, beta =8, sigma =8, gamma =8)
## ----plot-irates,echo=FALSE, fig.keep='none',results='hide'--------------
pdf("figures/plot-irates.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(rates)
dev.off()
## ----echo=TRUE,results='hide'--------------------------------------------
lasso.est <- lasso(yuima, start=start, lower=low, upper=upp,
method="L-BFGS-B", delta=2)
lasso.est
## ----results='hide'------------------------------------------------------
mod1 <- setModel(drift="alpha", diffusion="sigma*x^gamma")
yuima1 <- setYuima(data=setData(X,delta=1/12), model=mod1)
start1 <- list(alpha=1, sigma =.1, gamma =1)
low1 <- list(alpha=-5, sigma =-5, gamma =-5)
upp1 <- list(alpha=8, sigma =8, gamma =8)
fit <- qmle(yuima, start=start, lower=low, upper=upp,
method="L-BFGS-B")
fit1 <- qmle(yuima1, start=start1, lower=low1, upper=upp1,
method="L-BFGS-B")
summary(fit)
summary(fit1)
AIC(fit, fit1)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit)),width=60))
writeLines(strwrap(capture.output(summary(fit1)),width=60))
writeLines(strwrap(capture.output(AIC(fit, fit1)),width=60))
## ----cpoint1-------------------------------------------------------------
diff.matrix <- matrix(c("theta1.k*x1","0*x2","0*x1","theta2.k*x2"),
2, 2)
drift.c <- c("sin(x1)", "3-x2")
drift.matrix <- matrix(drift.c, 2, 1)
ymodel <- setModel(drift=drift.matrix, diffusion=diff.matrix,
time.variable="t", state.variable=c("x1", "x2"),
solve.variable=c("x1", "x2"))
ymodel
## ----cpoint3,results='hide'----------------------------------------------
n <- 1000
set.seed(123)
t0 <- list(theta1.k=0.5, theta2.k=0.3)
T <- 10
tau <- 4
pobs <- tau/T
ysamp1 <- setSampling(n=n*pobs, Initial=0, delta=0.01)
yuima1 <- setYuima(model=ymodel, sampling=ysamp1)
yuima1 <- simulate(yuima1, xinit=c(3, 3), true.parameter=t0)
v11 <- get.zoo.data(yuima1)[[1]]
x1 <- as.numeric(v11[length(v11)]) # terminal value
v21 <- get.zoo.data(yuima1)[[2]]
x2 <- as.numeric(v21[length(v21)]) # terminal value
## ----cpoint3b,results='hide'---------------------------------------------
t1 <- list(theta1.k=0.2, theta2.k=0.4)
ysamp2 <- setSampling(Initial=n*pobs*0.01, n=n*(1-pobs), delta=0.01)
yuima2 <- setYuima(model=ymodel, sampling=ysamp2)
yuima2 <- simulate(yuima2, xinit=c(x1, x2), true.parameter=t1)
## ----cpoint3c,results='hide'---------------------------------------------
v12 <- get.zoo.data(yuima2)[[1]]
v22 <- get.zoo.data(yuima2)[[2]]
v1 <- c(v11,v12[-1])
v2 <- c(v21,v22[-1])
new.data <- setData(zoo(cbind(v1,v2)),delta=0.01)
yuima <- setYuima(model=ymodel, data=new.data)
## ----cpoint4,fig.keep='none'---------------------------------------------
plot(yuima)
## ----plot-cpoint4,echo=FALSE, fig.keep='none',results='hide'-------------
pdf("figures/plot-cpoint4.pdf",width=9,height=5)
par(mar=c(4,4,1,1))
plot(yuima)
dev.off()
## ----cpoint4b------------------------------------------------------------
noDriftModel <- setModel(drift=c(0,0), diffusion=diff.matrix,
time.variable="t", state.variable=c("x1", "x2"),
solve.variable=c("x1", "x2"))
noDriftModel <- setYuima(noDriftModel, data=new.data)
noDriftModel@model@drift
noDriftModel
## ----cpoint5-------------------------------------------------------------
t.est <- CPoint(yuima,param1=t0,param2=t1)
t.est$tau
t.est2 <- CPoint(noDriftModel,param1=t0,param2=t1)
t.est2$tau
## ------------------------------------------------------------------------
qmleL(noDriftModel, t=1.5, start=list(theta1.k=0.1, theta2.k=0.1),
lower=list(theta1.k=0, theta2.k=0),
upper=list(theta1.k=1, theta2.k=1),
method="L-BFGS-B") -> estL
qmleR(noDriftModel, t=8.5, start=list(theta1.k=0.1, theta2.k=0.1),
lower=list(theta1.k=0, theta2.k=0),
upper=list(theta1.k=1, theta2.k=1),
method="L-BFGS-B") -> estR
t0.est <- coef(estL)
t1.est <- coef(estR)
## ------------------------------------------------------------------------
t.est3 <- CPoint(noDriftModel,param1=t0.est,param2=t1.est)
t.est3
## ----eval=FALSE----------------------------------------------------------
## CPoint(noDriftModel,param1=t0.est,param2=t1.est, plot=TRUE)
## ----plot-cpoint-stat,echo=FALSE,results='hide'--------------------------
pdf("figures/plot-cpoint-stat.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
CPoint(noDriftModel,param1=t0.est,param2=t1.est, plot=TRUE)
dev.off()
## ------------------------------------------------------------------------
qmleL(noDriftModel, t=t.est3$tau,
start=list(theta1.k=0.1, theta2.k=0.1),
lower=list(theta1.k=0, theta2.k=0),
upper=list(theta1.k=1, theta2.k=1),
method="L-BFGS-B") -> estL
qmleR(noDriftModel, t=t.est3$tau,
start=list(theta1.k=0.1, theta2.k=0.1),
lower=list(theta1.k=0, theta2.k=0),
upper=list(theta1.k=1, theta2.k=1),
method="L-BFGS-B") -> estR
t02s.est <- coef(estL)
t12s.est <- coef(estR)
t2s.est3 <- CPoint(noDriftModel,param1=t02s.est,param2=t12s.est)
t2s.est3
## ------------------------------------------------------------------------
library(quantmod)
getSymbols("AAPL", to="2016-12-31")
S <- AAPL$AAPL.Adjusted
Delta <- 1/252
gBm <- setModel(drift="mu*x", diffusion="sigma*x")
mod <- setYuima(model=gBm, data=setData(S, delta=Delta))
lower <- list(mu=0.1, sigma=0.1)
upper <- list(mu=100, sigma=10)
start <- list(mu=1, sigma=1)
fit <- qmle(mod, start= start, upper=upper, lower=lower)
summary(fit)
## ------------------------------------------------------------------------
fit1 <- qmleL(mod, t=1, start= list(mu=1,sigma=1))
fit2 <- qmleR(mod, t=6, start= list(mu=1,sigma=1))
fit1
fit2
## ------------------------------------------------------------------------
cp <- CPoint(mod,param1=coef(fit1),param2=coef(fit2))
cp
## ----fig.keep='none'-----------------------------------------------------
X <- diff(log(get.zoo.data(mod)[[1]]))
plot(X)
abline(v=cp$tau, lty=3,lwd=2,col="red")
## ----plot-returns,echo=FALSE, fig.keep='none',results='hide'-------------
pdf("figures/plot-returns.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(X,main="log returns of AAPL")
abline(v=cp$tau, lty=3,lwd=2,col="red")
dev.off()
## ----plot-cpoint-aapl,echo=FALSE,results='hide'--------------------------
pdf("figures/plot-cpoint-aapl.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
CPoint(mod,param1=coef(fit1),param2=coef(fit2),plot=TRUE)
dev.off()
## ----echo=TRUE-----------------------------------------------------------
# diffusion coefficient for process 1
diff.coef.1 <- function(t,x1=0, x2=0) sqrt(1+t)
# diffusion coefficient for process 2
diff.coef.2 <- function(t,x1=0, x2=0) sqrt(1+t^2)
# correlation
cor.rho <- function(t,x1=0, x2=0) sqrt(1/2)
# coefficient matrix for diffusion term
diff.coef.matrix <- matrix( c( "diff.coef.1(t,x1,x2)",
"diff.coef.2(t,x1,x2) * cor.rho(t,x1,x2)", "",
"diff.coef.2(t,x1,x2) * sqrt(1-cor.rho(t,x1,x2)^2)"),2,2)
# Model SDE using yuima.model
cor.mod <- setModel(drift = c("",""), diffusion = diff.coef.matrix,
solve.variable=c("x1","x2"))
## ----echo=TRUE-----------------------------------------------------------
CC.theta <- function( T, sigma1, sigma2, rho){
tmp <- function(t) return( sigma1(t) * sigma2(t) * rho(t) )
integrate(tmp,0,T)
}
## ------------------------------------------------------------------------
set.seed(123)
Terminal <- 1
n <- 1000
# Cumulative Covariance
theta <- CC.theta(T=Terminal, sigma1=diff.coef.1,
sigma2=diff.coef.2, rho=cor.rho)$value
cat(sprintf("theta=%5.3f\n",theta))
## ----results='hide'------------------------------------------------------
yuima.samp <- setSampling(Terminal=Terminal,n=n)
yuima <- setYuima(model=cor.mod, sampling=yuima.samp)
X <- simulate(yuima)
## ------------------------------------------------------------------------
cce(X)
## ----cceplot1,fig.keep='none'--------------------------------------------
plot(X,main="complete data")
## ----plot-cceplot1,echo=FALSE, fig.keep='none',results='hide'------------
pdf("figures/plot-cceplot1.pdf",width=9,height=5)
par(mar=c(4,4,1,1))
plot(X,main="complete data")
dev.off()
## ------------------------------------------------------------------------
p1 <- 0.2
p2 <- 0.3
newsamp <- setSampling(random=list(rdist=c(
function(x) rexp(x, rate=p1*n/Terminal),
function(x) rexp(x, rate=p2*n/Terminal))) )
## ------------------------------------------------------------------------
Y <- subsampling(X, sampling=newsamp)
## ----cceplot2,fig.keep='none'--------------------------------------------
plot(Y,main="asynchronous data")
## ----plot-cceplot2,echo=FALSE, fig.keep='none',results='hide'------------
pdf("figures/plot-cceplot2.pdf",width=9,height=5)
par(mar=c(4,4,1,1))
plot(Y,main="asynchronous data")
dev.off()
## ------------------------------------------------------------------------
cce(Y)$covmat # asynch data
cce(X)$covmat # full data
## ------------------------------------------------------------------------
b1 <- function(x,y) y
b2 <- function(x,y) -x
s1 <- function(t,x,y) sqrt(abs(x)*(1+t))
s2 <- function(t,x,y) sqrt(abs(y))
cor.rho <- function(t,x,y) 1/(1+x^2)
diff.mat <- matrix(c("s1(t,x,y)", "s2(t,x,y) * cor.rho(t,x,y)","",
"s2(t,x,y) * sqrt(1-cor.rho(t,x,y)^2)"), 2, 2)
cor.mod <- setModel(drift = c("b1","b2"), diffusion = diff.mat,
solve.variable = c("x", "y"),state.var=c("x","y"))
## Generate a path of the process
set.seed(111)
Terminal <- 1
n <- 10000
yuima.samp <- setSampling(Terminal = Terminal, n = n)
yuima <- setYuima(model = cor.mod, sampling = yuima.samp)
yuima <- simulate(yuima, xinit=c(2,3))
## ------------------------------------------------------------------------
p1 <- 0.2
p2 <- 0.3
newsamp <- setSampling(random=list(rdist=c(
function(x) rexp(x, rate=p1*n/Terminal),
function(x) rexp(x, rate=p2*n/Terminal))) )
Y <- subsampling(yuima, sampling = newsamp)
## ----cceplot3,fig.keep='none'--------------------------------------------
plot(Y,main="asynchronous data (non linear case)")
## ----plot-cceplot3,echo=FALSE, fig.keep='none',results='hide'------------
pdf("figures/plot-cceplot3.pdf",width=9,height=5)
par(mar=c(4,4,1,1))
plot(Y,main="asynchronous data (non linear case)")
dev.off()
## ------------------------------------------------------------------------
cce(yuima)$covmat # full data
cce(Y)$covmat # asynch data
## ------------------------------------------------------------------------
diff.coef.matrix <- matrix(c("sqrt(x1)", "3/5*sqrt(x2)",
"1/3*sqrt(x3)", "", "4/5*sqrt(x2)","2/3*sqrt(x3)","","",
"2/3*sqrt(x3)"), 3, 3)
drift <- c("1-x1","2*(10-x2)","3*(4-x3)")
cor.mod <- setModel(drift = drift, diffusion = diff.coef.matrix,
solve.variable = c("x1", "x2","x3"))
set.seed(111)
Terminal <- 1
yuima.samp <- setSampling(Terminal = Terminal, n = 1200)
yuima <- setYuima(model = cor.mod, sampling = yuima.samp)
yuima <- simulate(yuima, xinit=c(1,7,5))
# intentionally displace the second time series
data1 <- get.zoo.data(yuima)[[1]]
data2 <- get.zoo.data(yuima)[[2]]
time2 <- time( data2 )
theta2 <- 0.05 # the lag of x2 behind x1
stime2 <- time2 + theta2
time(data2) <- stime2
data3 <- get.zoo.data(yuima)[[3]]
time3 <- time( data3 )
theta3 <- 0.12 # the lag of x3 behind x1
stime3 <- time3 + theta3
time(data3) <- stime3
syuima <- setYuima(data=setData(merge(data1, data2, data3)))
yuima
syuima
## ----shifted,fig.keep='none'---------------------------------------------
plot(syuima,main="time shifted data")
## ----plot-shifted,echo=FALSE, fig.keep='none',results='hide'-------------
pdf("figures/plot-shifted.pdf",width=9,height=5)
par(mar=c(4,4,2,1))
plot(syuima,main="time shifted data")
dev.off()
## ------------------------------------------------------------------------
llag(yuima)
llag(syuima)
## ----plot-shifted-ci,echo=FALSE, fig.keep='none',results='hide'----------
pdf("figures/plot-shifted-ci.pdf",width=9,height=5)
par(mar=c(4,5,2,1))
par(mfrow=c(1,3))
llag(syuima,plot=TRUE,ci=TRUE)
dev.off()
## ------------------------------------------------------------------------
data2 <- get.zoo.data(yuima)[[2]]
time2 <- time( data2 )
theta2 <- 0.05 # the lag of x2 behind x1
stime2 <- time2 + theta2
time(data2) <- stime2
data3 <- get.zoo.data(yuima)[[3]]
time3 <- time( data3 )
theta3 <- 0.12 # the lag of x3 behind x1
stime3 <- time3 + theta3
time(data3) <- stime3
data1 <- data1[which(time(data1)>0.5 & time(data1)<1)]
data2 <- data2[which(time(data2)>0.5 & time(data2)<1)]
data3 <- data3[which(time(data3)>0.5 & time(data3)<1)]
syuima2 <- setYuima(data=setData(merge(data1, data2, data3)))
syuima2
llag(syuima2)
## ------------------------------------------------------------------------
p1 <- 0.2
p2 <- 0.3
p3 <- 0.4
n <- 1000
newsamp <- setSampling(
random=list(rdist=c( function(x) rexp(x, rate=p1*n/Terminal),
function(x) rexp(x, rate=p2*n/Terminal),
function(x) rexp(x, rate=p3*n/Terminal))) )
psample <- subsampling(syuima, sampling = newsamp)
psample
llag(psample)
## ----results='hide'------------------------------------------------------
library(quantmod)
getSymbols("AAPL", from="2013-01-01", to="2013-12-31")
getSymbols("IBM", from="2013-01-01", to="2013-12-31")
getSymbols("AMZN", from="2013-01-01", to="2013-12-31")
getSymbols("EBAY", from="2013-01-01", to="2013-12-31")
getSymbols("FB", from="2013-01-01", to="2013-12-31")
getSymbols("MSFT", from="2013-01-01", to="2013-12-31")
data1 <- AAPL$AAPL.Close
data2 <- IBM$IBM.Close
data3 <- AMZN$AMZN.Close
data4 <- EBAY$EBAY.Close
data5 <- FB$FB.Close
data6 <- MSFT$MSFT.Close
market.data <- merge(data1, data2, data3, data4,data5,data6)
colnames(market.data) <- c("AAPL", "IBM", "AMZN", "EBAY",
"FB", "MSFT")
mkt <- setYuima(data=setData(market.data, delta=1/252))
## ------------------------------------------------------------------------
mkt
round(cce(mkt)$cormat,2) # correlation matrix
## ----market,fig.keep='none'----------------------------------------------
plot(mkt)
## ----plot-market,echo=FALSE, fig.keep='none',results='hide'--------------
pdf("figures/plot-market.pdf",width=9,height=5)
par(mar=c(4,4,1,1))
plot(mkt,main="")
dev.off()
## ------------------------------------------------------------------------
round(llag(mkt),4)
## ----corrplot,fig.keep='none',message=FALSE------------------------------
require(corrplot)
cols <- colorRampPalette(c("#7F0000", "red", "#FF7F00",
"yellow", "white", "cyan",
"#007FFF", "blue", "#00007F"))
corrplot(cce(mkt)$cormat,method="ellipse",
cl.pos = "b", tl.pos = "d", tl.srt = 60,
col=cols(100), outline=TRUE)
corrplot(llag(mkt),method="ellipse",is.corr=FALSE,
cl.pos = "b", tl.pos = "d", tl.srt = 60,
col=cols(100), outline=TRUE)
## ----plot-corrplot,echo=FALSE, fig.keep='none',results='hide'------------
pdf("figures/plot-corrplot1.pdf",width=6,height=6)
require(corrplot)
corrplot(cce(mkt)$cormat,method="ellipse",
cl.pos = "b", tl.pos = "d", tl.srt = 60, col=cols(100), outline=TRUE)
dev.off()
pdf("figures/plot-corrplot2.pdf",width=6,height=6)
corrplot(llag(mkt),method="ellipse",is.corr=FALSE,
cl.pos = "b", tl.pos = "d", tl.srt = 60, col=cols(100), outline=TRUE)
dev.off()
## ----echo=TRUE, results='hide'-------------------------------------------
model <- setModel(drift = "x", diffusion = matrix( "x*e", 1,1))
T <- 1
xinit <- 150
K <- 100
f <- list( expression(x/T), expression(0))
F <- 0
e <- 0.5
yuima <- setYuima(model = model,
sampling = setSampling(Terminal=T, n=1000))
yuima <- setFunctional( yuima, f=f,F=F, xinit=xinit,e=e)
## ----echo=TRUE-----------------------------------------------------------
str(yuima@functional)
## ----echo=TRUE-----------------------------------------------------------
F0 <- F0(yuima)
F0
## ----echo=TRUE,results='hide'--------------------------------------------
rho <- expression(0)
epsilon <- e # noise level
g <- function(x) {
tmp <- (F0 - K) + (epsilon * x)
tmp[(epsilon * x) < (K-F0)] <- 0
tmp
}
## ----echo=TRUE,results='hide'--------------------------------------------
asymp <- asymptotic_term(yuima, block=10, rho, g)
asymp
## ----echo=TRUE-----------------------------------------------------------
asy1 <- asymp$d0 + e * asymp$d1
# 1st order asymp. exp. of asian call price
asy1
asy2 <- asymp$d0 + e * asymp$d1 + e^2* asymp$d2
# 2nd order asymp. exp. of asian call price
asy2
## ----message=FALSE-------------------------------------------------------
library("fExoticOptions")
levy <- LevyAsianApproxOption(TypeFlag = "c", S = xinit, SA = xinit,
X = K, Time = 1, time = 1, r = 0.0, b = 1, sigma = e)@price
levy
## ------------------------------------------------------------------------
a <- 0.9
e <- 0.4
Terminal <- 3
xinit <- 1
K <- 10
drift <- "a * x"
diffusion <- "e * sqrt(x)"
model <- setModel(drift=drift,diffusion=diffusion)
n <- 1000*Terminal
yuima <- setYuima(model = model,
sampling = setSampling(Terminal=Terminal,n=n))
f <- list(c(expression(0)),c(expression(0)))
F <- expression(x)
yuima.ae <- setFunctional(yuima,f=f,F=F,xinit=xinit,e=e)
rho <- expression(0)
F1 <- F0(yuima.ae)
get_ge <- function(x,epsilon,K,F0){
tmp <- (F0 - K) + (epsilon * x[1])
tmp[(epsilon * x[1]) > (K - F0)] <- 0
return( - tmp )
}
g <- function(x){
return(get_ge(x,e,K,F1))
}
time1 <- proc.time()
asymp <- asymptotic_term(yuima.ae,block=100,rho,g)
time2 <- proc.time()
## ------------------------------------------------------------------------
ae.value0 <- asymp$d0
ae.value0
ae.value1 <- asymp$d0 + e * asymp$d1
ae.value1
ae.value2 <- as.numeric(asymp$d0 + e * asymp$d1 + e^2 * asymp$d2)
ae.value2
ae.time <- time2 - time1
ae.time
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter2.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ----mod1----------------------------------------------------------------
mod1 <- setPoisson(intensity="lambda", df=list("dconst(z,1)"))
mod1
## ----poi1,fig.keep='none'------------------------------------------------
Terminal <- 30
samp <- setSampling(T=Terminal,n=3000)
set.seed(123)
poisson1 <- simulate(mod1, true.par=list(lambda=1),sampling=samp)
poisson1
plot(poisson1)
## ----plot-poi1,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi1.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson1,type="S")
dev.off()
## ----eval=FALSE----------------------------------------------------------
## setPoisson(intensity="lambda", df=list("dconst(z,1)"), scale=5)
## setPoisson(intensity="lambda", df=list("dconst(z,5)"))
## ----mod2,fig.keep='none'------------------------------------------------
mod2 <- setPoisson(intensity="lambda", df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson2 <- simulate(mod2, sampling=samp,
true.par=list(lambda=1,mu=0, sigma=2))
poisson2
plot(poisson2)
## ----plot-poi2,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi2.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson2,type="S")
dev.off()
## ----fig.keep='none'-----------------------------------------------------
mod3 <- setPoisson(intensity="lambda",
df=list("dNIG(z,alpha,beta,gamma,mu)"))
poisson3 <- simulate(mod3, sampling=samp,
true.par=list(lambda=10,alpha=2,beta=0.3,gamma=1,mu=0))
poisson3
## ----message=FALSE-------------------------------------------------------
require(fBasics)
mod4 <- setPoisson(intensity="lambda",
df=list("dnig(z,alpha,beta,gamma)"))
poisson4 <- simulate(mod4, sampling=samp,
true.par=list(lambda=10,alpha=2,beta=0.3,gamma=1))
poisson4
## ----mod5,fig.keep='none'------------------------------------------------
mod5 <- setPoisson(intensity="alpha+beta*t",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson5 <- simulate(mod5, sampling=samp,
true.par=list(alpha=2,beta=.5,mu=0, sigma=2))
plot(poisson5)
f <- function(t,alpha,beta) alpha + beta*t
curve(f(x,alpha=2,beta=0.5)-20,0,30,add=TRUE,col="red",lty=3,lwd=2)
## ----plot-poi5,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi5.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson5,type="S")
curve(f(x,alpha=2,beta=0.5)-20,0,30,add=TRUE,col="red",lty=3,lwd=2)
dev.off()
## ----mod6,fig.keep='none'------------------------------------------------
mod6 <- setPoisson(intensity="theta*t^(theta-1)",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson6 <- simulate(mod6, sampling=samp,
true.par=list(theta=1.5,mu=0, sigma=2))
plot(poisson6)
f <- function(t,theta) theta*t^(theta-1)
curve(f(x,theta=1.5),0,30,add=TRUE,col="red",lty=3,lwd=2)
## ----plot-poi6,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi6.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson6,type="S")
curve(f(x,theta=1.5),0,30,add=TRUE,col="red",lty=3,lwd=2)
dev.off()
## ----mod7,fig.keep='none'------------------------------------------------
mod7 <- setPoisson(intensity="beta*exp(-lambda*t)",
df=list("dexp(z,gamma)"))
set.seed(123)
poisson7 <- simulate(mod7, sampling=samp,
true.par=list(lambda=.2,beta=10,gamma=1))
plot(poisson7)
f <- function(t,beta,lambda) beta*exp(-lambda*t)
curve(f(x,beta=10,lambda=0.2),0,30,add=TRUE,col="red",lty=3,lwd=2)
## ----plot-poi7,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi7.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson7,type="S")
curve(f(x,beta=10,lambda=0.2),0,30,add=TRUE,col="red",lty=3,lwd=2)
dev.off()
## ----mod8,fig.keep='none'------------------------------------------------
mod8 <- setPoisson(intensity="0.5*a*(1+cos(omega*t+phi))+lambda",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson8 <- simulate(mod8, sampling=samp,
true.par=list(a=2,omega=0.5,phi=3.14,lambda=5,mu=0,sigma=1))
plot(poisson8)
f <- function(t,a,omega,phi,lambda) 0.5*a*(1+cos(omega*t+phi))+lambda
curve(f(x,a=2,omega=0.5,phi=3.14,lambda=5),0,30,add=TRUE,
col="red",lty=3,lwd=2)
## ----plot-poi8,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi8.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson8,type="S")
curve(f(x,a=2,omega=0.5,phi=3.14,lambda=5),0,30,add=TRUE, col="red",lty=3,lwd=2)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
mod9 <- setPoisson(intensity="a*cos(theta*t)+lambda",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson9 <- simulate(mod9, sampling=samp,
true.par=list(a=1,theta=0.5,lambda=5,mu=0,sigma=1))
plot(poisson9)
f <- function(t,a,theta,lambda) a*cos(theta*t)+lambda
curve(f(x,a=1,theta=0.5,lambda=5),0,30,add=TRUE,col="red",lty=3,lwd=2)
## ----plot-poi9,echo=FALSE,results='hide'---------------------------------
pdf("figures/plot-poi9.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson9,type="S")
curve(f(x,a=1,theta=0.5,lambda=5),0,30,add=TRUE,col="red",lty=3,lwd=2)
dev.off()
## ----mod10,fig.keep='none'-----------------------------------------------
mod10 <- setPoisson(intensity="lambda*t",
df=list("dmvnorm(z,c(0.15,-0.1),matrix(c(2,-1.9,-1.9,4.3),2,2))"),
dimension=2)
set.seed(123)
poisson10 <- simulate(mod10, true.par=list(lambda=5), sampling=samp)
poisson10
plot(poisson10)
## ----plot-poi10,echo=FALSE,results='hide'--------------------------------
pdf("figures/plot-poi10.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson10,type="S")
dev.off()
## ----mod111,fig.keep='none'----------------------------------------------
mod11 <- setPoisson(intensity="lambda*t",
df=list("dmvnorm(z,c(0.01,-0.01,.05),
matrix(c(1,.5,0,.5,1,0,0,0,1),3,3))"),
dimension=3)
set.seed(123)
poisson11 <- simulate(mod11, true.par=list(lambda=5),
sampling=samp,xinit=c(-100,200,300))
plot(poisson11)
## ----plot-poi11,echo=FALSE,results='hide'--------------------------------
pdf("figures/plot-poi11.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(poisson11,type="S")
dev.off()
## ------------------------------------------------------------------------
r2DNIG <- function(n,alpha){
alpha <- 2
beta <- c(0,0)
delta0 <- 0.55
mu <- c(0,0)
Lambda <- matrix(c(1,0,0,1),2,2)
t(rNIG(n,alpha=alpha,beta=beta,delta=delta0,mu=mu,Lambda=Lambda))
}
# the next fake density plays no role in simulation
# but it is needed for model specification
d2DNIG <- function(n,alpha){
rep(0,2)
}
## ------------------------------------------------------------------------
mod12 <- setPoisson(intensity="lambda", df=list("d2DNIG(z,)"),
dim=2)
set.seed(123)
poisson12 <- simulate(mod12, true.par= list(lambda=1),
sampling=samp)
poisson12
## ------------------------------------------------------------------------
rMydis <- function(n,a=1){
cbind(rnorm(n), rexp(n), rNIG(n,1,1,1,1))
}
dMydis <- function(n,a=1){
rep(0,3)
}
mod13 <- setPoisson(intensity="lambda*t",
df=list("dMydis(z,1)"), dimension=3)
set.seed(123)
poisson13 <- simulate(mod13, true.par=list(lambda=5),
sampling=samp)
poisson13
## ------------------------------------------------------------------------
mod14 <- setPoisson(intensity="alpha+lambda*t",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson14 <- simulate(mod14, sampling=samp,
true.par=list(alpha=1,lambda=.5,mu=0, sigma=2))
poisson14
fit14 <- qmle(poisson14, start=list(alpha=2,lambda=1,mu=0,sigma=1),
lower=list(alpha=0.1, lambda=0.1,mu=-1,sigma=0.1),
upper=list(alpha=10,lambda=10,mu=3,sigma=4),
method="L-BFGS-B")
coef(fit14)
## ------------------------------------------------------------------------
summary(fit14)
## ----results='hide'------------------------------------------------------
mod15 <- setPoisson(intensity="lambda",
df=list("dNIG(z,alpha,beta,gamma,mu)"))
set.seed(123)
poisson15 <- simulate(mod15,sampling=samp,
true.par=list(lambda=10,alpha=2,beta=0.3,gamma=1,mu=0))
poisson15
fit15 <- qmle(poisson15,
start=list(beta=5,lambda=2,gamma=0.5,alpha=1,mu=0),
lower=list(alpha=1,beta=0.1,lambda=0.1,gamma=0.1,mu=-1),
upper=list(alpha=5,beta=0.99,lambda=20,gamma=2,mu=2),
method="L-BFGS-B")
summary(fit15)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit15)),width=60))
## ------------------------------------------------------------------------
mod16 <- setPoisson(intensity="beta*exp(-lambda*t)",
df=list("dexp(z,lambda)"))
set.seed(123)
poisson16 <- simulate(mod16, true.par=list(lambda=.2,beta=10),
sampling=samp)
poisson16
fit16 <- qmle(poisson16,
start=list(beta=.5,lambda=2),
lower=list(beta=0.1,lambda=0.1),
upper=list(beta=20,lambda=10),
method="L-BFGS-B")
summary(fit16)
## ----results='hide'------------------------------------------------------
mod17 <- setPoisson(intensity="lambda*t^(lambda-1)",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson17 <- simulate(mod17, true.par=list(lambda=2,mu=0, sigma=2),
sampling=samp)
poisson17
fit17 <- qmle(poisson17,
start=list(lambda=5,mu=0,sigma=1),
lower=list(lambda=0.1,mu=-1,sigma=0.1),
upper=list(lambda=10,mu=3,sigma=4),
method="L-BFGS-B")
summary(fit17)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit17)),width=60))
## ----results='hide'------------------------------------------------------
mod18 <- setPoisson(intensity="0.5*a*(1+cos(omega*t+phi))+lambda",
df=list("dnorm(z,mu,sigma)"))
set.seed(123)
poisson18 <- simulate(mod18, sampling=samp,
true.par=list(a=2,omega=0.5,phi=3.14,lambda=5,mu=0,sigma=1))
fit18 <- qmle(poisson18,
start=list(a=1, omega=0.2, phi=1, lambda=2, mu=1, sigma=2),
lower=list(a=0.1, omega=0.1, phi=0.1, lambda=0.1, mu=-2, sigma=0.1),
upper=list(a=5, omega=1, phi=5, lambda=10, mu=2, sigma=3),
method="L-BFGS-B")
summary(fit18)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(summary(fit18)),width=60))
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter3.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ------------------------------------------------------------------------
set.seed(123)
mu <- 0
sigma <- 1
lambda <- 10
samp <- setSampling(Terminal=10, n=1000)
mod10b <- setPoisson(intensity="lambda", df=list("dnorm(z,mu,sigma)"))
y10b <- simulate(mod10b,sampling=samp,
true.par=list(lambda=lambda,mu=0.1, sigma=2))
y10b
## ----fig.keep='none'-----------------------------------------------------
BGmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
measure.type="code", measure=list(df="rbgamma(z, delta.plus=1.4,
gamma.plus=0.3, delta.minus=2,
gamma.minus=0.6)"))
n <- 1000
samp <- setSampling(Terminal=1, n=n)
BGyuima <- setYuima(model=BGmodel, sampling=samp)
set.seed(127)
for (i in 1:5) {
result <- simulate(BGyuima)
plot(result,xlim=c(0,1),ylim=c(-6,6),
main="Paths of bilateral gamma process",col=i,par(new=T))
}
## ----plot-BGprocess,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-BGprocess.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(127)
for (i in 1:5) {
result <- simulate(BGyuima)
plot(result,xlim=c(0,1),ylim=c(-6,6),
main="Paths of bilateral gamma process",col=i,par(new=T))
}
dev.off()
## ----fig.keep='none'-----------------------------------------------------
VGmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
measure.type="code", measure=list(df="rbgamma(z, delta.minus=2,
gamma.minus=0.6, delta.plus=2, gamma.plus=0.3)"))
VGyuima <- setYuima(model=VGmodel, sampling=samp)
set.seed(127)
for (i in 1:5) {
result <- simulate(VGyuima)
plot(result,xlim=c(0,1),ylim=c(-4,12),
main="Paths of variance gamma process",col=i,par(new=T))
}
## ----plot-VGprocess,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-VGprocess.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(127)
for (i in 1:5) {
result <- simulate(VGyuima)
plot(result,xlim=c(0,1),ylim=c(-4,12),
main="Paths of variance gamma process",col=i,par(new=T))
}
dev.off()
## ----eval=FALSE----------------------------------------------------------
## Gmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
## measure.type="code", measure=list(df="rgamma(z,
## shape=0.7, scale=1)"))
## n <- 10000
## samp <- setSampling(Terminal=1, n=n)
## Gyuima <- setYuima(model=Gmodel, sampling=samp)
## set.seed(129)
## for (i in 1:5){
## result <- simulate(Gyuima)
## plot(result,xlim=c(0,1),ylim=c(-0.1,1.2),
## main="Paths of gamma process",col=i,par(new=T))
## }
## ----plot-Gprocess,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-Gprocess.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
Gmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
measure.type="code", measure=list(df="rgamma(z,
shape=0.7, scale=1)"))
n <- 10000
samp <- setSampling(Terminal=1, n=n)
Gyuima <- setYuima(model=Gmodel, sampling=samp)
set.seed(129)
for (i in 1:5){
result <- simulate(Gyuima)
plot(result,xlim=c(0,1),ylim=c(-0.1,1.2),
main="Paths of gamma process",col=i,par(new=T))
}
dev.off()
## ----eval=FALSE----------------------------------------------------------
## n <- 5
## sampling <- setSampling(Terminal=1, n=n)
## Gmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
## measure.type="code", measure=list(df="rgamma(z,
## shape=0.7, scale=1)"))
## Gyuima <- setYuima(model=Gmodel, sampling=samp)
## simdata <- NULL
## set.seed(127)
## for (i in 1:3000){
## result <- simulate(Gyuima)
## x1 <- result@[email protected][n+1,1]
## simdata <- c(simdata,as.numeric(x1))
## }
## hist(simdata, xlim=c(0,2), ylim=c(0,3), breaks=100, freq=FALSE,
## main=expression(paste("Distribution of ", X[1],
## " and Density of Gamma(0.7,1)")))
## curve(dgamma(x,0.7,1),add=TRUE,col="red")
## ----plot-dgamma,echo=FALSE,results='hide'-------------------------------
pdf("figures/plot-dgamma.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
n <- 5
samp <- setSampling(Terminal=1, n=n)
Gmodel <- setModel(drift="0", xinit="0", jump.coeff="1",
measure.type="code", measure=list(df="rgamma(z,
shape=0.7, scale=1)"))
Gyuima <- setYuima(model=Gmodel, sampling=samp)
simdata <- NULL
set.seed(127)
for (i in 1:3000){
result <- simulate(Gyuima)
x1 <- result@[email protected][n+1,1]
simdata <- c(simdata,as.numeric(x1))
}
hist(simdata, xlim=c(0,2), ylim=c(0,3), breaks=100, freq=FALSE,
main=expression(paste("Distribution of ", X[1],
" and Density of Gamma(0.7,1)")))
curve(dgamma(x,0.7,1),add=TRUE,col="red")
dev.off()
## ----fig.keep='none'-----------------------------------------------------
delta <- 1
gamma <- 2
set.seed(127)
x <- rIG(100000,delta,gamma)
hist(x,xlim=c(0,2),ylim=c(0,2),breaks=100,freq=FALSE)
curve(dIG(x,delta,gamma),add=TRUE,col="red",
from=min(x), to=max(x), n=500)
mean(x)
var(x)
## ----plot-dIG,echo=FALSE,results='hide'----------------------------------
pdf("figures/plot-dIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
hist(x,xlim=c(0,2),ylim=c(0,2),breaks=100,freq=FALSE)
curve(dIG(x,delta,gamma),add=TRUE,col="red",
from=min(x), to=max(x), n=500)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
IGmodel <- setModel(drift=0, xinit=0, jump.coeff=1,
measure.type="code", measure=list(df="rIG(z, delta=1, gamma=2)"))
n <- 1000
samp <- setSampling(Terminal=1, n=n)
IGyuima <- setYuima(model=IGmodel, sampling=samp)
set.seed(127)
for (i in 1:5){
result <- simulate(IGyuima,xinit=0)
plot(result, xlim=c(0,1), ylim=c(0,1),
main="Paths of IG process (delta=1, gamma=2)",par(new=T),col=i)
}
## ----plot-dIGproc,echo=FALSE,results='hide'------------------------------
pdf("figures/plot-dIGproc.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(127)
for (i in 1:5){
result <- simulate(IGyuima,xinit=0)
plot(result, xlim=c(0,1), ylim=c(0,1),
main="Paths of IG process (delta=1, gamma=2)",par(new=T),col=i)
}
dev.off()
## ----eval=FALSE----------------------------------------------------------
## n <- 5
## samp <- setSampling(Terminal=1, n=n)
## IGyuima <- setYuima(model=IGmodel, sampling=samp)
## IGsimdata <- NULL
## for (i in 1:3000){
## result <- simulate(IGyuima)
## x1 <- result@[email protected][n+1,1]
## IGsimdata <- c(IGsimdata,as.numeric(x1))
## }
## hist(IGsimdata,xlim=c(0,2), ylim=c(0,2), breaks=100, freq=FALSE,
## main=expression(paste("Distribution of ",X[1],
## " and Density of IG(1,2)")))
## curve(dIG(x,delta,gamma),add=TRUE,col="red",
## from = 0.001, to = 5, n=500)
## ----plot-IGprocd,echo=FALSE,results='hide'------------------------------
pdf("figures/plot-IGprocd.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
n <- 5
samp <- setSampling(Terminal=1, n=n)
IGyuima <- setYuima(model=IGmodel, sampling=samp)
IGsimdata <- NULL
for (i in 1:3000){
result <- simulate(IGyuima)
x1 <- result@[email protected][n+1,1]
IGsimdata <- c(IGsimdata,as.numeric(x1))
}
hist(IGsimdata,xlim=c(0,2), ylim=c(0,2), breaks=100, freq=FALSE,
main=expression(paste("Distribution of ",X[1]," and Density of IG(1,2)")))
curve(dIG(x,delta,gamma),add=TRUE,col="red",
from = 0.001, to = 5, n=500)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
rep <- 3000000
set.seed(129)
X1 <- rpts(rep,0.5,0.2,1)
hist(X1,xlim=c(0,3),ylim=c(0,3),breaks=100,
main=expression(X[1]),probability=TRUE)
X05 <- rpts(rep,0.5,0.1,1)
X05.prime <- rpts(rep,0.5,0.1,1)
Xsum <- X05+X05.prime
summary(X1)
summary(Xsum)
ks.test(X1,Xsum)
## ----plot-X1pts,echo=FALSE,results='hide'--------------------------------
pdf("figures/plot-X1pts.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
hist(X1,xlim=c(0,3),ylim=c(0,3),breaks=100,main=expression(paste(X[1]," positive tempered stable distribution")),probability=TRUE)
dev.off()
rm(X1)
rm(Xsum)
rm(X05)
rm(X05.prime)
## ----fig.keep='none'-----------------------------------------------------
lambda <- 2
alpha <- 1.5
beta <- -0.7
mu <- 3
xinit <- 0
gamma <- sqrt(alpha^2-beta^2)
n <- 1000
T <- 1.8
VGPmodel <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rvgamma(z,lambda,alpha,beta,mu)"))
samp <- setSampling(Terminal=T, n=n)
VGPyuima <- setYuima(model=VGPmodel, sampling=samp)
# simulation
set.seed(127)
for (i in 1:7) {
result <- simulate(VGPyuima, xinit=xinit,
true.par=list(lambda=lambda,alpha=alpha,beta=beta,mu=mu))
plot(result,xlim=c(0,T),ylim=c(-5,6),col=i,
main="Paths of variance gamma process",par(new=T))
}
## ----plot-VGprocess2,echo=FALSE,results='hide'---------------------------
pdf("figures/plot-VGprocess2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(127)
for (i in 1:7) {
result <- simulate(VGPyuima, xinit=xinit,
true.par=list(lambda=lambda,alpha=alpha,beta=beta,mu=mu))
plot(result,xlim=c(0,T),ylim=c(-5,6),col=i, main="Paths of variance gamma process",par(new=T))
}
dev.off()
## ----eval=FALSE----------------------------------------------------------
## n <- 5
## samp <- setSampling(Terminal=T, n=n)
## VGPyuima <- setYuima(model=VGPmodel, sampling=samp)
## VGPsimdata <- NULL
## for (i in 1:5000){
## result <- simulate(VGPyuima, xinit=xinit,
## true.par=list(lambda=lambda,alpha=alpha,beta=beta,mu=mu))
## x1 <- result@[email protected][n+1,1]
## VGPsimdata <- c(VGPsimdata,as.numeric(x1[1]))
## }
## hist(VGPsimdata,xlim=c(-7,10),ylim=c(0,0.22),breaks=100,freq=FALSE,
## main=expression(paste("Distribution of ",X[1.8],
## " and Density of VG")))
## curve(dvgamma(x,lambda*T,alpha,beta,mu*T),add=TRUE,col="red")
## ----plot-VGPproc2,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-VGPproc2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
n <- 5
samp <- setSampling(Terminal=T, n=n)
VGPyuima <- setYuima(model=VGPmodel, sampling=samp)
VGPsimdata <- NULL
for (i in 1:5000){
result <- simulate(VGPyuima, xinit=xinit,
true.par=list(lambda=lambda,alpha=alpha,beta=beta,mu=mu))
x1 <- result@[email protected][n+1,1]
VGPsimdata <- c(VGPsimdata,as.numeric(x1[1]))
}
hist(VGPsimdata,xlim=c(-7,10),ylim=c(0,0.22),breaks=100,freq=FALSE,
main=expression(paste("Distribution of ",X[1.8], " and Density of VG")))
curve(dvgamma(x,lambda*T,alpha,beta,mu*T),add=TRUE,col="red")
dev.off()
## ----fig.keep='none'-----------------------------------------------------
delta <- 0.5
alpha <- 1.5
beta <- -0.7
mu <- 3
gamma <- sqrt(alpha^2-beta^2)
n <- 10000
T <- 1.8
set.seed(127)
normal.rn <- rnorm(n,0,1)
iv.rn <- rIG(n,delta*T,gamma)
z <- mu*T+beta*iv.rn+sqrt(iv.rn)*normal.rn
title <- expression(paste(NIGP[1.8],
" built by subordination (green) and rNIG (white)"))
nig.rn <- rNIG(n,alpha,beta,delta*T,mu*T)
hist(z,xlim=c(-1,10),ylim=c(0,0.61),breaks=100, freq=FALSE,
col="green", main=title, xlab=expression(X[1.8]) )
curve(dNIG(x,alpha,beta,delta*T,mu*T),add=TRUE,col="red")
par(new=T)
hist(nig.rn,xlim=c(-1,10),ylim=c(0,0.61),breaks=100,
freq=FALSE, main="", xlab="")
## ----plot-NIGproc2,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-NIGproc2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
hist(z,xlim=c(-1,10),ylim=c(0,0.61),breaks=100, freq=FALSE,
col="green", main=title, xlab=expression(X[1.8]) )
curve(dNIG(x,alpha,beta,delta*T,mu*T),add=TRUE,col="red")
par(new=T)
hist(nig.rn,xlim=c(-1,10),ylim=c(0,0.61),breaks=100, freq=FALSE,
main="", xlab="")
dev.off()
## ----eval=FALSE----------------------------------------------------------
## delta1 <- 0.5
## alpha <- 1.5
## beta <- -0.7
## mu <- 3
## xinit <- 0
## gamma <- sqrt(alpha^2-beta^2)
## n <- 1000
## T <- 1.8
## NIG2model <- setModel(drift=0, jump.coeff=1, measure.type="code",
## measure=list(df="rNIG(z,alpha,beta,delta1,mu)"))
## samp <- setSampling(Terminal=T, n=n)
## NIG2yuima <- setYuima(model=NIG2model, sampling=samp)
## set.seed(127)
## for (i in 1:10) {
## result <- simulate(NIG2yuima, xinit=xinit,
## true.par=list(delta1=delta1, alpha=alpha, beta=beta,
## mu=mu, gamma=gamma))
## plot(result,xlim=c(0,T),ylim=c(-1,10),col=i,
## main="Paths of NIG process",par(new=T))
## }
## ----plot-NIGproc3,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-NIGproc3.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
delta1 <- 0.5
alpha <- 1.5
beta <- -0.7
mu <- 3
xinit <- 0
gamma <- sqrt(alpha^2-beta^2)
n <- 1000
T <- 1.8
NIG2model <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rNIG(z,alpha,beta,delta1,mu)"))
samp <- setSampling(Terminal=T, n=n)
NIG2yuima <- setYuima(model=NIG2model, sampling=samp)
set.seed(127)
for (i in 1:10) {
result <- simulate(NIG2yuima, xinit=xinit,
true.par=list(delta1=delta1, alpha=alpha, beta=beta,
mu=mu, gamma=gamma))
plot(result,xlim=c(0,T),ylim=c(-1,10),col=i,
main="Paths of NIG process",par(new=T))
}
dev.off()
## ----fig.keep='none'-----------------------------------------------------
n <- 5
samp <- setSampling(Terminal=T, n=n)
NIG2yuima <- setYuima(model=NIG2model, sampling=samp)
NIG2data <- NULL
for (i in 1:3000){
result <- simulate(NIG2yuima, xinit=xinit,
true.par=list(delta1=delta1, alpha=alpha, beta=beta,
mu=mu, gamma=gamma))
x1 <- result@[email protected][n+1,1]
NIG2data <- c(NIG2data,as.numeric(x1[1]))
}
hist(NIG2data,xlim=c(2,8),ylim=c(0,0.8),breaks=100, freq=FALSE,
main=expression(paste("Distribution of ",X[1.8],
" and Density of NIG")))
curve(dNIG(x,alpha,beta,delta*T,mu*T),add=TRUE,col="red")
## ----plot-NIGproc4,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-NIGproc4.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
hist(NIG2data,xlim=c(2,8),ylim=c(0,0.8),breaks=100, freq=FALSE,
main=expression(paste("Distribution of ",X[1.8], " and Density of NIG")))
curve(dNIG(x,alpha,beta,delta*T,mu*T),add=TRUE,col="red")
dev.off()
## ----fig.keep='none'-----------------------------------------------------
nrep <- 100000
alpha <- 0.5
delta <- 0.2
gamma <- 1
beta <- 1
mu <- -0.7
Lambda <- matrix(1,1,1)
t <- 1.5
par(mfrow=c(2,2))
set.seed(127)
x <- rnts(nrep,alpha,delta*t,gamma,beta,mu*t,Lambda)
s <- rpts(nrep,alpha,delta*t,gamma)
w <- rnorm(nrep,0,1)
y <- rep(mu*t,nrep) + beta*s + sqrt(s)*w
hist(x,xlim=c(-3,3),ylim=c(0,1.2),breaks=200,
main=expression(X[t]),probability=TRUE)
hist(y,xlim=c(-3,3),ylim=c(0,1.2),breaks=200,
main=expression(Y[t]),probability=TRUE,col="red")
## experiment by convolution
nrep <- 3000000
Xt <- rnts(nrep,alpha,delta*t,gamma,beta,mu*t,Lambda)
X05 <- rnts(nrep,alpha,delta*t/2,gamma,beta,mu*t/2,Lambda)
X05.prime <- rnts(nrep,alpha,delta*t/2,gamma,beta,mu*t/2,Lambda)
Xsum <- X05+X05.prime
hist(Xt,xlim=c(-3,3),ylim=c(0,1.2),breaks=300,
main=expression(X[t]),probability=TRUE)
hist(Xsum,xlim=c(-3,3),ylim=c(0,1.2),breaks=300,
main=expression(paste(X[t/2]+X[t/2],"'")),
probability=TRUE,col="red")
ks.test(Xt,Xsum)
## ----plot-NTSPproc,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-NTSPproc.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
par(mfrow=c(2,2))
hist(x,xlim=c(-3,3),ylim=c(0,1.2),breaks=200,
main=expression(X[t]),probability=TRUE)
hist(y,xlim=c(-3,3),ylim=c(0,1.2),breaks=200,
main=expression(Y[t]),probability=TRUE,col="red")
hist(Xt,xlim=c(-3,3),ylim=c(0,1.2),breaks=300,
main=expression(X[t]),probability=TRUE)
hist(Xsum,xlim=c(-3,3),ylim=c(0,1.2),breaks=300,
main=expression(paste(X[t/2]+X[t/2],"'")),
probability=TRUE,col="red")
dev.off()
rm(Xt)
rm(X05)
rm(X05.prime)
rm(Xsum)
## ----eval=FALSE----------------------------------------------------------
## alpha <- 0.5
## beta <- -0.4
## sigma <- 0.7
## gamma <- 0.5
## n <- 1000
## T <- 1.8
## ASmodel <- setModel(drift=0, jump.coeff=1, measure.type="code",
## measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
## samp <- setSampling(Terminal=T, n=n)
## ASyuima <- setYuima(model=ASmodel, sampling=samp)
## set.seed(129)
## for (i in 1:10) {
## result <- simulate(ASyuima, true.par=list(alpha=alpha,
## beta=beta,sigma=sigma,gamma=gamma))
## plot(result,xlim=c(0,T),ylim=c(-40,10),col=i,
## main=expression(paste("Paths of stable process (",
## alpha==0.5,",",beta==-0.4,")")),par(new=T))
## }
##
## #param2
## alpha <- 1
## beta <- -0.4
## sigma <- 0.7
## gamma <- 0.5
## AS2model <- setModel(drift=0, jump.coeff=1, measure.type="code",
## measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
## AS2yuima <- setYuima(model=AS2model, sampling=samp)
## for (i in 1:10) {
## result <- simulate(AS2yuima, true.par=list(alpha=alpha,
## beta=beta,sigma=sigma,gamma=gamma))
## plot(result,xlim=c(0,T),ylim=c(-5,5),col=i,
## main=expression(paste("Paths of stable process (",
## alpha==1,",",beta==-0.4,")")),par(new=T))
## }
##
## #param3
## alpha <- 1
## beta <- 0.4
## sigma <- 0.7
## gamma <- 0.5
## AS3model <- setModel(drift=0, jump.coeff=1, measure.type="code",
## measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
## AS3yuima <- setYuima(model=AS3model, sampling=samp)
## for (i in 1:10) {
## result <- simulate(AS3yuima, true.par=list(alpha=alpha,
## beta=beta,sigma=sigma,gamma=gamma))
## plot(result,xlim=c(0,T),ylim=c(-5,5),col=i,
## main=expression(paste("Paths of stable process (",
## alpha==1,",",beta==0.4,")")),par(new=T))
## }
##
## #param4
## alpha <- 1.5
## beta <- 0.4
## sigma <- 0.7
## gamma <- 0.5
## AS4model <- setModel(drift=0, jump.coeff=1, measure.type="code",
## measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
## AS4yuima <- setYuima(model=AS4model, sampling=samp)
## for (i in 1:10) {
## result <- simulate(AS4yuima, true.par=list(alpha=alpha,
## beta=beta, sigma=sigma,gamma=gamma))
## plot(result,xlim=c(0,T),ylim=c(-3,5),col=i,
## main=expression(paste("Paths of stable process (",
## alpha==1.5,",",beta==0.4,")")),par(new=T))
## }
## ----plot-ASproc,echo=FALSE,results='hide'-------------------------------
pdf("figures/plot-ASproc1.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
alpha <- 0.5
beta <- -0.4
sigma <- 0.7
gamma <- 0.5
n <- 1000
T <- 1.8
ASmodel <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
samp <- setSampling(Terminal=T, n=n)
ASyuima <- setYuima(model=ASmodel, sampling=samp)
set.seed(129)
for (i in 1:10) {
result <- simulate(ASyuima, true.par=list(alpha=alpha,
beta=beta,sigma=sigma,gamma=gamma))
plot(result,xlim=c(0,T),ylim=c(-40,10),col=i,
main=expression(paste("Paths of stable process (",
alpha==0.5,",",beta==-0.4,")")),par(new=T))
}
dev.off()
pdf("figures/plot-ASproc2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
#param2
alpha <- 1
beta <- -0.4
sigma <- 0.7
gamma <- 0.5
AS2model <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
AS2yuima <- setYuima(model=AS2model, sampling=samp)
for (i in 1:10) {
result <- simulate(AS2yuima, true.par=list(alpha=alpha,
beta=beta,sigma=sigma,gamma=gamma))
plot(result,xlim=c(0,T),ylim=c(-5,5),col=i,
main=expression(paste("Paths of stable process (",
alpha==1,",",beta==-0.4,")")),par(new=T))
}
dev.off()
pdf("figures/plot-ASproc3.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
#param3
alpha <- 1
beta <- 0.4
sigma <- 0.7
gamma <- 0.5
AS3model <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
AS3yuima <- setYuima(model=AS3model, sampling=samp)
for (i in 1:10) {
result <- simulate(AS3yuima, true.par=list(alpha=alpha,
beta=beta,sigma=sigma,gamma=gamma))
plot(result,xlim=c(0,T),ylim=c(-5,5),col=i,
main=expression(paste("Paths of stable process (",
alpha==1,",",beta==0.4,")")),par(new=T))
}
dev.off()
pdf("figures/plot-ASproc4.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
#param4
alpha <- 1.5
beta <- 0.4
sigma <- 0.7
gamma <- 0.5
AS4model <- setModel(drift=0, jump.coeff=1, measure.type="code",
measure=list(df="rstable(z,alpha,beta,sigma,gamma)"))
AS4yuima <- setYuima(model=AS4model, sampling=samp)
for (i in 1:10) {
result <- simulate(AS4yuima, true.par=list(alpha=alpha,beta=beta,
sigma=sigma,gamma=gamma))
plot(result,xlim=c(0,T),ylim=c(-3,5),col=i,
main=expression(paste("Paths of stable process (",
alpha==1.5,",",beta==0.4,")")),par(new=T))
}
dev.off()
## ----fig.keep='none'-----------------------------------------------------
modJump <- setModel(drift = c("-theta*x"), diffusion = "sigma",
jump.coeff=c("gamma+x/sqrt(1+x^2)"),
measure = list(intensity="lambda",df=list("dnorm(z, -3, 1)")),
measure.type="CP", solve.variable="x")
modJump
samp <- setSampling(n=10000,Terminal=10)
set.seed(125)
X <- simulate(modJump, xinit=2, sampling=samp,
true.par= list(theta=2, sigma=0.5,gamma=0.3,lambda=0.5))
plot(X)
## ----plot-modelJump,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-modelJump.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(X)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
x0 <- 2
a <- 0.1
c <- -1
model.ig <- setModel(drift="a*x", xinit=x0, jump.coeff=c,
measure.type="code", measure=list(df="rIG(z, delta0, gamma)"))
model.ig
sampling.ig <- setSampling(Terminal=10, n=10000)
yuima.ig <- setYuima(model=model.ig, sampling=sampling.ig)
set.seed(128)
result.ig <- simulate(yuima.ig,true.par=list(delta0=0.55,gamma=2))
plot(result.ig)
## ----plot-modelIG,echo=FALSE,results='hide'------------------------------
pdf("figures/plot-modelIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(128)
result.ig <- simulate(yuima.ig,true.par=list(delta0=0.55,gamma=2))
plot(result.ig)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
x0 <- 2
a <- 0.1
c <- -1
model.nig <- setModel(drift="a*x", xinit=x0, jump.coeff=c,
measure.type="code",measure=list(df="rNIG(z, alpha,
beta, delta0, mu)"))
sampling.nig <- setSampling(Terminal=10, n=10000)
yuima.nig <- setYuima(model=model.nig, sampling=sampling.ig)
set.seed(128)
result.nig <- simulate(yuima.nig,true.par=list(alpha=2, beta=0,
delta0=0.55, mu=0))
plot(result.nig)
## ----plot-modelNIG,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-modelNIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(128)
result.nig <- simulate(yuima.nig,true.par=list(alpha=2, beta=0,
delta0=0.55, mu=0))
plot(result.nig)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
x0 <- 2
a <- 0.1
c <- -1
Lambda <- matrix(1,1,1)
model.nig <- setModel(drift="a*x", xinit=x0, jump.coeff=c,
measure.type="code",measure=list(df="rNIG(z, alpha,
beta, delta0, mu, Lambda)"))
sampling.nig <- setSampling(Terminal=10, n=10000)
yuima.nig <- setYuima(model=model.nig, sampling=sampling.ig)
set.seed(128)
result.nig <- simulate(yuima.nig,true.par=list(alpha=2,
beta=0, delta0=0.55, mu=0, Lambda=Lambda))
plot(result.nig)
## ----plot-modelNIG2,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-modelNIG2.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(128)
result.nig <- simulate(yuima.nig,true.par=list(alpha=2,
beta=0, delta0=0.55, mu=0, Lambda=Lambda))
plot(result.nig)
dev.off()
## ----fig.keep='none'-----------------------------------------------------
x0 <- c(2,3)
a1 <- function(t,x1,x2){ x1*cos(2*pi*t)-x2*sin(2*pi*t) }
a2 <- function(t,x1,x2){ x1*sin(2*pi*t)+x2*cos(2*pi*t) }
a <- c("a1(t,x1,x2)","a2(t,x1,x2)")
b <- matrix(c("t*x2","1","0","x1"),2,2)
c <- matrix(c("cos(2*pi*t)", "(5-t)*x1","sin(2*pi*t)",1),2,2)
alpha <- 2
beta <- c(0,0)
delta0 <- 0.55
mu <- c(0,0)
Lambda <- matrix(c(1,0,0,1),2,2)
model.mnig <- setModel(drift=a, xinit=x0, diffusion=b,
jump.coeff=c, measure.type="code",
measure=list(df="rNIG(z, alpha, beta, delta0, mu, Lambda)"),
state.variable=c("x1","x2"),solve.variable=c("x1","x2") )
model.mnig
sampling.mnig <- setSampling(Terminal=1, n=10000)
yuima.mnig <- setYuima(model=model.mnig, sampling=sampling.mnig)
set.seed(128)
result.mnig <- simulate(yuima.mnig,true.par=list(alpha=alpha,
beta=beta, delta0=delta0, mu=mu, Lambda=Lambda))
plot(result.mnig)
## ----plot-modelMNIG,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-modelMNIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
set.seed(128)
result.mnig <- simulate(yuima.mnig,true.par=list(alpha=alpha,
beta=beta, delta0=delta0, mu=mu, Lambda=Lambda))
plot(result.mnig)
dev.off()
## ----fig.keep='none', results='hide'-------------------------------------
mod5 <- setModel(drift = c("-theta*x"), diffusion = "sigma",
jump.coeff=c("gamma+x/sqrt(1+x^2)"),
measure = list(intensity="lambda",df=list("dnorm(z, 2, 0.1)")),
measure.type="CP", solve.variable="x")
theta <- 2
sigma <- 0.5
gamma <- 0.3
lambda <- 2.5
T <- 10
N <- 10000
delta <- T/N
h <- T/N
true <- list(theta=theta, sigma=sigma,gamma=gamma,lambda=lambda)
set.seed(125)
X <- simulate(mod5, true.p=true,xinit=2,
sampling=setSampling(n=N,Terminal=T))
plot(X)
r <- h^0.4
est.qmle <- qmle(yuima=X, start=true,
lower=list(theta=1,sigma=0,gamma=0.1,lambda=0.1),
upper=list(theta=3,sigma=2,gamma=0.8,lambda=20), method="L-BFGS-B",
threshold=r)
unlist(true)
summary(est.qmle)
## ----echo=FALSE----------------------------------------------------------
unlist(true)
writeLines(strwrap(capture.output(summary(est.qmle)),width=60))
## ----plot-modelSDEJ,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-modelSDEJ.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(X)
dev.off()
## ------------------------------------------------------------------------
est.qmle1 <- qmle(yuima=X, start=true,
lower=list(theta=1,sigma=0,gamma=0.1,lambda=0.1),
upper=list(theta=3,sigma=2,gamma=0.8,lambda=20), method="L-BFGS-B",
threshold=2) # too large
coef(est.qmle1)
est.qmle2 <- qmle(yuima=X, start=true,
lower=list(theta=1,sigma=0,gamma=0.1,lambda=0.1),
upper=list(theta=3,sigma=2,gamma=10,lambda=1000), method="L-BFGS-B",
threshold=0.03) ## too low
coef(est.qmle2)
## ----fig.keep='none',message=FALSE---------------------------------------
require(quantmod)
getSymbols("ENI.MI",to="2016-12-31")
S <- ENI.MI$ENI.MI.Adjusted
Z <- na.omit(diff(log(S)))
Dt <- 1/252
# geometric Brownian motion estimation
model1 <- setModel(drift="mu*x", diff="sigma*x")
gBm <- setYuima(model=model1, data=setData(S,delta=Dt))
gBm.fit <- qmle(gBm, start=list(mu=0,sigma=1),method="BFGS")
gBm.cf <- coef(gBm.fit)
zMin <- min(Z)
zMax <- max(Z)
# Gaussian-Levy estimation
model3 <- setPoisson( df="dnorm(z,mu,sigma)")
Norm <- setYuima(model=model3, data=setData(cumsum(Z),delta=Dt))
Norm.fit <- qmle(Norm,start=list(mu=1, sigma=1),
lower=list(mu=1e-7,sigma=0.01),method="L-BFGS-B")
Norm.cf <- coef(Norm.fit)
# NIG-Levy estimation
model2 <- setPoisson( df="dNIG(z,alpha,beta,delta1,mu)")
NIG <- setYuima(model=model2, data=setData(cumsum(Z),delta=Dt))
NIG.fit <- qmle(NIG,start=list(alpha=10, beta=1, delta1=1,mu=1),
lower=list(alpha=1,beta=-2, delta1=0.001,mu=0.0001),
method="L-BFGS-B")
NIG.cf <- coef(NIG.fit)
myfgBm <- function(u)
dnorm(u, mean=gBm.cf["mu"], sd=gBm.cf["sigma"])
myfNorm <- function(u)
dnorm(u, mean=Norm.cf["mu"],sd=Norm.cf["sigma"])
myfNIG <- function(u)
dNIG(u, alpha=NIG.cf["alpha"],beta=NIG.cf["beta"],
delta=NIG.cf["delta1"], mu=NIG.cf["mu"])
plot(density(Z,na.rm=TRUE),main="Gaussian versus NIG")
curve(myfgBm, zMin, zMax, add=TRUE, lty=2)
curve(myfNorm, zMin, zMax, col="red", add=TRUE, lty=4)
curve(myfNIG, zMin, zMax, col="blue", add=TRUE,lty=3)
## ----plot-modelExpLevy,echo=FALSE,results='hide'-------------------------
pdf("figures/plot-modelExpLevy.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(density(Z,na.rm=TRUE),main="Gaussian versus NIG")
curve(myfgBm, zMin, zMax, add=TRUE, lty=2)
curve(myfNorm, zMin, zMax, col="red", add=TRUE, lty=4)
curve(myfNIG, zMin, zMax, col="blue", add=TRUE,lty=3)
dev.off()
## ------------------------------------------------------------------------
AIC(gBm.fit)
AIC(Norm.fit)
AIC(NIG.fit)
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter4.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ----sim-mod4AB, echo=TRUE,fig.keep='none'-------------------------------
mod4A <- setModel(drift="3*y", diffusion=1, hurst=0.3, solve.var="y")
mod4A
mod4B <- setModel(drift="3*y", diffusion=1, hurst=0.7, solve.var="y")
mod4B
set.seed(123)
X1 <- simulate(mod4A,sampling=setSampling(n=1000))
X2 <- simulate(mod4B,sampling=setSampling(n=1000))
par(mfrow=c(2,1))
par(mar=c(2,3,1,1))
plot(X1,main="H=0.3")
plot(X2,main="H=0.7")
## ----results='hide'------------------------------------------------------
str(mod4A)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(mod4A)),width=60))
## ----plot-mod4AB,echo=FALSE,results='hide'-------------------------------
pdf("figures/plot-mod4AB.pdf",width=9,height=4)
par(mfrow=c(2,1))
par(mar=c(2,3,1,1))
plot(X1,main="H=0.3")
plot(X2,main="H=0.7")
dev.off()
## ------------------------------------------------------------------------
set.seed(123)
samp <- setSampling(Terminal=100, n=10000)
mod <- setModel(drift="-lambda*x", diffusion="sigma", hurst=NA)
ou <- setYuima(model=mod, sampling=samp)
fou <- simulate(ou, xinit=1,
true.param=list(lambda=2, sigma=1), hurst=0.7)
fou
## ------------------------------------------------------------------------
qgv(fou)
## ------------------------------------------------------------------------
mmfrac(fou)
## ----results='hide'------------------------------------------------------
data(MWK151)
str(MWK151)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(MWK151)),width=60))
## ----MWK151, echo=TRUE,fig.keep='none'-----------------------------------
par(mfrow=c(1,2))
plot(MWK151, main="Methuselah Walk ring widths", xlab="year")
plot(acf(MWK151))
## ----plot-MWK151,echo=FALSE,results='hide'-------------------------------
pdf("figures/plot-MWK151.pdf",width=9,height=6)
par(mar=c(4,4,2,1))
par(mfrow=c(1,2))
plot(MWK151, main="Methuselah Walk ring widths", xlab="year")
plot(acf(MWK151))
dev.off()
## ------------------------------------------------------------------------
mod <- setModel(drift="-lambda *x", diffusion="sigma", hurst=NA)
mwk <- setYuima(model=mod, data=setData(MWK151))
mwk
mmfrac(mwk)
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter5.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ----carma.brown,echo=TRUE,eval=TRUE-------------------------------------
carma.mod<-setCarma(p=3,q=1,loc.par="c0",Carma.var="y",Latent.var="X")
carma.mod
## ----carma.brown.str,results='hide'--------------------------------------
str(carma.mod)
## ----echo=FALSE----------------------------------------------------------
writeLines(strwrap(capture.output(str(carma.mod)),width=60))
## ----carma.brown.par,echo=TRUE,eval=TRUE---------------------------------
par.carma<-list(a1=4,a2=4.75,a3=1.5,b0=1,b1=0.23,c0=0)
samp<-setSampling(Terminal=100, n=3000)
set.seed(123)
carma <-simulate(carma.mod,
true.parameter=par.carma, sampling=samp)
## ----plot-carma,echo=TRUE,fig.keep='none',results='hide'-----------------
plot(carma)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-carma.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(carma)
dev.off()
## ----eval=FALSE----------------------------------------------------------
## CarmaNoise(yuima, param, data=NULL)
## ----carma.brown.qmle,echo=TRUE,eval=TRUE--------------------------------
fit <- qmle(carma, start=par.carma)
fit
## ----Carm21Comp0, echo=TRUE----------------------------------------------
modCP<-setCarma(p=2,q=1,Carma.var="y",
measure=list(intensity="Lamb",df=list("dnorm(z, mu, sig)")),
measure.type="CP")
true.parmCP <-list(a1=1.39631,a2=0.05029,b0=1,b1=2,
Lamb=1,mu=0,sig=1)
## ----sim_Carm21Comp0, echo=TRUE------------------------------------------
samp.L<-setSampling(Terminal=200,n=4000)
set.seed(123)
simCP<-simulate(modCP,true.parameter=true.parmCP,sampling=samp.L)
## ----plot-simCP,echo=TRUE,fig.keep='none',results='hide'-----------------
plot(simCP,main="CP CARMA(2,1) model")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-simCP.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(simCP,main="CP CARMA(2,1) model")
dev.off()
## ----plot_Carm21Comp0,echo=TRUE,fig.width=14,fig.height=7----------------
carmaoptCP <- qmle(simCP, start=true.parmCP, Est.Incr="Incr")
summary(carmaoptCP)
## ----plot-carmaoptCP,echo=TRUE,fig.keep='none',results='hide'------------
plot(carmaoptCP,ylab="Incr.",type="l",
main="Compound Poisson with normal jump size")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-carmaoptCP.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(carmaoptCP,main="Compound Poisson with normal jump size",ylab="Incr.",type="l")
dev.off()
## ----Carm21vg, echo=TRUE-------------------------------------------------
modVG<-setCarma(p=2,q=1,Carma.var="y",
measure=list("rvgamma(z,lambda,alpha,beta,mu)"),
measure.type="code")
true.parmVG <-list(a1=1.39631, a2=0.05029, b0=1, b1=2,
lambda=1, alpha=1, beta=0, mu=0)
## ----PlotCarm21vg, echo=TRUE,fig.width=14,fig.height=7,fig.keep='none',results='hide'----
set.seed(100)
simVG<-simulate(modVG, true.parameter=true.parmVG,
sampling=samp.L)
plot(simVG,main="VG CARMA(2,1) model")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-simVG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(simVG,main="VG CARMA(2,1) model")
dev.off()
## ----EstPlotCarm21vg,echo=TRUE, fig.keep='none',results='hide'-----------
carmaoptVG <- qmle(simVG, start=true.parmVG, Est.Incr="Incr")
summary(carmaoptVG)
plot(carmaoptVG,xlab="Time",
main="Variance Gamma increments",ylab="Incr.",type="l")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-carmaoptVG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(carmaoptVG,main="Variance Gamma increments",ylab="Incr.",xlab="Time",type="l")
dev.off()
## ----Carm21Comp3, echo=TRUE----------------------------------------------
modNIG<-setCarma(p=2,q=1,Carma.var="y",
measure=list("rNIG(z,alpha,beta,delta1,mu)"),
measure.type="code")
IncMod<-setModel(drift="0",diffusion="0",jump.coeff="1",
measure=list("rNIG(z,1,0,1,0)"),measure.type="code")
set.seed(100)
simLev<-simulate(IncMod,sampling=samp.L)
incrLevy<-diff(as.numeric(get.zoo.data(simLev)[[1]]))
## ----plot-incrLevy,echo=TRUE,fig.keep='none',results='hide'--------------
plot(incrLevy,main="simulated noise increments",type="l")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-incrLevy.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(incrLevy,main="simulated noise increments",type="l")
dev.off()
## ----Carm21sim3, echo=TRUE-----------------------------------------------
true.parmNIG <-list(a1=1.39631,a2=0.05029,b0=1,b1=2,
alpha=1,beta=0,delta1=1,mu=0)
simNIG<-simulate(modNIG,true.parameter=true.parmNIG,sampling=samp.L)
## ----plot-simNIG,echo=TRUE,fig.keep='none',results='hide'----------------
plot(simNIG,main="NIG CARMA(2,1) model")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-simNIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(simNIG,main="NIG CARMA(2,1) model")
dev.off()
## ----plot_Carm21Comp3 ,echo=TRUE,fig.width=14,fig.height=7---------------
carmaoptNIG <- qmle(simNIG, start=true.parmNIG, Est.Incr="Incr")
summary(carmaoptNIG)
## ----plot-carmaoptNIG,echo=TRUE,fig.keep='none',results='hide'-----------
plot(carmaoptNIG,main="Normal Inverse Gaussian",ylab="Incr.",type="l")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-carmaoptNIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(carmaoptNIG,main="Normal Inverse Gaussian",ylab="Incr.",type="l")
dev.off()
## ----Incrtime1Levy3a, echo=TRUE------------------------------------------
NIG.Inc<-as.numeric(coredata([email protected]))
NIG.freq<-frequency([email protected])
## ----Incrtime1Levy3b, echo=TRUE------------------------------------------
t.idx <- seq(from=1, to=length(NIG.Inc), by=NIG.freq)
Unitary.NIG.Inc<-diff(cumsum(NIG.Inc)[t.idx])
## ----Incrtime1Levy3est, echo=TRUE----------------------------------------
library(GeneralizedHyperbolic)
FitInc.NIG.Lev<-nigFit(Unitary.NIG.Inc)
summary(FitInc.NIG.Lev, hessian = TRUE, hessianMethod = "tsHessian")
## ----plot-fitNIG,echo=TRUE,fig.keep='none',results='hide'----------------
par(mfrow = c(1, 2))
plot(FitInc.NIG.Lev, which = 2:3,
plotTitles = paste(c("Histogram of NIG ",
"Log-Histogram of NIG ",
"Q-Q Plot of NIG "), "Incr.", sep = ""))
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-fitNIG.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
par(mfrow = c(1, 2))
plot(FitInc.NIG.Lev, which = 2:3,
plotTitles = paste(c("Histogram of NIG ",
"Log-Histogram of NIG ",
"Q-Q Plot of NIG "), "Incr.",
sep = ""))
dev.off()
## ----message=FALSE,fig.keep='none'---------------------------------------
library(quantmod)
getSymbols("^VIX", to="2016-12-31")
X <- VIX$VIX.Close
VIX.returns <- log(X)
plot(VIX.returns, main="VIX daily log-Returns")
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-VIXret.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(VIX.returns, main="VIX daily log-Returns")
dev.off()
## ----fig.keep='none'-----------------------------------------------------
acf(VIX.returns)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-acfVIX.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(acf(VIX.returns))
dev.off()
## ----message=FALSE,fig.keep='none'---------------------------------------
library(TSA)
eacf(VIX.returns,ar.max = 3, ma.max = 4)
Delta <- 1/252
VIX.Data<-setData(VIX.returns,delta=Delta)
Normal.model<-setCarma(p=2, q=1,loc.par="mu")
Normal.CARMA<-setYuima(data=VIX.Data, model=Normal.model)
Normal.start <- list(a1=36,a2=56,b0=21,b1=1,mu=0)
Normal.est <- qmle(yuima=Normal.CARMA, start=Normal.start,
Est.Incr="Incr")
summary(Normal.est )
## ------------------------------------------------------------------------
inc <[email protected]
shapiro.test(as.numeric(inc))
## ----fig.keep='none'-----------------------------------------------------
plot(acf(as.numeric(inc)))
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-acf2VIX.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(acf(as.numeric(inc)))
dev.off()
## ------------------------------------------------------------------------
Box.test(x=as.numeric(inc), lag = 10, type ="Ljung-Box")
Box.test(x=as.numeric(inc), lag = 10, type ="Box-Pierce")
## ------------------------------------------------------------------------
VG.model <- setCarma(p=2, q=1,loc.par="mu",
measure=list("rvgamma(z,lambda,alpha,beta,mu0)"),
measure.type="code")
NIG.model <- setCarma(p=2, q=1,loc.par="mu",
measure=list(df=list("rNIG(z, alpha, beta, delta1, mu0)")),
measure.type="code")
VG.CARMA<-setYuima(data=VIX.Data, model=VG.model)
NIG.CARMA<-setYuima(data=VIX.Data, model=NIG.model)
VG.start <- list(a1=36,a2=56,b0=21,b1=1,mu=0,
lambda=1,alpha=1,beta=0,mu0=0)
NIG.start <- list(a1=36,a2=56,b0=21,b1=1,mu=0,
alpha=2,beta=1,delta1=1,mu0=0)
fit.VG <- qmle(yuima=VG.CARMA,start=VG.start,
Est.Incr="IncrPar",aggregation=FALSE)
fit.NIG <- qmle(yuima=NIG.CARMA,start=NIG.start,
Est.Incr="IncrPar",aggregation=FALSE)
cf.VG <- coef(fit.VG )
cf.NIG <- coef(fit.NIG )
summary(fit.VG)
summary(fit.NIG)
## ----fig.keep='none'-----------------------------------------------------
d.N <- function(u) log( 1+dnorm(u, mean=mean(inc), sd=sd(inc)) )
d.VG <- function(u) {
log(1+dvgamma(u, lambda=cf.VG["lambda"]*Delta,
alpha=cf.VG["alpha"], beta=cf.VG["beta"], mu=cf.VG["mu0"]*Delta))
}
d.NIG <- function(u) {
log(1+dNIG(u,alpha=cf.NIG["alpha"], beta=cf.NIG["beta"],
delta=cf.NIG["delta1"]*Delta, mu=cf.NIG["mu0"]*Delta))
}
d.Emp <- density(inc)
plot(d.Emp$x, log(1+d.Emp$y),type="l",
main="Rescaled log-densities")
curve(d.N, min(d.Emp$x), max(d.Emp$x), col="blue",add=TRUE, lty=3)
curve(d.VG, min(d.Emp$x), max(d.Emp$x), col="red",add=TRUE,lty=4)
curve(d.NIG, min(d.Emp$x), max(d.Emp$x), col="green",add=TRUE,lty=2)
## ----echo=FALSE,results='hide'-------------------------------------------
pdf("figures/plot-densVIX.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(d.Emp$x, log(1+d.Emp$y),type="l", main="Rescaled log-densities")
curve(d.N, min(d.Emp$x), max(d.Emp$x), col="blue",add=TRUE,lty=3, n=500)
curve(d.VG, min(d.Emp$x), max(d.Emp$x), col="red",add=TRUE,lty=4, n=500)
curve(d.NIG, min(d.Emp$x), max(d.Emp$x), col="green",add=TRUE,lty=2, n=500)
dev.off()
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter6.R |
## ----include=FALSE-------------------------------------------------------
library(knitr)
opts_chunk$set(
tidy=FALSE,
width.cutoff = 60,
strip.white=TRUE,
warning=FALSE
)
## ----include=FALSE-------------------------------------------------------
options(width=55)
options(continue=" ")
require(yuima)
## ----setCogarch,echo=TRUE,eval=TRUE--------------------------------------
# COGARCH(1,1) driven by CP
Cog11 <- setCogarch(p = 1, q=1, measure = list(intensity="1",
df="dnorm(z, 0, 1)"), measure.type = "CP", XinExpr = TRUE)
Cog11
# COGARCH(2,2) driven by CP
Cog22 <- setCogarch(p=2, q=2, measure = list(intensity="1",
df="dnorm(z, 0, 1)"), measure.type = "CP", XinExpr = TRUE)
Cog22
## ----str-cog,echo=TRUE,eval=TRUE-----------------------------------------
class(Cog11)
slotNames(Cog11)
str(Cog11@info,2)
## ------------------------------------------------------------------------
# Param of the COGARCH(1,1)
paramCP11 <- list(a1 = 0.038, b1= 0.053, a0 = 0.04/0.053,
y01 = 50.31)
check11 <- Diagnostic.Cogarch(Cog11, param=paramCP11)
str(check11)
# Param of the COGARCH(2,2)
paramCP22 <- list(a1 = 0.04, a2 = 0.001, b1 = 0.705, b2 = 0.1,
a0 = 0.1, y01=01, y02 = 0)
check22 <- Diagnostic.Cogarch(Cog22, param=paramCP22)
str(check22)
## ----cogarch-euler,echo=TRUE---------------------------------------------
model1 <- setCogarch(p = 1, q = 1,
measure=list("rvgamma(z, 1, sqrt(2), 0, 0)"),
measure.type = "code", Cogarch.var = "G",
V.var = "v", Latent.var="x", XinExpr=TRUE)
## ----cogarch-euler-bad---------------------------------------------------
param1 <- list(a1 = 0.038, b1 = 301, a0 =0.01, x01 = 0)
Diagnostic.Cogarch(model1, param=param1)
Terminal1 <- 5
n1 <- 750
samp1 <- setSampling(Terminal=Terminal1, n=n1)
set.seed(123)
sim1 <- simulate(model1, sampling = samp1, true.parameter = param1,
method="euler")
## ----plot-cogarch,echo=TRUE,fig.keep='none',results='hide'---------------
plot(sim1, main="VG-COGARCH(1,1) model with Euler scheme")
## ----plot-cogarch1,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-cogarch1.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(sim1,
main="VG-COGARCH(1,1) model with Euler scheme")
dev.off()
## ----sim-cogarch2,eval=TRUE,echo=TRUE------------------------------------
set.seed(123)
sim2 <- simulate(model1, sampling = samp1, true.parameter = param1,
method="mixed")
## ----plot-cogach2,echo=TRUE,fig.keep='none',results='hide'---------------
plot(sim2, main="VG-COGARCH(1,1) model with mixed scheme")
## ----plot-cogarch2,echo=FALSE,results='hide'-----------------------------
pdf("figures/plot-cogarch2.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(sim2,
main="VG-COGARCH(1,1) model with mixed scheme")
dev.off()
## ------------------------------------------------------------------------
sampCP <- setSampling(0, 1000, 5000)
simCog11 <- simulate(Cog11, true.par=paramCP11, sampling=sampCP)
simCog22 <- simulate(Cog22, true.par=paramCP22, sampling=sampCP)
## ----plot-cogachs,echo=TRUE,fig.keep='none',results='hide'---------------
plot(simCog11, main="CP-COGARCH(1,1) with Gaussian noise")
plot(simCog22, main="CP-COGARCH(2,2) with Gaussian noise")
## ----plot-cogarchs2,echo=FALSE,results='hide'----------------------------
pdf("figures/plot-cogarchs1.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(simCog11, main="CP-COGARCH(1,1) with Gaussian noise")
dev.off()
pdf("figures/plot-cogarchs2.pdf",width=9,height=4)
par(mar=c(4,4,1,1))
plot(simCog22, main="CP-COGARCH(2,2) with Gaussian noise")
dev.off()
## ----gmm-cogarch,eval=TRUE,echo=TRUE-------------------------------------
set.seed(123)
sampCP <- setSampling(0, 5000, 15000)
simCog11 <- simulate(Cog11, true.par=paramCP11, sampling=sampCP)
fit11 <- gmm(simCog11, start=paramCP11)
summary(fit11)
mat <- rbind(coef(fit11), unlist(paramCP11[names(coef(fit11))]))
rownames(mat) <- c("gmm", "true")
mat
## ----est-cogarch3,eval=TRUE,echo=TRUE------------------------------------
param.VG <- list(a1 = 0.038, b1 = 0.053, a0 = 0.04 / 0.053,
y01 = 50.33)
cog.VG <- setCogarch(p = 1, q = 1, work = FALSE,
measure=list("rvgamma(z, 1, sqrt(2), 0, 0)"),
measure.type = "code", XinExpr = TRUE)
samp.VG <- setSampling(Terminal = 1000, n = 15000)
set.seed(123)
sim.VG <- simulate(cog.VG, true.parameter = param.VG,
sampling = samp.VG, method = "mixed")
fit.gmm <- gmm(sim.VG, start=param.VG)
fit.qmle <- qmle(sim.VG, start=param.VG, grideq=TRUE)
nm <- names(coef(fit.gmm))
mat <- rbind(coef(fit.gmm), coef(fit.qmle)[nm],
unlist(param.VG[nm]))
rownames(mat) <- c("gmm", "qmle", "true")
round(mat,5)
## ----message=FALSE,fig.keep='none'---------------------------------------
require(quantmod)
getSymbols("NXT.L", to="2016-12-31")
S <- NXT.L$NXT.L.Close
X <- na.omit(diff(log(S)))
mX <- mean(X)
X <- X - mX
plot(X, main="Log-returns of NEXT Plc")
require(rugarch)
spec <- ugarchspec(variance.model =
list(model = "sGARCH", garchOrder = c(1, 1)),
mean.model = list(armaOrder = c(0, 0), include.mean = FALSE))
fitGARCH <- ugarchfit(data = X, spec = spec)
GARCH11param <- coef(fitGARCH)
GARCH11param
## ----plot-nextplc,echo=FALSE,results='hide'------------------------------
pdf("figures/plot-nextplc.pdf",width=9,height=4)
par(mar=c(4,4,2,1))
plot(X, main="Log-returns of NEXT Plc")
dev.off()
## ------------------------------------------------------------------------
Delta <- 1/252
ParGarToCog<- function(GARCH11param, dt, names=NULL){
if(is.null(names))
names <- names(GARCH11param)
my.omega <- GARCH11param["omega"]
my.alpha <- GARCH11param["alpha1"]
my.beta <- GARCH11param["beta1"]
a1 <- my.alpha/dt
b1 <- -log(my.beta)/dt
a0 <- my.omega/(b1*dt^2)
qmleparInGARCH <- c(a0, a1, b1)
names(qmleparInGARCH) <- c("a0", "a1", "b1")
return(qmleparInGARCH)
}
## ------------------------------------------------------------------------
ParGarToCog(GARCH11param, Delta)
start <- as.list(ParGarToCog(GARCH11param, Delta))
modCog11 <- setCogarch(p=1, q=1, measure =
list(intensity="1", df=list("dnorm(z, 0, 1)")), measure.type="CP")
NXT.data <- setData(cumsum(X), delta = Delta)
Cog11 <- setYuima(data = NXT.data, model = modCog11)
Cog11.fit <- qmle(yuima = Cog11, grideq=TRUE,
start = c(start, y1 = 0.1),
aggregation = FALSE, method = "Nelder-Mead")
COGARCH11par <- coef(Cog11.fit)
COGARCH11par
## ------------------------------------------------------------------------
ParCogToGar<- function(COGARCH11param, dt, names=NULL){
a0 <- COGARCH11param["a0"]
a1 <- COGARCH11param["a1"]
b1 <- COGARCH11param["b1"]
my.omega <- a0*b1*dt^2
my.alpha <- a1*dt
my.beta <- exp(-b1*dt)
qmleparInGARCH <- c(my.omega, my.alpha, my.beta)
names(qmleparInGARCH) <- c("omega", "alpha1", "beta1")
return(qmleparInGARCH)
}
ParCogToGar(COGARCH11par, Delta)
GARCH11param
| /scratch/gouwar.j/cran-all/cranData/yuima/inst/ybook/chapter7.R |
#' User-friendly Interface for the yuima package
#'
#' Runs yuima Graphical User Interface
#'
#' @param theme GUI theme: "black" or "white".
#'
#' @return Starts yuima GUI
#'
#' @author The YUIMA Project Team
#'
#' @examples
#' \dontrun{
#' yuimaGUI()
#' }
#'
#' @export
#'
yuimaGUI <- function(theme = "black") {
if(!(theme %in% c("black", "white"))) stop ("Theme not supported. Only 'black' or 'white' themes are available.")
print("Please wait while loading...")
options(yuimaGUItheme = theme)
utils::capture.output(
suppressWarnings(
shiny::runApp(system.file("yuimaGUI", package = "yuimaGUI"))
)
)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/R/sourceCodeYuimaGUI.R |
suppressMessages(require(DT))
suppressMessages(require(shinyjs))
suppressMessages(require(yuima))
suppressMessages(require(shiny))
suppressMessages(require(sde))
suppressMessages(require(quantmod))
suppressMessages(require(shinydashboard))
suppressMessages(require(shinyBS))
suppressMessages(require(ggplot2))
suppressMessages(require(plotly))
suppressMessages(require(ghyp))
### comment this for web app version ###
if(!exists("yuimaGUIdata"))
yuimaGUIdata <- reactiveValues(series=list(),
model=list(), multimodel=list(),
usr_model = list(), usr_multimodel = list(),
simulation=list(), multisimulation=list(),
usr_simulation = list(), usr_multisimulation = list(),
cp=list(),
cpYuima=list(),
llag = list(),
cluster = list(),
hedging = list())
### comment this for web app version ###
if(is.null(getOption("yuimaGUItheme"))) options(yuimaGUItheme = "black")
#NIG distribution
dNIG.gui <- function(x, alpha, delta, beta, mu){
g <- NIG.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
dghyp(x = x, object = g)
}
rNIG.gui <- function(n, alpha, delta, beta, mu){
g <- NIG.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
rghyp(n = n, object = g)
}
pNIG.gui <- function(q, alpha, delta, beta, mu){
g <- NIG.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
pghyp(q = q, object = g)
}
#hyp distribution
dhyp.gui <- function(x, alpha, delta, beta, mu){
g <- hyp.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
dghyp(x = x, object = g)
}
rhyp.gui <- function(n, alpha, delta, beta, mu){
g <- hyp.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
rghyp(n = n, object = g)
}
phyp.gui <- function(q, alpha, delta, beta, mu){
g <- hyp.ad(alpha = alpha, delta = delta, beta = beta, mu = mu)
pghyp(q = q, object = g)
}
#VG distribution
dVG.gui <- function(x, lambda, alpha, beta, mu){
g <- VG.ad(lambda = lambda, alpha = alpha, beta = beta, mu = mu)
dghyp(x = x, object = g)
}
rVG.gui <- function(n, lambda, alpha, beta, mu){
g <- VG.ad(lambda = lambda, alpha = alpha, beta = beta, mu = mu)
rghyp(n = n, object = g)
}
pVG.gui <- function(q, lambda, alpha, beta, mu){
g <- VG.ad(lambda = lambda, alpha = alpha, beta = beta, mu = mu)
pghyp(q = q, object = g)
}
#ghyp distribution
dghyp.gui <- function(x, lambda, alpha, delta, beta, mu){
g <- ghyp.ad(lambda = lambda, alpha = alpha, delta = delta, beta = beta, mu = mu)
dghyp(x = x, object = g)
}
rghyp.gui <- function(n, lambda, alpha, delta, beta, mu){
g <- ghyp.ad(lambda = lambda, alpha = alpha, delta = delta, beta = beta, mu = mu)
rghyp(n = n, object = g)
}
pghyp.gui <- function(q, lambda, alpha, delta, beta, mu){
g <- ghyp.ad(lambda = lambda, alpha = alpha, delta = delta, beta = beta, mu = mu)
pghyp(q = q, object = g)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/global.R |
###Display available data
output$changepoint_table_select <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to change point
seriesToChangePoint <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$changepoint_button_select, priority = 1, {
seriesToChangePoint$table <<- rbind(seriesToChangePoint$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$changepoint_table_select_rows_selected]) & !(rownames(yuimaGUItable$series) %in% rownames(seriesToChangePoint$table)),])
})
###SelectAll Button
observeEvent(input$changepoint_button_selectAll, priority = 1, {
seriesToChangePoint$table <<- rbind(seriesToChangePoint$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$changepoint_table_select_rows_all]) & !(rownames(yuimaGUItable$series) %in% rownames(seriesToChangePoint$table)),])
})
###Display Selected Data
output$changepoint_table_selected <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (length(rownames(seriesToChangePoint$table))==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (seriesToChangePoint$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(seriesToChangePoint$table)!=0){
if (length(yuimaGUItable$series)==0)
seriesToChangePoint$table <<- data.frame()
else
seriesToChangePoint$table <<- seriesToChangePoint$table[which(as.character(seriesToChangePoint$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$changepoint_button_delete, priority = 1,{
if (!is.null(input$changepoint_table_selected_rows_selected))
seriesToChangePoint$table <<- seriesToChangePoint$table[-input$changepoint_table_selected_rows_selected,]
})
###DeleteAll Button
observeEvent(input$changepoint_button_deleteAll, priority = 1,{
if (!is.null(input$changepoint_table_selected_rows_all))
seriesToChangePoint$table <<- seriesToChangePoint$table[-input$changepoint_table_selected_rows_all,]
})
observe({
shinyjs::toggle("changepoint_charts", condition = (length(names(yuimaGUIdata$cp))!=0))
})
observe({
shinyjs::toggle("parametric_changepoint_charts", condition = (length(names(yuimaGUIdata$cpYuima))!=0))
})
output$changepoint_symb <- renderUI({
n <- names(yuimaGUIdata$cp)
if(length(n)!=0)
selectInput("changepoint_symb", "Symbol", choices = sort(n), selected = last(n))
})
observeEvent(input$changepoint_button_startEstimation, {
if (length(rownames(seriesToChangePoint$table))!=0)
withProgress(message = 'Analyzing: ', value = 0, {
errors <- c()
for (i in rownames(seriesToChangePoint$table)){
incProgress(1/length(rownames(seriesToChangePoint$table)), detail = i)
errors <- c(errors, addCPoint_distribution(symb = i, method = input$changepoint_method, pvalue = input$changepoint_pvalue)$error)
}
if(!is.null(errors))
createAlert(session = session, anchorId = "nonparametric_changepoint_alert", content = paste("Unable to estimate change points of:", paste(errors, collapse = " ")), dismiss = T, style = "error")
})
})
range_changePoint <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$changePoint_brush) & !is.null(input$changepoint_symb)){
data <- yuimaGUIdata$cp[[input$changepoint_symb]]$series
test <- (length(index(window(data, start = input$changePoint_brush$xmin, end = input$changePoint_brush$xmax))) > 3)
if (test==TRUE){
range_changePoint$x <- c(as.Date(input$changePoint_brush$xmin), as.Date(input$changePoint_brush$xmax))
range_changePoint$y <- c(input$changePoint_brush$ymin, input$changePoint_brush$ymax)
}
}
})
observeEvent(input$changePoint_dbclick,{
range_changePoint$x <- c(NULL, NULL)
})
observeEvent(input$changepoint_symb, {
range_changePoint$x <- c(NULL, NULL)
output$changepoint_plot_series <- renderPlot({
if(!is.null(input$changepoint_symb)) {
cp <- isolate({yuimaGUIdata$cp[[input$changepoint_symb]]})
par(bg="black")
plot(window(cp$series, start = range_changePoint$x[1], end = range_changePoint$x[2]), main=cp$symb, xlab="Index", ylab=NA, log=switch(input$changepoint_scale,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
abline(v=cp$tau, col = "red")
grid(col="grey")
}
})
output$changepoint_plot_incr <- renderPlot({
if(!is.null(input$changepoint_symb)) {
cp <- isolate({yuimaGUIdata$cp[[input$changepoint_symb]]})
if(cp$method=="KSdiff") {
x <- diff(cp$series)
title <- " - Increments"
}
else {
x <- Delt(cp$series)
title <- " - Percentage Increments"
}
x <- x[x[,1]!="Inf"]
par(bg="black")
plot(window(x, start = range_changePoint$x[1], end = range_changePoint$x[2]), main=paste(cp$symb, title), xlab="Index", ylab=NA, log=switch(input$changepoint_scale,"Linear"="","Logarithmic (Y)"="", "Logarithmic (X)"="x", "Logarithmic (XY)"="x"), col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
abline(v=cp$tau, col = "red")
grid(col="grey")
}
})
})
output$text_ChangePointInfo <- renderUI({
if(!is.null(input$changepoint_symb)){
info <- yuimaGUIdata$cp[[input$changepoint_symb]]
div(
h3(input$changepoint_symb, class = "hModal"),
h4(
em(switch(info$method, "KSdiff"="Increments Distriution", "KSperc"="Percentage Increments Distriution")), br(),
class = "hModal"
),
align="center"
)
}
})
output$table_ChangePointInfo <- renderTable(digits = 4, {
table <- data.frame(Time = as.character(yuimaGUIdata$cp[[input$changepoint_symb]]$tau), "p.value" = yuimaGUIdata$cp[[input$changepoint_symb]]$pvalue, check.names = FALSE, row.names = yuimaGUIdata$cp[[input$changepoint_symb]]$tau)
return(table[order(rownames(table), decreasing = TRUE),])
})
observeEvent(input$changepoint_button_delete_estimated, {
yuimaGUIdata$cp[[input$changepoint_symb]] <<- NULL
})
observeEvent(input$changepoint_button_deleteAll_estimated, {
yuimaGUIdata$cp <<- list()
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/eda/changepoint_non_parametric.R |
###Display available data
output$parametric_changepoint_table_select <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to change point
parametric_seriesToChangePoint <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$parametric_changepoint_button_select, priority = 1, {
parametric_seriesToChangePoint$table <<- rbind(parametric_seriesToChangePoint$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$parametric_changepoint_table_select_rows_selected]) & !(rownames(yuimaGUItable$series) %in% rownames(parametric_seriesToChangePoint$table)),])
})
###SelectAll Button
observeEvent(input$parametric_changepoint_button_selectAll, priority = 1, {
parametric_seriesToChangePoint$table <<- rbind(parametric_seriesToChangePoint$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$parametric_changepoint_table_select_rows_all]) & !(rownames(yuimaGUItable$series) %in% rownames(parametric_seriesToChangePoint$table)),])
})
###Display Selected Data
output$parametric_changepoint_table_selected <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (length(rownames(parametric_seriesToChangePoint$table))==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (parametric_seriesToChangePoint$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(parametric_seriesToChangePoint$table)!=0){
if (length(yuimaGUItable$series)==0)
parametric_seriesToChangePoint$table <<- data.frame()
else
parametric_seriesToChangePoint$table <<- parametric_seriesToChangePoint$table[which(as.character(parametric_seriesToChangePoint$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$parametric_changepoint_button_delete, priority = 1,{
if (!is.null(input$parametric_changepoint_table_selected_rows_selected))
parametric_seriesToChangePoint$table <<- parametric_seriesToChangePoint$table[-input$parametric_changepoint_table_selected_rows_selected,]
})
###DeleteAll Button
observeEvent(input$parametric_changepoint_button_deleteAll, priority = 1,{
if (!is.null(input$parametric_changepoint_table_selected_rows_all))
parametric_seriesToChangePoint$table <<- parametric_seriesToChangePoint$table[-input$parametric_changepoint_table_selected_rows_all,]
})
output$parametric_changepoint_model <- renderUI({
choices <- as.vector(defaultModels[names(defaultModels)=="Diffusion process"])
sel <- choices[1]
for(i in names(yuimaGUIdata$usr_model))
if (yuimaGUIdata$usr_model[[i]]$class=="Diffusion process") choices <- c(i, choices)
selectInput("parametric_changepoint_model", label = "Model", choices = choices, multiple = FALSE, selected = sel)
})
###Interactive range of selectRange chart
parametric_range_selectRange <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$parametric_selectRange_brush) & !is.null(input$parametric_plotsRangeSeries)){
data <- getData(input$parametric_plotsRangeSeries)
test <- (length(index(window(data, start = input$parametric_selectRange_brush$xmin, end = input$parametric_selectRange_brush$xmax))) > 3)
if (test==TRUE){
parametric_range_selectRange$x <- c(as.Date(input$parametric_selectRange_brush$xmin), as.Date(input$parametric_selectRange_brush$xmax))
parametric_range_selectRange$y <- c(input$parametric_selectRange_brush$ymin, input$parametric_selectRange_brush$ymax)
}
}
})
observe({
shinyjs::toggle(id="parametric_plotsRangeErrorMessage", condition = nrow(parametric_seriesToChangePoint$table)==0)
shinyjs::toggle(id="parametric_plotsRangeAll", condition = nrow(parametric_seriesToChangePoint$table)!=0)
})
###Display charts: series and its increments
observe({
symb <- input$parametric_plotsRangeSeries
if(!is.null(symb))
if (symb %in% rownames(yuimaGUItable$series)){
data <- getData(symb)
incr <- na.omit(Delt(data, type = "arithmetic"))
condition <- all(is.finite(incr))
shinyjs::toggle("parametric_selectRangeReturns", condition = condition)
parametric_range_selectRange$x <- NULL
parametric_range_selectRange$y <- NULL
start <- as.character(parametric_seriesToChangePoint$table[input$parametric_plotsRangeSeries,"From"])
end <- as.character(parametric_seriesToChangePoint$table[input$parametric_plotsRangeSeries,"To"])
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
output$parametric_selectRange <- renderPlot({
if ((symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(parametric_seriesToChangePoint$table)))){
par(bg="black")
plot.zoo(window(data, start = parametric_range_selectRange$x[1], end = parametric_range_selectRange$x[2]), main=symb, xlab="Index", ylab=NA, log=switch(input$parametric_scale_selectRange,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(data, start = start, end = end), col = "green")
grid(col="grey")
}
})
output$parametric_selectRangeReturns <- renderPlot({
if (symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(parametric_seriesToChangePoint$table)) & condition){
par(bg="black")
plot.zoo( window(incr, start = parametric_range_selectRange$x[1], end = parametric_range_selectRange$x[2]), main=paste(symb, " - Percentage Increments"), xlab="Index", ylab=NA, log=switch(input$parametric_scale_selectRange,"Linear"="","Logarithmic (Y)"="", "Logarithmic (X)"="x", "Logarithmic (XY)"="x"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(incr, start = start, end = end), col = "green")
grid(col="grey")
}
})
}
})
output$parametric_plotsRangeSeries <- renderUI({
selectInput("parametric_plotsRangeSeries", label = "Series", choices = rownames(parametric_seriesToChangePoint$table), selected = input$parametric_plotsRangeSeries)
})
###Choose Range input set to "Select range from charts" if charts have been brushed
output$parametric_chooseRange <- renderUI({
sel <- "full"
if (!is.null(parametric_range_selectRange$x)) sel <- "selected"
selectInput("parametric_chooseRange", label = "Range", choices = c("Full Range" = "full", "Select Range from Charts" = "selected", "Specify Range" = "specify"), selected = sel)
})
output$parametric_chooseRange_specify <- renderUI({
if(!is.null(input$parametric_plotsRangeSeries)) {
data <- getData(input$parametric_plotsRangeSeries)
if(class(index(data))=="numeric")
return(div(
column(6,numericInput("parametric_chooseRange_specify_t0", label = "From", min = start(data), max = end(data), value = start(data))),
column(6,numericInput("parametric_chooseRange_specify_t1", label = "To", min = start(data), max = end(data), value = end(data)))
))
if(class(index(data))=="Date")
return(dateRangeInput("parametric_chooseRange_specify_date", start = start(data), end = end(data), label = "Specify Range"))
}
})
observe({
shinyjs::toggle(id = "parametric_chooseRange_specify", condition = (input$parametric_chooseRange)=="specify")
})
###Function to update data range to use to estimate models
updateRange_parametric_seriesToChangePoint <- function(symb, range = c("full","selected","specify"), type = c("Date", "numeric")){
for (i in symb){
data <- getData(i)
if (range == "full"){
levels(parametric_seriesToChangePoint$table[,"From"]) <- c(levels(parametric_seriesToChangePoint$table[,"From"]), as.character(start(data)))
levels(parametric_seriesToChangePoint$table[,"To"]) <- c(levels(parametric_seriesToChangePoint$table[,"To"]), as.character(end(data)))
parametric_seriesToChangePoint$table[i,"From"] <<- as.character(start(data))
parametric_seriesToChangePoint$table[i,"To"] <<- as.character(end(data))
}
if (range == "selected"){
if(!is.null(parametric_range_selectRange$x) & class(index(data))==type){
start <- parametric_range_selectRange$x[1]
end <- parametric_range_selectRange$x[2]
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(parametric_seriesToChangePoint$table[,"From"]) <- c(levels(parametric_seriesToChangePoint$table[,"From"]), as.character(start))
levels(parametric_seriesToChangePoint$table[,"To"]) <- c(levels(parametric_seriesToChangePoint$table[,"To"]), as.character(end))
parametric_seriesToChangePoint$table[i,"From"] <<- as.character(start)
parametric_seriesToChangePoint$table[i,"To"] <<- as.character(end)
}
}
if (range == "specify"){
if(class(index(data))==type){
if(class(index(data))=="Date"){
start <- input$parametric_chooseRange_specify_date[1]
end <- input$parametric_chooseRange_specify_date[2]
}
if(class(index(data))=="numeric"){
start <- input$parametric_chooseRange_specify_t0
end <- input$parametric_chooseRange_specify_t1
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(parametric_seriesToChangePoint$table[,"From"]) <- c(levels(parametric_seriesToChangePoint$table[,"From"]), as.character(start))
levels(parametric_seriesToChangePoint$table[,"To"]) <- c(levels(parametric_seriesToChangePoint$table[,"To"]), as.character(end))
parametric_seriesToChangePoint$table[i,"From"] <<- as.character(start)
parametric_seriesToChangePoint$table[i,"To"] <<- as.character(end)
}
}
}
}
###Apply selected range by double click
observeEvent(input$parametric_selectRange_dbclick, priority = 1, {
updateRange_parametric_seriesToChangePoint(input$parametric_plotsRangeSeries, range = "selected", type = class(index(getData(input$parametric_plotsRangeSeries))))
})
###Apply selected range
observeEvent(input$parametric_buttonApplyRange, priority = 1, {
updateRange_parametric_seriesToChangePoint(input$parametric_plotsRangeSeries, range = input$parametric_chooseRange, type = class(index(getData(input$parametric_plotsRangeSeries))))
})
###ApplyAll selected range
observeEvent(input$parametric_buttonApplyAllRange, priority = 1, {
updateRange_parametric_seriesToChangePoint(rownames(parametric_seriesToChangePoint$table), range = input$parametric_chooseRange, type = class(index(getData(input$parametric_plotsRangeSeries))))
})
### Estimation Settings
parametric_modal_prev_buttonDelta <- 0
parametric_modal_prev_buttonAllDelta <- 0
observe({
for (symb in rownames(parametric_seriesToChangePoint$table)){
if (is.null(yuimaGUIsettings$delta[[symb]])) yuimaGUIsettings$delta[[symb]] <<- 0.01
if (is.null(yuimaGUIsettings$toLog[[symb]])) yuimaGUIsettings$toLog[[symb]] <<- FALSE
data <- getData(symb)
if (yuimaGUIsettings$toLog[[symb]]==TRUE) data <- log(data)
for (modName in input$parametric_changepoint_model){
if (class(try(setModelByName(modName, jumps = NA, AR_C = NA, MA_C = NA)))!="try-error"){
if (is.null(yuimaGUIsettings$estimation[[modName]]))
yuimaGUIsettings$estimation[[modName]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]]))
yuimaGUIsettings$estimation[[modName]][[symb]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["fixed"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["fixed"]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["start"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["start"]] <<- list()
startMinMax <- defaultBounds(name = modName,
jumps = NA,
AR_C = NA,
MA_C = NA,
strict = FALSE,
data = data,
delta = yuimaGUIsettings$delta[[symb]])
upperLower <- defaultBounds(name = modName,
jumps = NA,
AR_C = NA,
MA_C = NA,
strict = TRUE,
data = data,
delta = yuimaGUIsettings$delta[[symb]])
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["startMin"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["startMin"]] <<- startMinMax$lower
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["startMax"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["startMax"]] <<- startMinMax$upper
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["upper"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["upper"]] <<- upperLower$upper
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["lower"]]) | parametric_modal_prev_buttonDelta!=input$parametric_modal_button_applyDelta | parametric_modal_prev_buttonAllDelta!=input$parametric_modal_button_applyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["lower"]] <<- upperLower$lower
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["method"]])){
yuimaGUIsettings$estimation[[modName]][[symb]][["method"]] <<- "L-BFGS-B"
}
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["trials"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["trials"]] <<- 1
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["seed"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["seed"]] <<- NA
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["joint"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["joint"]] <<- FALSE
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["aggregation"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["aggregation"]] <<- TRUE
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]] <<- NA
}
}
}
parametric_modal_prev_buttonDelta <<- input$advancedSettingsButtonApplyDelta
parametric_modal_prev_buttonAllDelta <<- input$advancedSettingsButtonApplyAllDelta
})
observe({
shinyjs::toggle(id="parametric_modal_body", condition = nrow(parametric_seriesToChangePoint$table)!=0)
shinyjs::toggle(id="parametric_modal_errorMessage", condition = nrow(parametric_seriesToChangePoint$table)==0)
})
output$parametric_modal_series <- renderUI({
if (nrow(parametric_seriesToChangePoint$table)!=0)
selectInput(inputId = "parametric_modal_series", label = "Series", choices = rownames(parametric_seriesToChangePoint$table))
})
output$parametric_modal_delta <- renderUI({
if (!is.null(input$parametric_modal_series))
return (numericInput("parametric_modal_delta", label = paste("delta", input$parametric_modal_series), value = yuimaGUIsettings$delta[[input$parametric_modal_series]]))
})
output$parametric_modal_toLog <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series)){
choices <- FALSE
if (all(getData(input$parametric_modal_series)>0)) choices <- c(FALSE, TRUE)
return (selectInput("parametric_modal_toLog", label = "Convert to log", choices = choices, selected = yuimaGUIsettings$toLog[[input$parametric_modal_series]]))
}
})
output$parametric_modal_model <- renderUI({
if(!is.null(input$parametric_changepoint_model))
selectInput(inputId = "parametric_modal_model", label = "Model", choices = input$parametric_changepoint_model)
})
output$parametric_modal_parameter <- renderUI({
if (!is.null(input$parametric_modal_model)){
mod <- setModelByName(input$parametric_modal_model, jumps = NA, AR_C = NA, MA_C = NA)
par <- getAllParams(mod, 'Diffusion process')
selectInput(inputId = "parametric_modal_parameter", label = "Parameter", choices = par)
}
})
output$parametric_modal_start <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_parameter))
numericInput(inputId = "parametric_modal_start", label = "start", value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["start"]][[input$parametric_modal_parameter]])
})
output$parametric_modal_startMin <- renderUI({
input$parametric_modal_button_applyDelta
input$parametric_modal_button_applyAllDelta
if (!is.null(input$parametric_modal_start) & !is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_parameter))
if (is.na(input$parametric_modal_start))
numericInput(inputId = "parametric_modal_startMin", label = "start: Min", value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["startMin"]][[input$parametric_modal_parameter]])
})
output$parametric_modal_startMax <- renderUI({
input$parametric_modal_button_applyDelta
input$parametric_modal_button_applyAllDelta
if (!is.null(input$parametric_modal_start) & !is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_parameter))
if (is.na(input$parametric_modal_start))
numericInput(inputId = "parametric_modal_startMax", label = "start: Max", value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["startMax"]][[input$parametric_modal_parameter]])
})
output$parametric_modal_lower <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_parameter))
if (input$parametric_modal_method=="L-BFGS-B" | input$parametric_modal_method=="Brent")
numericInput("parametric_modal_lower", label = "lower", value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["lower"]][[input$parametric_modal_parameter]])
})
output$parametric_modal_upper <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_parameter))
if (input$parametric_modal_method=="L-BFGS-B" | input$parametric_modal_method=="Brent")
numericInput("parametric_modal_upper", label = "upper", value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["upper"]][[input$parametric_modal_parameter]])
})
output$parametric_modal_method <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series))
selectInput("parametric_modal_method", label = "method", choices = c("L-BFGS-B", "Nelder-Mead", "BFGS", "CG", "SANN", "Brent"), selected = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["method"]])
})
output$parametric_modal_trials <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series) & !is.null(input$parametric_modal_method))
numericInput("parametric_modal_trials", label = "trials", min = 1, value = ifelse(input$parametric_modal_method=="SANN" & yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["method"]]!="SANN",1,yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["trials"]]))
})
output$parametric_modal_seed <- renderUI({
if (!is.null(input$parametric_modal_model) & !is.null(input$parametric_modal_series))
numericInput("parametric_modal_seed", label = "seed", min = 1, value = yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["seed"]])
})
observeEvent(input$parametric_modal_button_applyDelta, {
yuimaGUIsettings$delta[[input$parametric_modal_series]] <<- input$parametric_modal_delta
yuimaGUIsettings$toLog[[input$parametric_modal_series]] <<- input$parametric_modal_toLog
})
observeEvent(input$parametric_modal_button_applyAllDelta, {
for (symb in rownames(parametric_seriesToChangePoint$table)){
yuimaGUIsettings$delta[[symb]] <<- input$parametric_modal_delta
if (input$parametric_modal_toLog==FALSE) yuimaGUIsettings$toLog[[symb]] <<- input$parametric_modal_toLog
else if (all(getData(symb)>0)) yuimaGUIsettings$toLog[[symb]] <<- input$parametric_modal_toLog
}
})
observeEvent(input$parametric_modal_button_applyModel,{
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["start"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_start
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["startMin"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_startMin
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["startMax"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_startMax
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["lower"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_lower
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["upper"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_upper
})
observeEvent(input$parametric_modal_button_applyAllModel,{
for (symb in rownames(parametric_seriesToChangePoint$table)){
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["start"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_start
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["startMin"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_startMin
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["startMax"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_startMax
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["lower"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_lower
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["upper"]][[input$parametric_modal_parameter]] <<- input$parametric_modal_upper
}
})
observeEvent(input$parametric_modal_button_applyGeneral,{
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["method"]] <<- input$parametric_modal_method
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["trials"]] <<- input$parametric_modal_trials
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[input$parametric_modal_series]][["seed"]] <<- input$parametric_modal_seed
})
observeEvent(input$parametric_modal_button_applyAllModelGeneral,{
for (symb in rownames(parametric_seriesToChangePoint$table)){
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["method"]] <<- input$parametric_modal_method
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["trials"]] <<- input$parametric_modal_trials
yuimaGUIsettings$estimation[[input$parametric_modal_model]][[symb]][["seed"]] <<- input$parametric_modal_seed
}
})
output$parametric_changepoint_symb <- renderUI({
n <- names(yuimaGUIdata$cpYuima)
if(length(n)!=0)
selectInput("parametric_changepoint_symb", "Symbol", choices = sort(n), selected = last(n))
})
### Start Estimation
observeEvent(input$parametric_changepoint_button_startEstimation, {
if (length(rownames(parametric_seriesToChangePoint$table))!=0)
closeAlert(session = session, alertId = "parametric_changepoint_alert_err")
withProgress(message = 'Analyzing: ', value = 0, {
errors <- c()
for (symb in rownames(parametric_seriesToChangePoint$table)){
incProgress(1/length(rownames(parametric_seriesToChangePoint$table)), detail = symb)
test <- try(addCPoint(modelName = input$parametric_changepoint_model,
symb = symb,
fracL = input$parametric_modal_rangeFraction[1]/100,
fracR = input$parametric_modal_rangeFraction[2]/100,
from = as.character(parametric_seriesToChangePoint$table[symb, "From"]),
to = as.character(parametric_seriesToChangePoint$table[symb, "To"]),
delta = yuimaGUIsettings$delta[[symb]],
toLog = yuimaGUIsettings$toLog[[symb]],
start = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["start"]],
startMin = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["startMin"]],
startMax = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["startMax"]],
method = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["method"]],
trials = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["trials"]],
seed = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["seed"]],
lower = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["lower"]],
upper = yuimaGUIsettings$estimation[[input$parametric_changepoint_model]][[symb]][["upper"]]))
if(class(test)=="try-error")
errors <- c(errors, symb)
}
if (!is.null(errors))
createAlert(session = session, anchorId = "parametric_changepoint_alert", alertId = "parametric_changepoint_alert_err", style = "error", dismiss = TRUE, content = paste("Unable to estimate Change Point of:", paste(errors, collapse = " ")))
})
})
parametric_range_changePoint <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$parametric_changePoint_brush) & !is.null(input$parametric_changepoint_symb)){
data <- yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]$series
test <- (length(index(window(data, start = input$parametric_changePoint_brush$xmin, end = input$parametric_changePoint_brush$xmax))) > 3)
if (test==TRUE){
parametric_range_changePoint$x <- c(as.Date(input$parametric_changePoint_brush$xmin), as.Date(input$parametric_changePoint_brush$xmax))
parametric_range_changePoint$y <- c(input$parametric_changePoint_brush$ymin, input$parametric_changePoint_brush$ymax)
}
}
})
observeEvent(input$parametric_changePoint_dbclick,{
parametric_range_changePoint$x <- c(NULL, NULL)
})
observeEvent(input$parametric_changepoint_symb, {
parametric_range_changePoint$x <- c(NULL, NULL)
output$parametric_changepoint_plot_series <- renderPlot({
if(!is.null(input$parametric_changepoint_symb)) {
cp <- isolate({yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]})
data <- cp$series
toLog <- cp$info$toLog
symb <- cp$info$symb
par(bg="black")
plot(window(data, start = parametric_range_changePoint$x[1], end = parametric_range_changePoint$x[2]), main=ifelse(toLog==TRUE, paste("log(",symb,")", sep = ""), symb), xlab="Index", ylab=NA, log=switch(input$parametric_changepoint_scale,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
abline(v=cp$tau, col = "red")
grid(col="grey")
}
})
})
output$parametric_changepoint_info <- renderUI({
if(!is.null(input$parametric_changepoint_symb)){
info <- isolate({yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]$info})
div(
h3(info$model),
withMathJax(printModelLatex(names = info$model, process = "Diffusion process")), br(),
h4(
em(paste("Change Point:", as.character(isolate({yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]$tau}))))
),
align="center"
)
}
})
output$parametric_changepoint_modal_info_text <- renderUI({
info <- yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]$info
div(
h3(input$parametric_changepoint_symb, " - " , info$model, class = "hModal"),
h4(
em("series to log:"), info$toLog, br(),
em("method:"), info$method, br(),
em("trials:"), info$trials, br(),
em("seed:"), info$seed, br(), class = "hModal"
),
align="center")
})
output$parametric_changepoint_modal_info_tableL <- renderTable(rownames = T, {
cp <- yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]
tL <- summary(cp$qmleL)@coef
for (i in 1:nrow(tL)){
tL[i,"Estimate"] <- signifDigits(value = tL[i,"Estimate"], sd = tL[i,"Std. Error"])
tL[i,"Std. Error"] <- signifDigits(value = tL[i,"Std. Error"], sd = tL[i,"Std. Error"])
}
return(tL)
})
output$parametric_changepoint_modal_info_tableR <- renderTable(rownames = T, {
cp <- yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]]
tR <- summary(cp$qmleR)@coef
for (i in 1:nrow(tR)){
tR[i,"Estimate"] <- signifDigits(value = tR[i,"Estimate"], sd = tR[i,"Std. Error"])
tR[i,"Std. Error"] <- signifDigits(value = tR[i,"Std. Error"], sd = tR[i,"Std. Error"])
}
return(tR)
})
observeEvent(input$parametric_changepoint_button_delete_estimated, {
yuimaGUIdata$cpYuima[[input$parametric_changepoint_symb]] <<- NULL
})
observeEvent(input$parametric_changepoint_button_deleteAll_estimated, {
yuimaGUIdata$cpYuima <<- list()
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/eda/changepoint_parametric.R |
###Display available data
output$cluster_table_select <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to cluster
seriesToCluster <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$cluster_button_select, priority = 1, {
if (length(input$cluster_table_select_rows_selected)!=0){
closeAlert(session, "cluster_alert_indexes")
if (nrow(seriesToCluster$table)==0)
seriesToCluster$table <<- rbind(seriesToCluster$table, yuimaGUItable$series[rownames(yuimaGUItable$series)[input$cluster_table_select_rows_selected[1]],])
for (symb in rownames(yuimaGUItable$series)[input$cluster_table_select_rows_selected]){
if (class(index(yuimaGUIdata$series[[symb]]))==class(index(yuimaGUIdata$series[[rownames(seriesToCluster$table)[1]]]))){
if (!(symb %in% rownames(seriesToCluster$table)))
seriesToCluster$table <<- rbind(seriesToCluster$table, yuimaGUItable$series[symb,])
} else {
createAlert(session, anchorId = "cluster_alert", alertId = "cluster_alert_indexes", append = FALSE, content = "Cannot cluster series with different type of index (numeric/date)", style = "warning")
}
}
}
})
###SelectAll Button
observeEvent(input$cluster_button_selectAll, priority = 1, {
if (length(input$cluster_table_select_rows_all)!=0){
closeAlert(session, "cluster_alert_indexes")
if (nrow(seriesToCluster$table)==0)
seriesToCluster$table <<- rbind(seriesToCluster$table, yuimaGUItable$series[rownames(yuimaGUItable$series)[input$cluster_table_select_rows_all[1]],])
for (symb in rownames(yuimaGUItable$series)[input$cluster_table_select_rows_all]){
if (class(index(yuimaGUIdata$series[[symb]]))==class(index(yuimaGUIdata$series[[rownames(seriesToCluster$table)[1]]]))){
if (!(symb %in% rownames(seriesToCluster$table)))
seriesToCluster$table <<- rbind(seriesToCluster$table, yuimaGUItable$series[symb,])
} else {
createAlert(session, anchorId = "cluster_alert", alertId = "cluster_alert_indexes", append = FALSE, content = "Cannot cluster series with different type of index (numeric/date)", style = "warning")
}
}
}
})
###Display Selected Data
output$cluster_table_selected <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (length(rownames(seriesToCluster$table))==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (seriesToCluster$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(seriesToCluster$table)!=0){
if (length(yuimaGUItable$series)==0)
seriesToCluster$table <<- data.frame()
else
seriesToCluster$table <<- seriesToCluster$table[which(as.character(seriesToCluster$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$cluster_button_delete, priority = 1,{
if (!is.null(input$cluster_table_selected_rows_selected))
seriesToCluster$table <<- seriesToCluster$table[-input$cluster_table_selected_rows_selected,]
})
###DeleteAll Button
observeEvent(input$cluster_button_deleteAll, priority = 1,{
if (!is.null(input$cluster_table_selected_rows_all))
seriesToCluster$table <<- seriesToCluster$table[-input$cluster_table_selected_rows_all,]
})
observe({
shinyjs::toggle("cluster_distance_minkowskiPower", condition = (input$cluster_distance=="minkowski"))
})
observeEvent(input$cluster_button_startCluster, {
closeAlert(session, "cluster_alert_dist")
if (length(rownames(seriesToCluster$table))<=2)
createAlert(session, anchorId = "cluster_alert", alertId = "cluster_alert_dist", content = "Select at least 3 series", style = "error")
if (length(rownames(seriesToCluster$table))>2){ withProgress(value = 1, message = "Calculating...", {
names_list <- rownames(seriesToCluster$table)
x <- yuimaGUIdata$series[[names_list[1]]]
for(i in names_list[-1])
x <- merge(x, yuimaGUIdata$series[[i]])
colnames(x) <- names_list
d <- switch(
input$cluster_distance,
"MOdist" = try(sde::MOdist(na.omit(x))),
"MYdist_perc" = try(MYdist(x, percentage = TRUE)),
"MYdist_ass" = try(MYdist(x, percentage = FALSE)),
"euclidean" = try(dist(t(as.data.frame(x)), method = "euclidean")),
"maximum" = try(dist(t(as.data.frame(x)), method = "maximum")),
"manhattan" = try(dist(t(as.data.frame(x)), method = "manhattan")),
"canberra" = try(dist(t(as.data.frame(x)), method = "canberra")),
"minkowski" = try(dist(t(as.data.frame(x)), method = "minkowski", p = input$cluster_distance_minkowskiPower))
)
if (class(d)=="try-error")
createAlert(session, anchorId = "cluster_alert", alertId = "cluster_alert_dist", content = "Error in clustering", style = "error")
else{
hc <- hclust(d, method = input$cluster_linkage)
i <- 1
id <- "Clustering"
repeat {
if(id %in% names(yuimaGUIdata$cluster)){
id <- paste("Clustering", i)
i <- i+1
} else break
}
yuimaGUIdata$cluster[[id]] <<- list(d = d, linkage = input$cluster_linkage, distance = input$cluster_distance, power = input$cluster_distance_minkowskiPower)
}
})}
})
output$cluster_analysis_id <- renderUI({
n <- names(yuimaGUIdata$cluster)
if(length(n)!=0)
selectInput("cluster_analysis_id", label = "Clustering ID", choices = sort(n), selected = last(n))
})
observeEvent(input$cluster_analysis_id, {
if(!is.null(input$cluster_analysis_id)) if (input$cluster_analysis_id %in% names(yuimaGUIdata$cluster)){
d <- yuimaGUIdata$cluster[[input$cluster_analysis_id]]$d
hc <- hclust(d, method = yuimaGUIdata$cluster[[input$cluster_analysis_id]]$linkage)
labelColors <- c("#CDB380", "#FF0000", "#036564", "#FF00FF", "#EB6841", "#7FFFD4", "#EDC951","#FF8000", "#FFE4E1", "#A2CD5A", "#71C671", "#AAAAAA", "#555555", "#FFA07A", "#8B6508", "#FFC125", "#FFFACD", "#808000", "#458B00", "#54FF9F", "#43CD80", "#008B8B", "#53868B", "#B0E2FF", "#0000FF", "#F8F8FF", "#551A8B", "#AB82FF", "#BF3EFF", "#FF83FA", "#8B1C62", "#CD6839", "#8E8E38", "#1E1E1E")
dendrClick <- reactiveValues(y = NULL)
output$cluster_dendogram <- renderPlot({
if(!is.null(input$cluster_dendrogram_click$y))
dendrClick$y <- input$cluster_dendrogram_click$y
if(!is.null(dendrClick$y)){
clusMember = cutree(hc, h = dendrClick$y)
colLab <- function(n) {
if (is.leaf(n)) {
a <- attributes(n)
labCol <- labelColors[clusMember[which(names(clusMember) == a$label)]]
attr(n, "nodePar") <- c(a$nodePar, lab.col = labCol)
}
n
}
hc <- dendrapply(as.dendrogram(hc), colLab)
}
if(is.null(dendrClick$y)){
colDefault <- function(n){
if (is.leaf(n))
attr(n, "nodePar") <- c(attributes(n)$nodePar, lab.col = labelColors[1])
return(n)
}
hc <- dendrapply(as.dendrogram(hc), colDefault)
}
output$cluster_button_saveDendogram <- downloadHandler(
filename = "Dendrogram.png",
content = function(file) {
png(file, width = 960)
par(bg="black", xaxt = "n", mar= c(10, 4, 4, 2)+0.1)
plot(hc, ylab = "", xlab = "", main = "Dendrogram", edgePar=list(col="grey50"), col.main = "#FFF68F", col.axis="grey")
dev.off()
}
)
par(bg="black", xaxt = "n", mar= c(10, 4, 4, 2)+0.1)
plot(hc, ylab = "", xlab = "", main = "Dendrogram", edgePar=list(col="grey50"), col.main = "#FFF68F", col.axis="grey")
})
output$cluster_scaling2D <- renderPlot({
points <- cmdscale(d)
if(!is.null(dendrClick$y))
g1 <- cutree(hclust(d), h = dendrClick$y)
else
g1 <- 1
output$cluster_button_saveScaling2D <- downloadHandler(
filename = "Multidimensional scaling.png",
content = function(file) {
png(file)
par(bg="black", xaxt = "n", yaxt = "n", bty="n")
plot(points, col=labelColors[g1], pch=16, cex=2, main = "Multidimensional scaling", col.main = "#FFF68F", xlab="", ylab="")
dev.off()
}
)
par(bg="black", xaxt = "n", yaxt = "n", bty="n")
plot(points, col=labelColors[g1], pch=16, cex=2, main = "Multidimensional scaling", col.main = "#FFF68F", xlab="", ylab="")
})
}
})
output$cluster_moreInfo <- renderUI({
if(!is.null(input$cluster_analysis_id)) if (input$cluster_analysis_id %in% names(isolate({yuimaGUIdata$cluster}))){
info <- isolate({yuimaGUIdata$cluster[[input$cluster_analysis_id]]})
dist <- switch(info$distance,
"MOdist"="Markov Operator",
"MYdist_perc"="Percentage Increments Distribution",
"MYdist_ass"="Increments Distribution",
"euclidean"="Euclidean",
"maximum"="Maximum",
"manhattan"="Manhattan",
"canberra"="Canberra",
"minkowski"="Minkowski")
linkage <- switch(info$linkage,
"complete"="Complete",
"single"="Single",
"average"="Average",
"ward.D"="Ward",
"ward.D2"="Ward squared",
"mcquitty"="McQuitty",
"Median"="median",
"centroid"="Centroid")
if (dist=="Minkowski") dist <- paste(dist, " (", info$power,")", sep = "")
return(HTML(paste("<div><h4>    Linkage:",linkage, "         Distance:", dist, "</h4></div>")))
}
})
observeEvent(input$cluster_button_delete_analysis, {
yuimaGUIdata$cluster[[input$cluster_analysis_id]] <<- NULL
})
observeEvent(input$cluster_button_deleteAll_analysis, {
yuimaGUIdata$cluster <<- list()
})
observe({
shinyjs::toggle("cluster_charts", condition = length(names(yuimaGUIdata$cluster))!=0)
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/eda/clustering.R |
###Display available data
output$llag_table_select <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to change point
seriesToLeadLag <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$llag_button_select, priority = 1, {
if (length(input$llag_table_select_rows_selected)!=0){
closeAlert(session, "llag_alert_select")
if (nrow(seriesToLeadLag$table)==0)
seriesToLeadLag$table <<- rbind(seriesToLeadLag$table, yuimaGUItable$series[rownames(yuimaGUItable$series)[input$llag_table_select_rows_selected[1]],])
for (symb in rownames(yuimaGUItable$series)[input$llag_table_select_rows_selected]){
if (class(index(yuimaGUIdata$series[[symb]]))==class(index(yuimaGUIdata$series[[rownames(seriesToLeadLag$table)[1]]]))){
if (!(symb %in% rownames(seriesToLeadLag$table)))
seriesToLeadLag$table <<- rbind(seriesToLeadLag$table, yuimaGUItable$series[symb,])
} else {
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", append = FALSE, content = "Cannot analyze Lead-Lag for series with different type of index (numeric/date)", style = "warning")
}
}
}
})
###SelectAll Button
observeEvent(input$llag_button_selectAll, priority = 1, {
if (length(input$llag_table_select_rows_all)!=0){
closeAlert(session, "llag_alert_select")
if (nrow(seriesToLeadLag$table)==0)
seriesToLeadLag$table <<- rbind(seriesToLeadLag$table, yuimaGUItable$series[rownames(yuimaGUItable$series)[input$llag_table_select_rows_all[1]],])
for (symb in rownames(yuimaGUItable$series)[input$llag_table_select_rows_all]){
if (class(index(yuimaGUIdata$series[[symb]]))==class(index(yuimaGUIdata$series[[rownames(seriesToLeadLag$table)[1]]]))){
if (!(symb %in% rownames(seriesToLeadLag$table)))
seriesToLeadLag$table <<- rbind(seriesToLeadLag$table, yuimaGUItable$series[symb,])
} else {
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", append = FALSE, content = "Cannot analyze Lead-Lag for series with different type of index (numeric/date)", style = "warning")
}
}
}
})
###Display Selected Data
output$llag_table_selected <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (length(rownames(seriesToLeadLag$table))==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (seriesToLeadLag$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(seriesToLeadLag$table)!=0){
if (length(yuimaGUItable$series)==0)
seriesToLeadLag$table <<- data.frame()
else
seriesToLeadLag$table <<- seriesToLeadLag$table[which(as.character(seriesToLeadLag$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$llag_button_delete, priority = 1,{
if (!is.null(input$llag_table_selected_rows_selected))
seriesToLeadLag$table <<- seriesToLeadLag$table[-input$llag_table_selected_rows_selected,]
})
###DeleteAll Button
observeEvent(input$llag_button_deleteAll, priority = 1,{
if (!is.null(input$llag_table_selected_rows_all))
seriesToLeadLag$table <<- seriesToLeadLag$table[-input$llag_table_selected_rows_all,]
})
observe({
if (length(rownames(seriesToLeadLag$table))!=0){
type <- try(class(index(yuimaGUIdata$series[[rownames(seriesToLeadLag$table)[1]]])[1]))
if(type!="try-error"){
shinyjs::toggle(id = "llag_range_date", condition = type=="Date")
shinyjs::toggle(id = "llag_range_numeric", condition = type!="Date")
}
}
else {
shinyjs::show(id = "llag_range_date")
shinyjs::hide(id = "llag_range_numeric")
}
})
observe({
shinyjs::toggle("llag_maxLag", condition = input$llag_type=="llag")
shinyjs::toggle("llag_corr_method", condition = input$llag_type=="corr")
})
observeEvent(input$llag_button_startEstimation, {
closeAlert(session, alertId = "llag_alert_select")
if (is.na(input$llag_maxLag) | input$llag_maxLag <= 0)
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", content = "Lag max must be greater than zero", style = "warning")
else {
series <- rownames(seriesToLeadLag$table)
if (length(series)<=1)
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", content = "Select at least two series", style = "warning")
else {
withProgress(message = "Calculating...", value = 1, {
data <- yuimaGUIdata$series[[series[1]]]
type <- class(index(data)[1])
for (i in 2:length(series))
data <- merge(data, yuimaGUIdata$series[[series[i]]])
colnames(data) <- series
if(type=="Date") {
start <- input$llag_range_date[1]
end <- input$llag_range_date[2]
} else {
start <- input$llag_range_numeric1
end <- input$llag_range_numeric2
}
data <- window(data, start = start, end = end)
if(is.regular(data)){
delta <- mode(na.omit(diff(index(data))))
yuimaData <- setDataGUI(data, delta = delta)
if(input$llag_type=="llag"){
res <- try(llag(yuimaData, ci=TRUE, plot=FALSE, grid = seq(from = -input$llag_maxLag, to = input$llag_maxLag, by = delta)))
if (class(res)=="try-error")
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", content = "Error in computing lead-lag", style = "error")
else {
i <- 1
id <- "Lead-Lag Analysis"
repeat {
if(id %in% names(yuimaGUIdata$llag)){
id <- paste("Lead-Lag Analysis", i)
i <- i+1
} else break
}
yuimaGUIdata$llag[[id]] <<- list(type = "llag", maxLag = input$llag_maxLag, delta = delta, llag = res$lagcce, cor = res$cormat, p.values = res$p.values, start = start, end = end)
}
}
if(input$llag_type=="corr"){
if(input$llag_corr_method %in% c("pearson", "kendall", "spearman")){
x <- as.matrix([email protected])
res <- try(cor(x, method = input$llag_corr_method, use = "pairwise.complete.obs"))
}
else
res <- try(cce(x = yuimaData, method = input$llag_corr_method)$cormat)
if (class(res)=="try-error")
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", content = "Error in computing the correlation matrix", style = "error")
else {
i <- 1
id <- "Correlation Analysis"
repeat {
if(id %in% names(yuimaGUIdata$llag)){
id <- paste("Correlation Analysis", i)
i <- i+1
} else break
}
yuimaGUIdata$llag[[id]] <<- list(type = "corr", cormat = res, method = input$llag_corr_method, start = start, end = end)
}
}
} else{
createAlert(session, anchorId = "llag_alert", alertId = "llag_alert_select", content = "Cannot analyze non-regular grid of observations", style = "error")
}
})
}
}
})
observe({
shinyjs::toggle("llag_plot_body", condition = length(names(yuimaGUIdata$llag))!=0)
})
output$llag_analysis_id <- renderUI({
n <- names(yuimaGUIdata$llag)
if(length(n)!=0)
selectInput("llag_analysis_id", label = "Analysis ID", choices = sort(n), selected = last(n))
})
output$llag_plot_corr_method <- renderUI({
if(!is.null(input$llag_analysis_id)) if (input$llag_analysis_id %in% names(isolate({yuimaGUIdata$llag}))){
info <- isolate({yuimaGUIdata$llag})[[input$llag_analysis_id]]
if (info$type=="corr"){
method <- switch(info$method,
"HY"="Hayashi-Yoshida",
"PHY"="Pre-averaged Hayashi-Yoshida",
"MRC"="Modulated Realized Covariance",
"TSCV"="Two Scales realized CoVariance",
"GME"="Generalized Multiscale Estimator",
"RK"="Realized Kernel",
"QMLE"="Quasi Maximum Likelihood Estimator",
"SIML"="Separating Information Maximum Likelihood",
"THY"="Truncated Hayashi-Yoshida",
"PTHY"="Pre-averaged Truncated Hayashi-Yoshida",
"SRC"="Subsampled Realized Covariance",
"SBPC"="Subsampled realized BiPower Covariation")
return(HTML(paste("<div><h4>    Method:", method, "</h4></div>")))
}
}
})
observe({
if(!is.null(input$llag_analysis_id)) if (input$llag_analysis_id %in% isolate({names(yuimaGUIdata$llag)})) {
type <- isolate({yuimaGUIdata$llag})[[input$llag_analysis_id]]$type
shinyjs::toggle("llag_plot_confidence", condition = type=="llag")
shinyjs::toggle("llag_plot_corr_method", condition = type=="corr")
shinyjs::toggle("llag_plot_howToRead", condition = type=="llag")
}
})
output$llag_plot <- renderPlot({
if(!is.null(input$llag_analysis_id) & !is.null(input$llag_plot_confidence)) if (input$llag_analysis_id %in% isolate({names(yuimaGUIdata$llag)})) {
info <- isolate({yuimaGUIdata$llag[[input$llag_analysis_id]]})
if(info$type=="llag"){
co <- ifelse(info$p.values > input$llag_plot_confidence | is.na(info$p.values), 0, info$llag)
co <- melt(t(co))
co$label <- paste(
round(co$value, 1+as.integer(abs(log10(info$delta)))),
'\n',
'(',
apply(co, MARGIN = 1, function(x) {round(info$cor[x[1], x[2]], 2)}),
')',
sep = '')
}
if(info$type=="corr"){
co <- info$cormat
co <- melt(t(co))
co$label <- round(co$value, 2)
}
fillColor <- switch(getOption("yuimaGUItheme"), "black"="#282828", "white"="#f0f4f5")
textColor <- switch(getOption("yuimaGUItheme"), "black"="#CDCECD", "white"="black")
ggplot(co, aes(Var1, Var2)) + # x and y axes => Var1 and Var2
geom_tile(aes(fill = value)) + # background colours are mapped according to the value column
geom_text(aes(label = co$label)) + # write the values
scale_fill_gradient2(low = "#ffa500",
mid = switch(getOption("yuimaGUItheme"), "black"="gray30", "white"="#C7E2DF"),
high = "#74d600",
midpoint = 0) + # determine the colour
theme(panel.grid.major.x=element_blank(), #no gridlines
panel.grid.minor.x=element_blank(),
panel.grid.major.y=element_blank(),
panel.grid.minor.y=element_blank(),
panel.background=element_rect(fill=fillColor), # background=white
plot.background = element_rect(fill = fillColor, linetype = 0, color = fillColor),
axis.text.x = element_text(angle=90,hjust = 1, size = 12,face = "bold", colour = textColor),
plot.title = element_text(size=20,face="bold", colour = textColor, hjust = 0.5),
axis.text.y = element_text(size = 12,face = "bold", colour = textColor)) +
ggtitle(paste("Analyzed data from", info$start, "to", info$end)) +
theme(legend.title=element_text(face="bold", size=14)) +
scale_x_discrete(name="") +
scale_y_discrete(name="") +
labs(fill="")
}
})
observeEvent(input$llag_delete_analysis, {
yuimaGUIdata$llag[[input$llag_analysis_id]] <<- NULL
})
observeEvent(input$llag_deleteAll_analysis, {
yuimaGUIdata$llag <<- list()
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/eda/llag_and_corr.R |
hedging_databaseModels_table <- data.frame()
output$hedging_databaseModels <- DT::renderDataTable(options=list(scrollY = 200, scrollX = TRUE, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$model)==0){
NoData <- data.frame("Symb"=NA,"Please estimate some models first"=NA, check.names = FALSE)
return(NoData[-1,])
}
date_indexed <- c()
for (row in rownames(yuimaGUItable$model)){
id <- unlist(strsplit(row, split = " "))
if (class(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date")
date_indexed <- c(date_indexed,row)
}
hedging_databaseModels_table <<- yuimaGUItable$model[rownames(yuimaGUItable$model) %in% date_indexed,]
return (hedging_databaseModels_table)
})
output$hedging_assMarketPrice <- renderUI({
if (is.null(input$hedging_databaseModels_rows_selected))
numericInput("hedging_assMarketPrice", label="Asset Market Price:", value=NA, min = 0)
else {
if(input$hedging_databaseModels_row_last_clicked %in% input$hedging_databaseModels_rows_selected){
id <- unlist(strsplit(rownames(hedging_databaseModels_table)[input$hedging_databaseModels_row_last_clicked], split = " "))
numericInput("hedging_assMarketPrice", label="Asset Market Price:", value=as.numeric(tail(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected],1)), min = 0)
}
else
numericInput("hedging_assMarketPrice", label="Asset Market Price:", value=NA, min = 0)
}
})
output$hedging_strike <- renderUI({
if (is.null(input$hedging_databaseModels_rows_selected))
numericInput("hedging_strike", label="Strike Price:", value=0, min = 0, max = NA, step = NA, width = NULL)
else {
if(input$hedging_databaseModels_row_last_clicked %in% input$hedging_databaseModels_rows_selected){
id <- unlist(strsplit(rownames(hedging_databaseModels_table)[input$hedging_databaseModels_row_last_clicked], split = " "))
numericInput("hedging_strike", label="Strike Price:", value=as.numeric(tail(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected],1)), min = 0, max = NA, step = NA, width = NULL)
}
else
numericInput("hedging_assMarketPrice", label="Asset Market Price:", value=NA, min = 0)
}
})
observeEvent(input$hedging_button_startComputation, {
closeAlert(session, "hedging_alert_selectRow")
if (is.na(input$hedging_optMarketPrice)){
createAlert(session, anchorId = "hedging_alert", alertId = "hedging_alert_selectRow", content = "Option market price is missing", style = "error")
return()
}
if (input$hedging_optMarketPrice<=0){
createAlert(session, anchorId = "hedging_alert", alertId = "hedging_alert_selectRow", content = "Option market price must be positive", style = "error")
return()
}
if (!is.null(input$hedging_databaseModels_rows_selected) & !is.null(input$hedging_databaseModels_row_last_clicked)){
if(input$hedging_databaseModels_row_last_clicked %in% input$hedging_databaseModels_rows_selected){
modID <- rownames(hedging_databaseModels_table)[input$hedging_databaseModels_row_last_clicked]
id <- unlist(strsplit(modID, split = " "))
info = list(
"maturity"= input$hedging_maturity,
"strike"=input$hedging_strike,
"type"=input$hedging_type,
"optPrice"=input$hedging_optMarketPrice,
"optLotMult"=input$hedging_lotMult,
"optLotCost"= ifelse(is.na(input$hedging_lotCostOpt), 0, input$hedging_lotCostOpt),
"assPrice"=input$hedging_assMarketPrice,
"assPercCost"= ifelse(is.na(input$hedging_percCostAss), 0, input$hedging_percCostAss/100),
"assMinCost"= ifelse(is.na(input$hedging_minCostAss), 0, input$hedging_minCostAss),
"assRateShortSelling"= ifelse(is.na(input$hedging_rateShort), 0, input$hedging_rateShort/100))
addHedging(
symbName = id[1],
modelYuimaGUI = yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]],
info = info,
xinit = input$hedging_assMarketPrice,
nsim = input$hedging_nSim,
nstep = NA,
simulate.from = end(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]),
simulate.to = input$hedging_maturity,
session = session,
anchorId = "hedging_alert"
)
updateTabsetPanel(session = session, inputId = "panel_hedging", selected = "Profit&Loss")
}
else createAlert(session, anchorId = "hedging_alert", alertId = "hedging_alert_selectRow" , content = "Please select a model to simulate the evolution of the underlying asset", style = "error")
}
else createAlert(session, anchorId = "hedging_alert", alertId = "hedging_alert_selectRow", content = "Please select a model to simulate the evolution of the underlying asset", style = "error")
})
output$hedging_table_results <- DT::renderDataTable(options=list(scrollX=TRUE, scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$hedging)==0){
NoData <- data.frame("Symb"=NA, "Here will be stored simulations you run in the previous tab"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$hedging)
})
hedging_values <- reactiveValues(profits=NULL, symb=NULL, model=NULL, return = NA)
hedging_values2 <- reactiveValues(number_of_ids = 0)
na_zero <- function(x){ifelse(is.na(x), 0, x)}
observe({
hedging_values2$number_of_ids <<- length(yuimaGUIdata$hedging)
shinyjs::toggle(id = "hedging_button_show", condition = hedging_values2$number_of_ids>0)
})
output$hedging_modal_id <- renderUI({
if(hedging_values2$number_of_ids>0)
selectInput("hedging_modal_id", label = "ID", choices = seq(1, hedging_values2$number_of_ids))
})
output$hedging_modal_id_hidden <- renderUI({
selectInput("hedging_modal_id_hidden", label = "ID", choices = input$hedging_modal_id, selected = input$hedging_modal_id)
})
output$hedging_nAss_hedge <- renderUI({
if (!is.null(input$hedging_modal_id)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
info <- yuimaGUIdata$hedging[[id]]$info
val <- switch (info$type, "call" = info$sell, "put" = info$buy)
lab <- paste("Number of Assets to", ifelse(info$type=="call", "Sell", "Buy"))
numericInput("hedging_nAss_hedge", label = lab, min = 0, value = val, step = 1)
}
}
})
output$hedging_nOptLot_hedge <- renderUI({
if (!is.null(input$hedging_modal_id)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
info <- yuimaGUIdata$hedging[[id]]$info
nOpt <- info$LotsToBuy
numericInput("hedging_nOptLot_hedge", label = "Option - number of Lots", min = 0, value = nOpt, step = 1)
}
}
})
output$hedging_type2 <- renderUI({
if (!is.null(input$hedging_modal_id)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
type <- yuimaGUIdata$hedging[[id]]$info$type
selectInput("hedging_type2", width = "75%", label="Modify Type", c(Call="call", Put="put"), selected = type)
}
}
})
output$hedging_strike2 <- renderUI({
if (!is.null(input$hedging_modal_id)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
strike <- yuimaGUIdata$hedging[[id]]$info$strike
numericInput("hedging_strike2", width = "75%", label = "Modify Strike", min = 0, value = strike)
}
}
})
output$hedging_optMarketPrice2 <- renderUI({
if (!is.null(input$hedging_modal_id)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
optPrice <- yuimaGUIdata$hedging[[id]]$info$optPrice
numericInput("hedging_optMarketPrice2", width = "75%", label = "Modify Market Price", min = 0, value = optPrice)
}
}
})
observe({
id <- input$hedging_modal_id_hidden
if (!is.null(id) & !is.null(input$hedging_strike2) & !is.null(input$hedging_nAss_hedge)){
id <- as.integer(id)
if(hedging_values2$number_of_ids>=id){
info <- yuimaGUIdata$hedging[[id]]$info
profits <- profit_distribution(nOpt= na_zero(input$hedging_nOptLot_hedge)*info$optLotMult,
nAss= na_zero(input$hedging_nAss_hedge),
type=input$hedging_type2,
strike=ifelse(is.na(input$hedging_strike2), info$strike, input$hedging_strike2),
priceAtMaturity=yuimaGUIdata$hedging[[id]]$hist,
optMarketPrice=ifelse(is.na(input$hedging_optMarketPrice2), info$optPrice, input$hedging_optMarketPrice2),
assMarketPrice=info$assPrice,
percCostAss=na_zero(input$hedging_percCostAss)/100,
minCostAss=na_zero(input$hedging_minCostAss),
lotCostOpt=na_zero(input$hedging_lotCostOpt),
lotMultiplier=info$optLotMult,
shortCostPerYear=na_zero(input$hedging_rateShort)/100,
t0=info$today,
maturity=info$maturity)
hedging_values$profits <- profits
hedging_values$symb <- yuimaGUIdata$hedging[[id]]$symb
hedging_values$model <- yuimaGUIdata$hedging[[id]]$model$info$modName
}
}
})
output$hedging_plot_distribution <- renderPlot({
par(bg="black")
if (!is.null(hedging_values$profits) & !is.null(hedging_values$model) & !is.null(hedging_values$symb))
hist(hedging_values$profits, main = paste(hedging_values$symb,"-",hedging_values$model), xlab = "Profit & Loss", breaks = input$hedging_slider_nBin, col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black", right = FALSE)
grid()
})
output$hedging_quantiles_text <- renderUI({
if(!is.null(input$hedging_slider_rangeHist) & !is.null(hedging_values$profits)){
val <- hedging_values$profits
qq <- quantile(val, probs = input$hedging_slider_rangeHist/100)
HTML(paste("<div>", "Lower:", round(qq[1],0),"<br/>", "Upper: ", round(qq[2],0), "<br/>", "Mean: ", round(mean(val[val>=qq[1] & val<=qq[2]]),0), "</div>"))
}
})
output$hedging_capital_text <- renderUI({
if (!is.null(input$hedging_modal_id) & !is.null(hedging_values$profits)){
id <- as.integer(input$hedging_modal_id)
if (hedging_values2$number_of_ids>=id){
info <- isolate({yuimaGUIdata$hedging[[id]]$info})
optPrice <- ifelse(is.na(input$hedging_optMarketPrice2), info$optPrice, input$hedging_optMarketPrice2)
percCostAss <- na_zero(input$hedging_percCostAss)/100
minCostAss <- na_zero(input$hedging_minCostAss)
lotCostOpt <- na_zero(input$hedging_lotCostOpt)
nOptLot <- na_zero(input$hedging_nOptLot_hedge)
nAss <- na_zero(input$hedging_nAss_hedge)
cap <- nOptLot*(info$optLotMult*optPrice+lotCostOpt)+nAss*info$assPrice + ifelse(nAss>0,max(nAss*info$assPrice*percCostAss,minCostAss),0)
val <- hedging_values$profits
ret <- mean(val)/cap
hedging_values$return <- ret
HTML(paste("Invested Capital: ", round(cap,0), "<br/>", "Average Return: ", round(ret*100,2), "%"))
}
}
})
observeEvent(input$hedging_button_saveHedging, {
id <- as.integer(input$hedging_modal_id)
yuimaGUIdata$hedging[[id]]$info$assPercCost <<- ifelse(is.na(input$hedging_percCostAss), 0, input$hedging_percCostAss/100)
yuimaGUIdata$hedging[[id]]$info$assMinCost <<- ifelse(is.na(input$hedging_minCostAss), 0, input$hedging_minCostAss)
yuimaGUIdata$hedging[[id]]$info$assRateShortSelling <<- ifelse(is.na(input$hedging_rateShort), 0, input$hedging_rateShort/100)
yuimaGUIdata$hedging[[id]]$info$optLotCost <<- ifelse(is.na(input$hedging_lotCostOpt), 0, input$hedging_lotCostOpt)
yuimaGUIdata$hedging[[id]]$info$type <<- input$hedging_type2
if (yuimaGUIdata$hedging[[id]]$info$type=="put"){
yuimaGUIdata$hedging[[id]]$info$buy <<- na_zero(input$hedging_nAss_hedge)
yuimaGUIdata$hedging[[id]]$info$sell <<- NA
}
if (yuimaGUIdata$hedging[[id]]$info$type=="call"){
yuimaGUIdata$hedging[[id]]$info$sell <<- na_zero(input$hedging_nAss_hedge)
yuimaGUIdata$hedging[[id]]$info$buy <<- NA
}
yuimaGUIdata$hedging[[id]]$info$LotsToBuy <<- na_zero(input$hedging_nOptLot_hedge)
if (!is.na(input$hedging_strike2))
yuimaGUIdata$hedging[[id]]$info$strike <<- input$hedging_strike2
if (!is.na(input$hedging_optMarketPrice2))
yuimaGUIdata$hedging[[id]]$info$optPrice <<- input$hedging_optMarketPrice2
yuimaGUIdata$hedging[[id]]$info$profit <<- hedging_values$return
})
observe({
shinyjs::toggle("hedging_alert_selectRow", condition = (input$panel_hedging=="Start simulations"))
})
###Delete Hedging
observeEvent(input$hedging_button_delete, priority = 1, {
if(!is.null(input$hedging_table_results_rows_selected) & !is.null(input$hedging_modal_id)){
if(input$hedging_table_results_row_last_clicked %in% input$hedging_table_results_rows_selected){
delHedging(n=input$hedging_table_results_row_last_clicked)
}
}
})
###DeleteAll Hedging
observeEvent(input$hedging_button_deleteAll, priority = 1, {
if(!is.null(input$hedging_table_results_rows_all))
delHedging(n=input$hedging_table_results_rows_all)
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/finance/profit_and_loss.R |
rbind.fill <- function(..., rep = NA){
dots <- list(...)
names <- c()
for (i in length(dots):1){
if (length(rownames(dots[[i]]))==0)
dots[i] <- NULL
else
names <- unique(c(names, colnames(dots[[i]])))
}
for (symb in names)
for (i in 1:length(dots))
if (!(symb %in% colnames(dots[[i]])))
dots[[i]][,symb] <- rep
return (do.call("rbind", dots))
}
melt <- function(x){
V1 <- rep(rownames(x), ncol(x))
V2 <- sort(V1)
xx <- data.frame(Var1 = V1, Var2 = V2, value = NA)
for (i in 1:nrow(xx)) xx[i,"value"] <- x[as.character(xx[i,"Var1"]), as.character(xx[i,"Var2"])]
return(xx)
}
mode <- function(x) {
ux <- unique(na.omit(x))
ux[which.max(tabulate(match(x, ux)))]
}
isUserDefined <- function(name){
n <- names(isolate({yuimaGUIdata$usr_model}))
if (length(n)!=0) return (name %in% n)
return (FALSE)
}
setDataGUI <- function(original.data, delta){
original.data <- na.omit(original.data)
delta <- max(delta)
t <- index(original.data)
t0 <- 0
if(is.numeric(t)){
delta.original.data <- mode(diff(t))
t0 <- min(t, na.rm = TRUE)*delta/delta.original.data
}
setData(original.data = original.data, delta = delta, t0 = t0)
}
addData <- function(x, typeIndex){
x <- data.frame(x, check.names = TRUE)
err <- c()
alreadyIn <- c()
for (symb in colnames(x)){
if (symb %in% names(yuimaGUIdata$series))
alreadyIn <- c(alreadyIn, symb)
else{
temp <- data.frame("Index" = rownames(x), "symb" = as.numeric(gsub(as.character(x[,symb]), pattern = ",", replacement = ".")))
temp <- temp[complete.cases(temp), ]
rownames(temp) <- temp[,"Index"]
colnames(temp) <- c("Index", symb)
if (all(is.na(temp[,2]))) err <- c(err, symb)
else if (typeIndex=="numeric"){
test <- try(read.zoo(temp, FUN=as.numeric, drop = FALSE))
if (class(test)!="try-error")
yuimaGUIdata$series[[symb]] <<- test
else
err <- c(err, symb)
}
else{
test <- try(read.zoo(temp, FUN=as.Date, format = typeIndex, drop = FALSE))
if (class(test)!="try-error")
yuimaGUIdata$series[[symb]] <<- test
else
err <- c(err, symb)
}
}
}
return(list(err = err, already_in = alreadyIn))
}
getData <- function(symb){
return(isolate({yuimaGUIdata$series[[symb]]}))
}
delData <- function(symb){
for (i in symb)
yuimaGUIdata$series <<- yuimaGUIdata$series[-which(names(yuimaGUIdata$series)==i)]
}
defaultBounds <- function(name, delta, strict, jumps = NA, AR_C = NA, MA_C = NA, data, intensity = NULL, threshold = NULL){
lastPrice = as.numeric(last(data))
if ( isUserDefined(name) ){
mod <- setModelByName(name = name, jumps = jumps, AR_C = AR_C, MA_C = MA_C)
par <- getAllParams(mod, yuimaGUIdata$usr_model[[name]]$class)
if(strict==TRUE){
lower <- rep(NA, length(par))
upper <- rep(NA, length(par))
} else {
if (yuimaGUIdata$usr_model[[name]]$class=="Compound Poisson"){
lower <- rep(0, length(par))
upper <- rep(1, length(par))
} else {
lower <- rep(-100, length(par))
upper <- rep(100, length(par))
}
}
names(lower) <- par
names(upper) <- par
if (!is.na(jumps)){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
for (i in par[par %in% names(boundsJump$lower)]){
lower[[i]] <- boundsJump$lower[[i]]
upper[[i]] <- boundsJump$upper[[i]]
}
}
return(list(lower=as.list(lower), upper=as.list(upper)))
}
if (name == "Hawkes"){
if (strict==TRUE) return (list(lower=list("nu1"=0, "c11"=0, "a11"=0), upper=list("nu1"=NA, "c11"=100, "a11"=NA)))
else {
x <- as.numeric(diff(data))
t1 <- tail(time(data),n=1)
t0 <- time(data)[1]
n <- length(x[x!=0])
nu1 <- n/as.numeric(t1-t0)
c11 <- 0
a11 <- 1
return (list(lower=list("nu1"=nu1, "c11"=c11, "a11"=a11), upper=list("nu1"=nu1, "c11"=c11, "a11"=a11)))
}
}
if (name == "Hawkes Power Law Kernel"){
if (strict==TRUE) return (list(lower=list("nu"=0, "k"=NA, "beta"=NA, 'gamma'=NA), upper=list("nu"=NA, "k"=NA, "beta"=NA, 'gamma'=NA)))
else {
x <- as.numeric(diff(data))
t1 <- tail(time(data),n=1)
t0 <- time(data)[1]
n <- length(x[x!=0])
nu <- n/as.numeric(t1-t0)
return (list(lower=list("nu"=nu, "k"=0, "beta"=0, 'gamma'=-1), upper=list("nu"=nu, "k"=0, "beta"=0, 'gamma'=1)))
}
}
if (name %in% defaultModels[names(defaultModels) == "COGARCH"]){
mod <- setModelByName(name = name, jumps = jumps, AR_C = AR_C, MA_C = MA_C)
par <- getAllParams(mod, "COGARCH")
if(strict==TRUE){
lower <- rep(NA, length(par))
upper <- rep(NA, length(par))
} else {
lower <- rep(0, length(par))
upper <- rep(10, length(par))
}
names(lower) <- par
names(upper) <- par
return(list(lower=as.list(lower), upper=as.list(upper)))
}
if (name %in% defaultModels[names(defaultModels) == "CARMA"]){
mod <- setModelByName(name = name, jumps = jumps, AR_C = AR_C, MA_C = MA_C)
par <- getAllParams(mod, "CARMA")
if(strict==TRUE){
lower <- rep(NA, length(par))
upper <- rep(NA, length(par))
names(lower) <- par
names(upper) <- par
} else {
lower <- rep(0, length(par))
upper <- rep(1, length(par))
names(lower) <- par
names(upper) <- par
lower["MA0"] <- min(lastPrice*0.5, lastPrice*1.5)
upper["MA0"] <- max(lastPrice*0.5, lastPrice*1.5)
}
return(list(lower=as.list(lower), upper=as.list(upper)))
}
if (name == "Brownian Motion" | name == "Bm"){
if (strict==TRUE) return (list(lower=list("sigma"=0, "mu"=NA), upper=list("sigma"=NA, "mu"=NA)))
else {
x <- as.numeric(diff(data))
mu <- mean(x)
sigma <- sd(x)
return (list(lower=list("sigma"=sigma/sqrt(delta), "mu"=mu/delta), upper=list("sigma"=sigma/sqrt(delta), "mu"=mu/delta)))
}
}
if (name == "Geometric Brownian Motion" | name == "gBm") {
if (strict==TRUE) return (list(lower=list("sigma"=0, "mu"=NA), upper=list("sigma"=NA, "mu"=NA)))
else {
x <- as.numeric(na.omit(Delt(data)))
mu <- mean(x)
sigma <- sd(x)
return (list(lower=list("sigma"=sigma/sqrt(delta), "mu"=mu/delta), upper=list("sigma"=sigma/sqrt(delta), "mu"=mu/delta)))
}
}
if (name == "Ornstein-Uhlenbeck (OU)" | name == "OU"){
if (strict==TRUE) return(list(lower=list("theta"=0, "sigma"=0),upper=list("theta"=NA, "sigma"=NA)))
else return(list(lower=list("theta"=0, "sigma"=0),upper=list("theta"=1/delta, "sigma"=1/sqrt(delta))))
}
if (name == "Vasicek model (VAS)" | name == "VAS"){
if (strict==TRUE) return(list(lower=list("theta3"=0, "theta1"=NA, "theta2"=NA), upper=list("theta3"=NA, "theta1"=NA, "theta2"=NA)))
else {
mu <- abs(mean(as.numeric(data), na.rm = TRUE))
return(list(lower=list("theta3"=0, "theta1"=-0.1*mu/delta, "theta2"=-0.1/delta), upper=list("theta3"=1/sqrt(delta), "theta1"=0.1*mu/delta, "theta2"=0.1/delta)))
}
}
if (name == "Constant elasticity of variance (CEV)" | name == "CEV"){
if (strict==TRUE) return(list(lower=list("mu"=NA, "sigma"=0, "gamma"=0), upper=list("mu"=NA, "sigma"=NA, "gamma"=NA)))
else return(list(lower=list("mu"=-1/delta, "sigma"=0, "gamma"=0), upper=list("mu"=1/delta, "sigma"=1/sqrt(delta), "gamma"=3)))
}
if (name == "Cox-Ingersoll-Ross (CIR)" | name == "CIR"){
if (strict==TRUE) return(list(lower=list("theta1"=0,"theta2"=0,"theta3"=0),upper=list("theta1"=NA,"theta2"=NA,"theta3"=NA)))
else return(list(lower=list("theta1"=0,"theta2"=0,"theta3"=0),upper=list("theta1"=1/delta,"theta2"=1/delta,"theta3"=1/sqrt(delta))))
}
if (name == "Chan-Karolyi-Longstaff-Sanders (CKLS)" | name == "CKLS"){
if (strict==TRUE) return(list(lower=list("theta1"=NA, "theta2"=NA, "theta3"=0, "theta4"=0), upper=list("theta1"=NA, "theta2"=NA, "theta3"=NA, "theta4"=NA)))
else return(list(lower=list("theta1"=-1/delta, "theta2"=-1/delta, "theta3"=0, "theta4"=0), upper=list("theta1"=1/delta, "theta2"=1/delta, "theta3"=1/sqrt(delta), "theta4"=3)))
}
if (name == "Hyperbolic (Barndorff-Nielsen)" | name == "hyp1"){
if (strict==TRUE) return(list(lower=list("delta"=0, "alpha"=0, "beta"=0, "sigma"=0, "mu"=0), upper=list("delta"=NA, "alpha"=NA, "beta"=NA, "sigma"=NA, "mu"=NA)))
else return(list(lower=list("delta"=0, "alpha"=0, "beta"=0, "sigma"=0, "mu"=0), upper=list("delta"=100, "alpha"=10, "beta"=10, "sigma"=1/sqrt(delta), "mu"=mean(as.numeric(data), na.rm = TRUE))))
}
if (name == "Hyperbolic (Bibby and Sorensen)" | name == "hyp2"){
if (strict==TRUE) return(list(lower=list("delta"=0, "alpha"=0, "beta"=0, "sigma"=0, "mu"=0), upper=list("delta"=NA, "alpha"=NA, "beta"=NA, "sigma"=NA, "mu"=NA)))
else return(list(lower=list("delta"=0, "alpha"=0, "beta"=0, "sigma"=0, "mu"=0),upper=list("delta"=10, "alpha"=1, "beta"=10, "sigma"=1/sqrt(delta), "mu"=mean(as.numeric(data), na.rm = TRUE))))
}
if (name == "Constant Intensity"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
if (strict==TRUE) return(list(lower=c(list("lambda"=0), boundsJump$lower),upper=c(list("lambda"=NA), boundsJump$upper)))
else {
x <- as.numeric(diff(data))
counts <- length(x[x!=0 & !is.na(x)])
lambda <- counts/(length(x)*delta)
return(list(lower=c(list("lambda"=lambda), boundsJump$lower),upper=c(list("lambda"=lambda), boundsJump$upper)))
}
}
if (name == "Power Low Intensity"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
if (strict==TRUE) return(list(lower=c(list("alpha"=0, "beta"=NA), boundsJump$lower),upper=c(list("alpha"=NA, "beta"=NA), boundsJump$upper)))
else {
x <- as.numeric(diff(data))
counts <- length(x[x!=0 & !is.na(x)])
alpha <- counts/(length(x)*delta)
return(list(lower=c(list("alpha"=0, "beta"=0), boundsJump$lower),upper=c(list("alpha"=alpha, "beta"=0), boundsJump$upper)))
}
}
if (name == "Linear Intensity"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
if (strict==TRUE) return(list(lower=c(list("alpha"=0, "beta"=0), boundsJump$lower),upper=c(list("alpha"=NA, "beta"=NA), boundsJump$upper)))
else {
x <- as.numeric(diff(data))
counts <- length(x[x!=0 & !is.na(x)])
alpha <- counts/(length(x)*delta)
return(list(lower=c(list("alpha"=0, "beta"=0), boundsJump$lower),upper=c(list("alpha"=alpha, "beta"=0), boundsJump$upper)))
}
}
if (name == "Exponentially Decaying Intensity"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
if (strict==TRUE) return(list(lower=c(list("alpha"=0, "beta"=0), boundsJump$lower),upper=c(list("alpha"=NA, "beta"=NA), boundsJump$upper)))
else {
x <- as.numeric(diff(data))
counts <- length(x[x!=0 & !is.na(x)])
alpha <- counts/(length(x)*delta)
return(list(lower=c(list("alpha"=0, "beta"=0), boundsJump$lower),upper=c(list("alpha"=alpha, "beta"=0), boundsJump$upper)))
}
}
if (name == "Periodic Intensity"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data)
if (strict==TRUE) return(list(lower=c(list("a"=0, "b"=0, "omega"=0, "phi"=0), boundsJump$lower),upper=c(list("a"=NA, "b"=NA, "omega"=NA, "phi"=2*pi), boundsJump$upper)))
else return(list(lower=c(list("a"=0, "b"=0, "omega"=0, "phi"=0), boundsJump$lower),upper=c(list("a"=1/delta, "b"=1/delta, "omega"=1/delta, "phi"=2*pi), boundsJump$upper)))
}
if (name == "Geometric Brownian Motion with Jumps"){
boundsJump <- jumpBounds(jumps = jumps, strict = strict, data = data, threshold = threshold)
boundsIntensity <- intensityBounds(intensity = intensity, strict = strict, delta = delta)
if (strict==TRUE) return(list(lower=c(list("mu"=NA, "sigma"=0), boundsJump$lower, boundsIntensity$lower),upper=c(list("mu"=NA, "sigma"=NA), boundsJump$upper, boundsIntensity$upper)))
else return(list(lower=c(list("mu"=-1, "sigma"=0), boundsJump$lower, boundsIntensity$lower),upper=c(list("mu"=1, "sigma"=1), boundsJump$upper, boundsIntensity$upper)))
}
if (name == "Correlated Brownian Motion"){
mod <- setModelByName(name = name, jumps = jumps, AR_C = AR_C, MA_C = MA_C, dimension = ncol(data))
par <- getAllParams(mod, "Diffusion process", FALSE)
drift <- rep(NA, length(par@drift))
diffusion <- rep(NA, length(par@diffusion))
names(drift) <- par@drift
names(diffusion) <- par@diffusion
if (strict==TRUE) {
diffusion[] <- 0; lower_diffusion <- diffusion
diffusion[] <- NA; upper_diffusion <- diffusion
drift[] <- NA; lower_drift <- drift
drift[] <- NA; upper_drift <- drift
return (list(lower=as.list(c(lower_drift, lower_diffusion)), upper=as.list(c(upper_drift, upper_diffusion))))
}
else {
x <- na.omit(diff(data))
mu <- colMeans(x)
sigma <- sapply(x, sd)
drift[] <- mu/delta; lower_drift <- drift; upper_drift <- drift
diffusion[] <- 0; diffusion[paste("s",seq(1,ncol(data)),seq(1,ncol(data)), sep = "")] <- sigma/sqrt(delta); lower_diffusion <- diffusion; upper_diffusion <- diffusion
return (list(lower=as.list(c(lower_drift, lower_diffusion)), upper=as.list(c(upper_drift, upper_diffusion))))
}
}
}
setThreshold <- function(class, data){
if(class!="Levy process") return(NA)
else {
return(0)
}
}
setJumps <- function(jumps){
if(is.na(jumps)) return("")
if(jumps=='Gaussian') {
return(list("dnorm(z, mean = mu_jump, sd = sigma_jump)"))
}
if(jumps=='Constant') {
return(list("dconst(z, k = k_jump)"))
}
if(jumps=='Uniform') {
return(list("dunif(z, min = a_jump, max = b_jump)"))
}
if(jumps=='Inverse Gaussian') {
return(list("dIG(z, delta = delta_jump, gamma = gamma_jump)"))
}
if(jumps=='Normal Inverse Gaussian') {
return(list("dNIG.gui(z, alpha = alpha_jump, beta = beta_jump, delta = delta_jump, mu = mu_jump)"))
}
if(jumps=='Hyperbolic') {
return(list("dhyp.gui(z, alpha = alpha_jump, beta = beta_jump, delta = delta_jump, mu = mu_jump)"))
}
if(jumps=='Student t') {
return(list("dt(z, df = nu_jump, ncp = mu_jump)"))
}
if(jumps=='Variance Gamma') {
return(list("dVG.gui(z, lambda = lambda_jump, alpha = alpha_jump, beta = beta_jump, mu = mu_jump)"))
}
if(jumps=='Generalized Hyperbolic') {
return(list("dghyp.gui(z, lambda = lambda_jump, alpha = alpha_jump, delta = delta_jump, beta = beta_jump, mu = mu_jump)"))
}
}
jumpBounds <- function(jumps, data, strict, threshold = 0){
x <- na.omit(as.numeric(diff(data)))
x <- x[abs(x)>threshold]
x <- x-sign(x)*threshold
switch(jumps,
"Gaussian" = {
if(strict==TRUE) return(list(lower=list("mu_jump"=NA, "sigma_jump"=0), upper=list("mu_jump"=NA, "sigma_jump"=NA)))
else {
mu <- mean(x)
s <- sd(x)
return(list(lower=list("mu_jump"=mu, "sigma_jump"=s), upper=list("mu_jump"=mu, "sigma_jump"=s)))
}
},
"Uniform" = {
if(strict==TRUE) return(list(lower=list("a_jump"=NA, "b_jump"=NA), upper=list("a_jump"=NA, "b_jump"=NA)))
else {
a <- min(x)
b <- max(x)
return(list(lower=list("a_jump"=a, "b_jump"=b), upper=list("a_jump"=a, "b_jump"=b)))
}
},
"Constant" = {
if(strict==TRUE) return(list(lower=list("k_jump"=NA), upper=list("k_jump"=NA)))
else {
k <- median(x)
return(list(lower=list("k_jump"=k), upper=list("k_jump"=k)))
}
},
"Inverse Gaussian" = {
if(strict==TRUE) return(list(lower=list("delta_jump"=NA, "gamma_jump"=NA), upper=list("delta_jump"=NA, "gamma_jump"=NA)))
else {
x <- x[x>0]
delta <- mean(x)
gamma <- delta^3/var(x)
return(list(lower=list("delta_jump"=delta, "gamma_jump"=gamma), upper=list("delta_jump"=delta, "gamma_jump"=gamma)))
}
},
"Normal Inverse Gaussian" = {
if(strict==TRUE) return(list(lower=list("alpha_jump"=0, "beta_jump"=NA, "delta_jump"=0, "mu_jump"=NA), upper=list("alpha_jump"=NA, "beta_jump"=NA, "delta_jump"=NA, "mu_jump"=NA)))
else {
fit <- try(coef(fit.NIGuv(x), type = 'alpha.delta'))
if(class(fit)!='try-error'){
alpha <- fit$alpha
beta <- fit$beta
delta <- fit$delta
mu <- fit$mu
} else {
alpha <- 1.5
beta <- 0
delta <- 1
mu <- mean(x)
}
return(list(lower=list("alpha_jump"=alpha, "beta_jump"=beta, "delta_jump"=delta, "mu_jump" = mu), upper=list("alpha_jump"=alpha, "beta_jump"=beta, "delta_jump"=delta, "mu_jump" = mu)))
}
},
"Hyperbolic" = {
if(strict==TRUE) return(list(lower=list("alpha_jump"=NA, "beta_jump"=NA, "delta_jump"=NA, "mu_jump"=NA), upper=list("alpha_jump"=NA, "beta_jump"=NA, "delta_jump"=NA, "mu_jump"=NA)))
else {
fit <- try(coef(fit.hypuv(x), type = 'alpha.delta'))
if(class(fit)!='try-error'){
alpha <- fit$alpha
beta <- fit$beta
delta <- fit$delta
mu <- fit$mu
} else {
alpha <- 1.5
beta <- 0
delta <- 1
mu <- mean(x)
}
return(list(lower=list("alpha_jump"=alpha, "beta_jump"=beta, "delta_jump"=delta, "mu_jump" = mu), upper=list("alpha_jump"=alpha, "beta_jump"=beta, "delta_jump"=delta, "mu_jump" = mu)))
}
},
"Student t" = {
if(strict==TRUE) return(list(lower=list("nu_jump"=0, "mu_jump"=NA), upper=list("nu_jump"=NA, "mu_jump"=NA)))
else {
mu <- mean(x)
nu <- 1
return(list(lower=list("nu_jump"=nu, "mu_jump" = mu), upper=list("nu_jump"=nu, "mu_jump" = mu)))
}
},
"Variance Gamma" = {
if(strict==TRUE) return(list(lower=list("lambda_jump"=0, "alpha_jump"=NA, "beta_jump"=NA, "mu_jump"=NA), upper=list("lambda_jump"=NA, "alpha_jump"=NA, "beta_jump"=NA, "mu_jump"=NA)))
else {
fit <- try(coef(fit.VGuv(x), type = 'alpha.delta'))
if(class(fit)!='try-error'){
lambda <- fit$lambda
alpha <- fit$alpha
beta <- fit$beta
mu <- fit$mu
} else {
lambda <- 1
alpha <- 1.5
beta <- 0
mu <- mean(x)
}
return(list(lower=list("lambda_jump"=lambda, "alpha_jump"=alpha, "beta_jump"=beta, "mu_jump" = mu), upper=list("lambda_jump"=lambda, "alpha_jump"=alpha, "beta_jump"=beta, "mu_jump" = mu)))
}
},
"Generalized Hyperbolic" = {
if(strict==TRUE) return(list(lower=list("lambda_jump"=NA, "alpha_jump"=NA, "delta_jump"=NA, "beta_jump"=NA, "mu_jump"=NA), upper=list("lambda_jump"=NA, "alpha_jump"=NA, "delta_jump"=NA, "beta_jump"=NA, "mu_jump"=NA)))
else {
fit <- try(coef(fit.ghypuv(x), type = 'alpha.delta'))
if(class(fit)!='try-error'){
lambda <- fit$lambda
alpha <- fit$alpha
delta <- fit$delta
beta <- fit$beta
mu <- fit$mu
} else {
lambda <- 0.5
alpha <- 1.5
delta <- 1
beta <- 0
mu <- mean(x)
}
return(list(lower=list("lambda_jump"=lambda, "alpha_jump"=alpha, "delta_jump"=delta, "beta_jump"=beta, "mu_jump" = mu), upper=list("lambda_jump"=lambda, "alpha_jump"=alpha, "delta_jump"=delta, "beta_jump"=beta, "mu_jump" = mu)))
}
}
)
}
latexJumps <- function(jumps){
if (!is.null(jumps)){
switch (jumps,
"Gaussian" = "Y_i \\sim N(\\mu_{jump}, \\; \\sigma_{jump})",
"Constant" = "Y_i = k_{jump}",
"Uniform" = "Y_i \\sim Unif(a_{jump}, \\; b_{jump})",
"Inverse Gaussian" = "Y_i \\sim IG(\\delta_{jump}, \\; \\gamma_{jump})",
"Normal Inverse Gaussian" = "Y_i \\sim NIG( \\alpha_{jump}, \\; \\beta_{jump}, \\; \\delta_{jump}, \\; \\mu_{jump})",
"Hyperbolic" = "Y_i \\sim HYP( \\alpha_{jump}, \\; \\beta_{jump}, \\; \\delta_{jump}, \\; \\mu_{jump})",
"Student t" = "Y_i \\sim t( \\nu_{jump}, \\; \\mu_{jump} )",
"Variance Gamma" = "Y_i \\sim VG( \\lambda_{jump}, \\; \\alpha_{jump}, \\; \\beta_{jump}, \\; \\mu_{jump})",
"Generalized Hyperbolic" = "Y_i \\sim GH( \\lambda_{jump}, \\; \\alpha_{jump}, \\; \\beta_{jump}, \\; \\delta_{jump}, \\; \\mu_{jump})"
)
}
}
intensityBounds <- function(intensity, strict, delta){
switch(intensity,
"lambda" = {
if(strict==TRUE) return(list(lower=list("lambda"=0), upper=list("lambda"=NA)))
else return(list(lower=list("lambda"=0), upper=list("lambda"=1/delta)))
}
)
}
setModelByName <- function(name, jumps = NA, AR_C = NA, MA_C = NA, XinExpr = FALSE, intensity = NA, dimension = 1){
dimension <- max(1, dimension)
if ( isUserDefined(name) ){
if (isolate({yuimaGUIdata$usr_model[[name]]$class=="Diffusion process" | yuimaGUIdata$usr_model[[name]]$class=="Fractional process"}))
return(isolate({yuimaGUIdata$usr_model[[name]]$object}))
if (isolate({yuimaGUIdata$usr_model[[name]]$class=="Compound Poisson"}))
return(setPoisson(intensity = isolate({yuimaGUIdata$usr_model[[name]]$intensity}), df = setJumps(jumps = jumps), solve.variable = "x"))
}
if (name == "Brownian Motion" | name == "Bm") return(yuima::setModel(drift="mu", diffusion="sigma", solve.variable = "x"))
if (name == "Geometric Brownian Motion" | name == "gBm") return(yuima::setModel(drift="mu*x", diffusion="sigma*x", solve.variable = "x"))
if (name == "Ornstein-Uhlenbeck (OU)" | name == "OU") return(yuima::setModel(drift="-theta*x", diffusion="sigma", solve.variable = "x"))
if (name == "Vasicek model (VAS)" | name == "VAS") return(yuima::setModel(drift="theta1-theta2*x", diffusion="theta3", solve.variable = "x"))
if (name == "Constant elasticity of variance (CEV)" | name == "CEV") return(yuima::setModel(drift="mu*x", diffusion="sigma*x^gamma", solve.variable = "x"))
if (name == "Cox-Ingersoll-Ross (CIR)" | name == "CIR") return(yuima::setModel(drift="theta1-theta2*x", diffusion="theta3*sqrt(x)", solve.variable = "x"))
if (name == "Chan-Karolyi-Longstaff-Sanders (CKLS)" | name == "CKLS") return(yuima::setModel(drift="theta1+theta2*x", diffusion="theta3*x^theta4", solve.variable = "x"))
if (name == "Hyperbolic (Barndorff-Nielsen)" | name == "hyp1") return(yuima::setModel(drift="(sigma/2)^2*(beta-alpha*((x-mu)/(sqrt(delta^2+(x-mu)^2))))", diffusion="sigma", solve.variable = "x"))
if (name == "Hyperbolic (Bibby and Sorensen)" | name == "hyp2") return(yuima::setModel(drift="0", diffusion="sigma*exp(0.5*(alpha*sqrt(delta^2+(x-mu)^2)-beta*(x-mu)))", solve.variable = "x"))
if (name == "Frac. Brownian Motion" | name == "Bm") return(yuima::setModel(drift="mu", diffusion="sigma", solve.variable = "x", hurst = NA))
if (name == "Frac. Geometric Brownian Motion" | name == "gBm") return(yuima::setModel(drift="mu*x", diffusion="sigma*x", solve.variable = "x", hurst = NA))
if (name == "Frac. Ornstein-Uhlenbeck (OU)" | name == "OU") return(yuima::setModel(drift="-theta*x", diffusion="sigma", solve.variable = "x", hurst = NA))
if (name == "Hawkes") return(yuima::setHawkes())
if (name == "Hawkes Power Law Kernel") {
df <- setLaw(rng = function(n){as.matrix(rep(1,n))}, dim = 1)
countMod <- setModel(drift = c("0"), diffusion = matrix("0",1,1), jump.coeff = matrix(c("1"),1,1), measure = list(df = df), measure.type = "code", solve.variable = c("N"), xinit=c("0"))
return(yuima::setPPR(yuima = countMod, counting.var="N", gFun="nu", Kernel = as.matrix("k/(beta+(t-s))^gamma"), lambda.var = "lambda", var.dx = "N", lower.var="0", upper.var = "t"))
}
if (name == "Power Low Intensity") return(yuima::setPoisson(intensity="alpha*t^(beta)", df=setJumps(jumps = jumps), solve.variable = "x"))
if (name == "Constant Intensity") return(yuima::setPoisson(intensity="lambda", df=setJumps(jumps = jumps), solve.variable = "x"))
if (name == "Linear Intensity") return(yuima::setPoisson(intensity="alpha+beta*t", df=setJumps(jumps = jumps), solve.variable = "x"))
if (name == "Exponentially Decaying Intensity") return(yuima::setPoisson(intensity="alpha*exp(-beta*t)", df=setJumps(jumps = jumps), solve.variable = "x"))
if (name == "Periodic Intensity") return(yuima::setPoisson(intensity="a/2*(1+cos(omega*t+phi))+b", df=setJumps(jumps = jumps), solve.variable = "x"))
if (name == "Cogarch(p,q)") return(yuima::setCogarch(p = MA_C, q = AR_C, measure.type = "CP", measure = list(intensity = "lambda", df = setJumps(jumps = "Gaussian")), XinExpr = XinExpr, Cogarch.var="y", V.var="v", Latent.var="x", ma.par="MA", ar.par="AR"))
if (name == "Carma(p,q)") return(yuima::setCarma(p = AR_C, q = MA_C, ma.par="MA", ar.par="AR", XinExpr = XinExpr))
if (name == "Geometric Brownian Motion with Jumps") {
if(intensity=="None") return(yuima::setModel(drift="mu*x", diffusion="sigma*x", jump.coeff="x", measure.type = "code", measure = list(df = setJumps(jumps = jumps)), solve.variable = "x"))
else return(yuima::setModel(drift="mu*x", diffusion="sigma*x", jump.coeff="x", measure.type = "CP", measure = list(intensity = intensity, df = setJumps(jumps = jumps)), solve.variable = "x"))
}
if (name == "Correlated Brownian Motion") {
mat <- matrix(rep(1:dimension, dimension),dimension,dimension)
diff <- matrix(paste("s",mat,t(mat),sep=""), dimension, dimension)
diff[lower.tri(diff, diag = FALSE)] <- 0
return(yuima::setModel(drift=paste("mu", seq(1,dimension), sep = ""), diffusion=diff, solve.variable = paste("x", seq(1,dimension))))
}
}
getAllParams <- function(mod, class, all = TRUE){
if(is(mod)=='yuima' & class!="Point Process") mod <- mod@model
if(all==TRUE){
if (class=="Point Process")
return(mod@PPR@allparamPPR)
else if (class=="Fractional process")
return(c(mod@parameter@all, "hurst"))
else if (class=="COGARCH")
return(c(mod@parameter@drift, mod@parameter@xinit))
else if (class=="CARMA")
return(mod@parameter@drift)
else
return(mod@parameter@all)
} else {
if (class=="Point Process")
return(mod@PPR)
else
return(mod@parameter)
}
}
printModelLatex <- function(names, process, jumps = NA, multi = FALSE, dimension = 1, symb = character(0)){
dimension <- max(dimension, 1)
if(length(symb)>0) dimension <- length(symb)
if (multi==TRUE){
if (process=="Diffusion process"){
text <- toLatex(setModelByName(names, dimension = dimension))
x <- paste(text[-1], collapse = "")
if(length(symb)>0) for (i in 1:dimension) {
x <- gsub(x, pattern = paste("x", i), replacement = paste("X_{", symb[i], "}", sep = ""))
} else
x <- gsub(x, pattern = "x ", replacement = "X_")
x <- gsub(x, pattern = "dW", replacement = "dW_")
x <- gsub(x, pattern = "\\$\\$\\$\\$.*", replacement = "$$")
return(x)
}
} else {
if (process=="Diffusion process"){
mod <- ""
for (name in names){
if ( isUserDefined(name) ){
text <- toLatex(setModelByName(name))
x <- paste(text[2:9], collapse = "")
x <- substr(x,3,nchar(x))
x <- gsub(x, pattern = "'", replacement = "")
x <- gsub(x, pattern = "x", replacement = "X_t")
x <- gsub(x, pattern = "W1", replacement = "W_t")
x <- gsub(x, pattern = "\\$", replacement = "")
mod <- paste(mod, ifelse(mod=="","","\\\\"), x)
}
if (name == "Brownian Motion" | name == "Bm")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = \\mu \\; dt + \\sigma \\; dW_t")
if (name == "Geometric Brownian Motion" | name == "gBm")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = \\mu X_t \\; dt + \\sigma X_t \\; dW_t")
if (name == "Ornstein-Uhlenbeck (OU)" | name == "OU")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = -\\theta X_t \\; dt + \\sigma \\; dW_t")
if (name == "Vasicek model (VAS)" | name == "VAS")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = (\\theta_1 - \\theta_2 X_t) \\;dt + \\theta_3 \\; dW_t")
if (name == "Constant elasticity of variance (CEV)" | name == "CEV")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = \\mu X_t \\;dt + \\sigma X_t^\\gamma \\; dW_t")
if (name == "Cox-Ingersoll-Ross (CIR)" | name == "CIR")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = (\\theta_1-\\theta_2 X_t) \\; dt + \\theta_3 \\sqrt{X_t} \\; dW_t")
if (name == "Chan-Karolyi-Longstaff-Sanders (CKLS)" | name == "CKLS")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = (\\theta_1+\\theta_2 X_t) \\; dt + \\theta_3 X_t^{\\theta_4} \\; dW_t")
if (name == "Hyperbolic (Barndorff-Nielsen)" | name == "hyp1")
mod <- paste(mod, ifelse(mod=="","","\\\\"),"dX_t = \\frac{\\sigma}{2}^2 \\Bigl (\\beta-\\alpha \\frac{X_t-\\mu}{\\sqrt{\\delta^2+(X_t-\\mu)^2}} \\Bigl ) \\; dt + \\sigma \\; dW_t")
if (name == "Hyperbolic (Bibby and Sorensen)" | name == "hyp2")
mod <- paste(mod, ifelse(mod=="","","\\\\"),"dX_t = \\sigma \\; exp\\Bigl[\\frac{1}{2} \\Bigl( \\alpha \\sqrt{\\delta^2+(X_t-\\mu)^2}-\\beta (X_t-\\mu)\\Bigl)\\Bigl] \\; dW_t")
}
return(paste("$$",mod,"$$"))
}
if (process=="Fractional process"){
mod <- ""
for (name in names){
if ( isUserDefined(name) ){
text <- toLatex(setModelByName(name))
x <- paste(text[2:9], collapse = "")
x <- substr(x,3,nchar(x))
x <- gsub(x, pattern = "'", replacement = "")
x <- gsub(x, pattern = "x", replacement = "X_t")
x <- gsub(x, pattern = "W1", replacement = "W_t^H")
x <- gsub(x, pattern = "\\$", replacement = "")
mod <- paste(mod, ifelse(mod=="","","\\\\"), x)
}
if (name == "Frac. Brownian Motion" | name == "Bm")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = \\mu \\; dt + \\sigma \\; dW_t^H")
if (name == "Frac. Geometric Brownian Motion" | name == "gBm")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = \\mu X_t \\; dt + \\sigma X_t \\; dW_t^H")
if (name == "Frac. Ornstein-Uhlenbeck (OU)" | name == "OU")
mod <- paste(mod, ifelse(mod=="","","\\\\"), "dX_t = -\\theta X_t \\; dt + \\sigma \\; dW_t^H")
}
return(paste("$$",mod,"$$"))
}
if (process=="Point Process"){
mod <- "\\lambda_t = \\nu_1+\\int_{0}^{t_-}kern(t-s)\\mbox{d}N_s"
for (name in names){
if ( isUserDefined(name) ){
}
if (name == "Hawkes") mod <- paste(mod, ifelse(mod=="","","\\\\"), "kern(t-s) = c_{11}\\exp\\left[-a_{11}\\left(t-s\\right)\\right]")
if( name == "Hawkes Power Law Kernel") mod <- paste(mod, ifelse(mod=="","","\\\\"), "kern(t-s) = \\frac{k}{\\left[\\beta+(t-s)\\right]^{\\gamma}}")
}
return(paste("$$",mod,"$$"))
}
if (process=="Compound Poisson"){
mod <- paste("X_t = X_0+\\sum_{i=0}^{N_t} Y_i \\; : \\;\\;\\; N_t \\sim Poi\\Bigl(\\int_0^t \\lambda(t)dt\\Bigl)", ifelse(!is.null(jumps), paste(", \\;\\;\\;\\; ", latexJumps(jumps)),""))
for (name in names){
if ( isUserDefined(name) ){
text <- paste("\\lambda(t)=",yuimaGUIdata$usr_model[[name]]$intensity)
mod <- paste(mod, ifelse(mod=="","","\\\\"), text)
}
if (name == "Power Low Intensity") mod <- paste(mod, ifelse(mod=="","","\\\\"), "\\lambda(t)=\\alpha \\; t^{\\beta}")
if (name == "Constant Intensity") mod <- paste(mod, ifelse(mod=="","","\\\\"), "\\lambda(t)=\\lambda")
if (name == "Linear Intensity") mod <- paste(mod, ifelse(mod=="","","\\\\"), "\\lambda(t)=\\alpha+\\beta \\; t")
if (name == "Exponentially Decaying Intensity") mod <- paste(mod, ifelse(mod=="","","\\\\"), "\\lambda(t)=\\alpha \\; e^{-\\beta t}")
if (name == "Periodic Intensity") mod <- paste(mod, ifelse(mod=="","","\\\\"), "\\lambda(t)=\\frac{a}{2}\\bigl(1+cos(\\omega t + \\phi)\\bigl)+b")
}
return(paste("$$",mod,"$$"))
}
if (process=="COGARCH"){
return(paste("$$","COGARCH(p,q)","$$"))
}
if (process=="CARMA"){
return(paste("$$","CARMA(p,q)","$$"))
}
if (process=="Levy process"){
return(paste("$$","dX_t = \\mu X_t \\; dt + \\sigma X_t \\; dW_t + X_t \\; dZ_t","$$"))
}
}
}
###Function to convert unit of measure of the estimates
changeBaseP <- function(param, StdErr, delta, original.data, paramName, modelName, newBase, allParam){
msg <- NULL
if (newBase == "delta")
return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
if(class(index(original.data))=="Date"){
seriesLength <- as.numeric(difftime(end(original.data),start(original.data)),units="days")
if (newBase == "Yearly") dt1 <- seriesLength/365/(length(original.data)-1)
if (newBase == "Semestral") dt1 <- seriesLength/182.50/(length(original.data)-1)
if (newBase == "Quarterly") dt1 <- seriesLength/120/(length(original.data)-1)
if (newBase == "Trimestral") dt1 <- seriesLength/90/(length(original.data)-1)
if (newBase == "Bimestral") dt1 <- seriesLength/60/(length(original.data)-1)
if (newBase == "Monthly") dt1 <- seriesLength/30/(length(original.data)-1)
if (newBase == "Weekly") dt1 <- seriesLength/7/(length(original.data)-1)
if (newBase == "Daily") dt1 <- seriesLength/(length(original.data)-1)
}
if(class(index(original.data))=="numeric"){
dt1 <- as.numeric(end(original.data) - start(original.data))/(length(index(original.data))-1)
msg <- "Parameters are in the same unit of measure of input data"
}
if (modelName %in% c("Brownian Motion","Bm","Geometric Brownian Motion","gBm")){
if(paramName == "mu") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "sigma") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
}
if (modelName %in% c("Ornstein-Uhlenbeck (OU)","OU")){
if(paramName == "theta") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "sigma") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
}
if (modelName %in% c("Vasicek model (VAS)","VAS")){
if(paramName == "theta1") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta2") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta3") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
}
if (modelName %in% c("Constant elasticity of variance (CEV)","CEV")){
if(paramName == "mu") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "sigma") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
if(paramName == "gamma") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Cox-Ingersoll-Ross (CIR)","CIR")){
if(paramName == "theta1") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta2") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta3") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
}
if (modelName %in% c("Chan-Karolyi-Longstaff-Sanders (CKLS)","CKLS")){
if(paramName == "theta1") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta2") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "theta3") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
if(paramName == "theta4") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Hyperbolic (Barndorff-Nielsen)", "Hyperbolic (Bibby and Sorensen)")){
if(paramName == "sigma") return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
if(paramName == "beta") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
if(paramName == "alpha") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
if(paramName == "mu") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
if(paramName == "delta") return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Constant Intensity")){
if(paramName == "lambda") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName %in% c("mu_jump", "sigma_jump", "a_jump", "b_jump")) return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Linear Intensity")){
if(paramName == "alpha") return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName == "beta") return(list("Estimate"= param*(delta/dt1)^2, "Std. Error"=StdErr*(delta/dt1)^2, "msg"=msg))
if(paramName %in% c("mu_jump", "sigma_jump", "a_jump", "b_jump")) return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Power Low Intensity")){
beta <- as.numeric(allParam["beta"])
if(paramName == "alpha") return(list("Estimate"= param*(delta/dt1)^(beta+1), "Std. Error"=StdErr*(delta/dt1)^(beta+1), "msg"=msg))
if(paramName %in% c("beta", "mu_jump", "sigma_jump", "a_jump", "b_jump")) return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Exponentially Decaying Intensity")){
if(paramName %in% c("alpha", "beta")) return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName %in% c("mu_jump", "sigma_jump", "a_jump", "b_jump")) return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Periodic Intensity")){
if(paramName %in% c("a", "b", "omega")) return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(paramName %in% c("phi", "mu_jump", "sigma_jump", "a_jump", "b_jump")) return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg))
}
if (modelName %in% c("Correlated Brownian Motion")){
if(startsWith(paramName, "mu")) return(list("Estimate"= param*delta/dt1, "Std. Error"=StdErr*delta/dt1, "msg"=msg))
if(startsWith(paramName, "s")) return(list("Estimate"= param*sqrt(delta/dt1), "Std. Error"=StdErr*sqrt(delta/dt1), "msg"=msg))
}
msg <- paste("No parameters conversion available for this model. Parameters have been obtained using delta = ", delta)
return(list("Estimate"= param, "Std. Error"=StdErr, "msg"=msg, "conversion"=FALSE))
}
###Function to manipulate digits
signifDigits <- function(value, sd){
if (is.na(sd) | sd=="NaN" | sd==0)
return (value)
else{
pow <- 10^(1-as.integer(log10(as.numeric(sd))))
return(round(as.numeric(value)*pow)/pow)
}
}
changeBase <- function(table, yuimaGUI, newBase, session = session, choicesUI, anchorId, alertId){
closeAlert(session, alertId)
shinyjs::toggle(id = choicesUI, condition = (class(index(yuimaGUI$model@[email protected]))=="Date"))
outputTable <- data.frame()
for (param in unique(colnames(table))){
temp <- changeBaseP(param = as.numeric(table["Estimate",param]), StdErr = as.numeric(table["Std. Error",param]), delta = yuimaGUI$model@sampling@delta, original.data = yuimaGUI$model@[email protected], paramName = param, modelName = yuimaGUI$info$modName, newBase = newBase, allParam = table["Estimate",])
outputTable["Estimate",param] <- as.character(signifDigits(temp[["Estimate"]],temp[["Std. Error"]]))
outputTable["Std. Error",param] <- as.character(signifDigits(temp[["Std. Error"]],temp[["Std. Error"]]))
}
colnames(outputTable) <- unique(colnames(table))
style <- "info"
msg <- NULL
if (any(outputTable["Std. Error",] %in% c(0, "NA", "NaN", "<NA>", NA, NaN))){
msg <- "The estimated model does not satisfy theoretical properties."
style <- "warning"
}
if (!is.null(temp$conversion)) if (temp$conversion==FALSE) shinyjs::hide(choicesUI)
if (yuimaGUI$info$class=="COGARCH") {
capture.output(test <- try(Diagnostic.Cogarch(yuimaGUI$model, param = as.list(coef(yuimaGUI$qmle)))))
if (class(test)=="try-error") createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("The estimated model does not satisfy theoretical properties.", temp$msg), style = "warning")
else if(test$stationary==FALSE | test$positivity==FALSE) createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("The estimated model does not satisfy theoretical properties.", temp$msg), style = "warning")
else createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste(msg, temp$msg), style = style)
}
else if (yuimaGUI$info$class=="CARMA") {
test <- try(Diagnostic.Carma(yuimaGUI$qmle))
if (class(test)=="try-error") createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("The estimated model does not satisfy theoretical properties.", temp$msg), style = "warning")
else if(test==FALSE) createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("The estimated model does not satisfy theoretical properties.", temp$msg), style = "warning")
else createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste(msg, temp$msg), style = style)
}
else if (!is.null(temp$msg) | !is.null(msg)) createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste(msg, temp$msg), style = style)
return(outputTable)
}
qmleGUI <- function(upper, lower, ...){
if(length(upper)!=0 & length(lower)!=0)
return (qmle(upper = upper, lower = lower, ...))
if(length(upper)!=0 & length(lower)==0)
return (qmle(upper = upper, ...))
if(length(upper)==0 & length(lower)!=0)
return (qmle(lower = lower, ...))
if(length(upper)==0 & length(lower)==0)
return (qmle(...))
}
clearNA <- function(List){
for (i in names(List))
if (is.na(List[[i]]))
List[[i]] <- NULL
return (List)
}
addModel <- function(timeout = Inf, modName, multi = FALSE, intensity_levy, modClass, AR_C, MA_C, jumps, symbName, data, toLog, delta, start, startMin, startMax, trials, seed, method="BFGS", fixed = list(), lower, upper, joint=FALSE, aggregation=TRUE, threshold=NULL, session, anchorId, alertId){
info <- list(
symb = names(data),
class = modClass,
modName = modName,
AR = AR_C,
MA = MA_C,
jumps = ifelse(is.null(jumps),NA,jumps),
method=method,
delta = delta,
toLog = toLog,
start = start,
startMin = startMin,
startMax = startMax,
trials = trials,
seed = seed,
fixed = fixed,
lower = lower,
upper = upper,
joint = joint,
aggregation = aggregation,
threshold = threshold
)
if(!is.na(seed)) set.seed(seed)
if(is.na(seed)) set.seed(NULL)
start <- clearNA(start)
fixed <- clearNA(fixed)
lower <- clearNA(lower)
upper <- clearNA(upper)
for (i in 1:length(toLog)) if(toLog[i]==TRUE) {
tmp <- try(log(data[,i]))
if(class(data)!="try-error")
data[,i] <- tmp
else {
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("Cannot convert series ", symbName, "to log. Try to use 'Advanced Settings' and customize estimation.", sep = ""), style = "error")
return()
}
}
if(modClass=='Point Process'){
model <- setModelByName(name = modName, dimension = ncol(data), intensity = intensity_levy, jumps = jumps, MA_C = MA_C, AR_C = AR_C)
t1 <- tail(time(data),n=1)
t0 <- time(data)[1]
if(!is.numeric(t0) | !is.numeric(t1)){
t0 <- 0
t1 <- as.numeric(t1-t0)/365
}
samp <- setSampling(t0, t1, n = as.integer(as.numeric(t1-t0)/delta)+1)
colnames(data) <- model@[email protected]
model <- DataPPR(CountVar = data, yuimaPPR = model, samp = samp)
} else {
model <- try(setYuima(data = setDataGUI(data, delta = delta), model=setModelByName(name = modName, dimension = ncol(data), intensity = intensity_levy, jumps = jumps, MA_C = MA_C, AR_C = AR_C)))
}
if (class(model)=="try-error"){
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = "Unable to construct a synchronous grid for the data provided", style = "error")
return()
}
#index(model@[email protected]) <- index(na.omit(data))
parameters <- getAllParams(model, modClass)
if (modClass == "Fractional process"){
QMLEtemp <- try(mmfrac(model))
if(class(QMLEtemp)!="try-error") {
estimates <- QMLEtemp[[1]]
dev <- diag(QMLEtemp[[2]])
QMLE <- rbind(estimates, dev)
col <- gsub(colnames(QMLE), pattern = "\\(", replacement = "")
col <- gsub(col, pattern = "\\)", replacement = "")
colnames(QMLE) <- col
rownames(QMLE) <- c("Estimate", "Std. Error")
}
}
else if (modClass=="CARMA") {
if (all(parameters %in% c(names(start),names(fixed))))
QMLE <- try(qmleGUI(model, start = start, method = method, lower = lower, upper = upper))
else {
miss <- parameters[!(parameters %in% c(names(start),names(fixed)))]
m2logL_prec <- NA
na_prec <- NA
withProgress(message = 'Step: ', value = 0, {
for(iter in 1:trials){
setTimeLimit(cpu = timeout, transient = TRUE)
incProgress(1/trials, detail = paste(iter,"(/", trials ,")"))
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtemp <- try(qmleGUI(model, start = start, method = method, lower = lower, upper = upper))
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"])))
break
}
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtemp)@m2logL
coefTable <- summary(QMLEtemp)@coef
for (param in rownames(coefTable))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtemp <- try(qmleGUI(model, start = start, method = method, lower = lower, upper = upper))
if (class(QMLEtemp)=="try-error") break
else if(summary(QMLEtemp)@m2logL>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtemp)!="try-error"){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtemp)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtemp)@m2logL
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLE <- QMLEtemp
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
})
}
}
else if (modClass=="COGARCH") {
if (all(parameters %in% c(names(start),names(fixed))))
QMLE <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #REMOVE# joint = joint, aggregation = aggregation,
threshold = threshold, grideq = TRUE, rcpp = TRUE))
else {
miss <- parameters[!(parameters %in% c(names(start),names(fixed)))]
m2logL_prec <- NA
na_prec <- NA
withProgress(message = 'Step: ', value = 0, {
for(iter in 1:trials){
setTimeLimit(cpu = timeout, transient = TRUE)
incProgress(1/trials, detail = paste(iter,"(/", trials ,")"))
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold, grideq = TRUE, rcpp = TRUE))
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"])))
break
}
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtemp)@objFunVal
coefTable <- summary(QMLEtemp)@coef
for (param in rownames(coefTable))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold, grideq = TRUE, rcpp = TRUE))
if (class(QMLEtemp)=="try-error") break
else if(summary(QMLEtemp)@objFunVal>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtemp)!="try-error"){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@objFunVal
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtemp)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@objFunVal
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtemp)@objFunVal
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLE <- QMLEtemp
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
})
}
}
else if (modClass == "Compound Poisson") {
if (all(parameters %in% c(names(start),names(fixed))))
QMLE <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #REMOVE# joint = joint, aggregation = aggregation,
threshold = threshold))
else {
miss <- parameters[!(parameters %in% c(names(start),names(fixed)))]
m2logL_prec <- NA
na_prec <- NA
withProgress(message = 'Step: ', value = 0, {
for(iter in 1:trials){
setTimeLimit(cpu = timeout, transient = TRUE)
incProgress(1/trials, detail = paste(iter,"(/", trials ,")"))
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold))
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"])))
break
}
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtemp)@m2logL
coefTable <- summary(QMLEtemp)@coef
for (param in names(start))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold))
if (class(QMLEtemp)=="try-error") break
else if (summary(QMLEtemp)@m2logL>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtemp)!="try-error"){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtemp)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtemp)@m2logL
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLE <- QMLEtemp
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
})
}
}
else if (modClass == "Levy process") {
if (all(parameters %in% c(names(start),names(fixed))))
QMLE <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #REMOVE# joint = joint, aggregation = aggregation,
threshold = threshold))
else {
miss <- parameters[!(parameters %in% c(names(start),names(fixed)))]
m2logL_prec <- NA
na_prec <- NA
withProgress(message = 'Step: ', value = 0, {
for(iter in 1:trials){
setTimeLimit(cpu = timeout, transient = TRUE)
incProgress(1/trials, detail = paste(iter,"(/", trials ,")"))
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold))
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"])))
break
}
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtemp)@m2logL
coefTable <- summary(QMLEtemp)@coef
for (param in names(start))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold))
if (class(QMLEtemp)=="try-error") break
else if (summary(QMLEtemp)@m2logL>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtemp)!="try-error"){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtemp)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtemp)@m2logL
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLE <- QMLEtemp
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
})
}
}
else {
if (all(parameters %in% c(names(start),names(fixed))))
QMLE <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #REMOVE# joint = joint, aggregation = aggregation,
threshold = threshold, rcpp = TRUE))
else {
miss <- parameters[!(parameters %in% c(names(start),names(fixed)))]
m2logL_prec <- NA
na_prec <- NA
withProgress(message = 'Step: ', value = 0, {
for(iter in 1:trials){
setTimeLimit(cpu = timeout, transient = TRUE)
incProgress(1/trials, detail = paste(iter,"(/", trials ,")"))
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold, rcpp = TRUE))
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"])))
break
}
if (class(QMLEtemp)!="try-error") if (all(!is.na(summary(QMLEtemp)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtemp)@m2logL
coefTable <- summary(QMLEtemp)@coef
for (param in names(start))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtemp <- try(qmle(model, start = start, fixed = fixed, method = method, lower = lower, upper = upper, #joint = joint, aggregation = aggregation,
threshold = threshold, rcpp = TRUE))
if (class(QMLEtemp)=="try-error") break
else if (summary(QMLEtemp)@m2logL>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtemp)!="try-error"){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtemp)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLE <- QMLEtemp
m2logL_prec <- summary(QMLE)@m2logL
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtemp)@m2logL
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLE <- QMLEtemp
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
})
}
}
if (!exists("QMLE")){
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("Unable to estimate ", modName," on ", symbName, ". Try to use 'Advanced Settings' and customize estimation.", sep = ""), style = "error")
return()
}
if(multi==FALSE)
yuimaGUIdata$model[[symbName]][[ifelse(is.null(length(yuimaGUIdata$model[[symbName]])),1,length(yuimaGUIdata$model[[symbName]])+1)]] <<- list(
model = model,
qmle = QMLE,
aic = ifelse(!(modClass %in% c("CARMA","COGARCH","Fractional process")), AIC(QMLE), NA),
bic = ifelse(!(modClass %in% c("CARMA","COGARCH","Fractional process")), BIC(QMLE), NA),
info = info
)
else
yuimaGUIdata$multimodel[[symbName]][[ifelse(is.null(length(yuimaGUIdata$multimodel[[symbName]])),1,length(yuimaGUIdata$multimodel[[symbName]])+1)]] <<- list(
model = model,
qmle = QMLE,
aic = ifelse(!(modClass %in% c("CARMA","COGARCH","Fractional process")), AIC(QMLE), NA),
bic = ifelse(!(modClass %in% c("CARMA","COGARCH","Fractional process")), BIC(QMLE), NA),
info = info
)
}
addCPoint <- function(modelName, symb, from, to, delta, toLog, start, startMin, startMax, method, trials, seed, lower, upper, fracL, fracR){
series <- getData(symb)
if(class(index(series)[1])=="Date") series <- window(series, start = as.Date(from), end = as.Date(to))
else series <- window(series, start = as.numeric(from), end = as.numeric(to))
mod <- setModelByName(name = modelName)
if(!is.na(seed)) set.seed(seed)
if(is.na(seed)) set.seed(NULL)
start <- clearNA(start)
lower <- clearNA(lower)
upper <- clearNA(upper)
if(toLog==TRUE) series <- try(log(series))
if(class(series)=="try-error") stop()
info <- list(
symb = symb,
seed = seed,
model = modelName,
toLog = toLog,
trials = trials,
method = method
)
yuima <- setYuima(data = setDataGUI(series, delta = delta), model = mod)
t0 <- start(yuima@[email protected][[1]])
par <- getAllParams(mod, "Diffusion process")
miss <- par[!(par %in% names(start))]
m2logL_prec <- NA
na_prec <- NA
qmleL <- function(yuima, t, start, method, lower , upper , rcpp){
yuima@[email protected][[1]] <- window(yuima@[email protected][[1]], end = t)
qmle(yuima = yuima, start = start, method = method, upper = upper, lower = lower, rcpp = rcpp)
}
qmleR <- function(yuima, t, start, method, lower , upper , rcpp){
yuima@[email protected][[1]] <- window(yuima@[email protected][[1]], start = t)
qmle(yuima = yuima, start = start, method = method, upper = upper, lower = lower, rcpp = rcpp)
}
for(iter in 1:trials){
for(j in 1:3){
for (i in miss)
start[[i]] <- runif(1, min = max(lower[[i]],startMin[[i]], na.rm = TRUE), max = min(upper[[i]],startMax[[i]],na.rm = TRUE))
QMLEtempL <- try(qmleL(yuima = yuima, t = t0 + fracL*length(series)*delta, start = start, method=method, lower = lower, upper = upper, rcpp = TRUE))
if (class(QMLEtempL)!="try-error") if (all(!is.na(summary(QMLEtempL)@coef[,"Estimate"])))
break
}
if (class(QMLEtempL)!="try-error") if (all(!is.na(summary(QMLEtempL)@coef[,"Estimate"]))){
repeat{
m2logL <- summary(QMLEtempL)@m2logL
coefTable <- summary(QMLEtempL)@coef
for (param in names(start))
start[[param]] <- as.numeric(coefTable[param,"Estimate"])
QMLEtempL <- try(qmleL(yuima = yuima, t = t0 + fracL*length(series)*delta, start = start, method=method, lower = lower, upper = upper, rcpp = TRUE))
if (class(QMLEtempL)=="try-error") break
else if (summary(QMLEtempL)@m2logL>=m2logL*abs(sign(m2logL)-0.001)) break
}
if(is.na(m2logL_prec) & class(QMLEtempL)!="try-error"){
QMLEL <- QMLEtempL
m2logL_prec <- summary(QMLEL)@m2logL
na_prec <- sum(is.na(coefTable))
}
else if (class(QMLEtempL)!="try-error"){
if (sum(is.na(coefTable)) < na_prec){
QMLEL <- QMLEtempL
m2logL_prec <- summary(QMLEL)@m2logL
na_prec <- sum(is.na(coefTable))
}
else {
test <- summary(QMLEtempL)@m2logL
if(test < m2logL_prec & sum(is.na(coefTable))==na_prec){
QMLEL <- QMLEtempL
m2logL_prec <- test
na_prec <- sum(is.na(coefTable))
}
}
}
}
}
if (!exists("QMLEL")) stop()
tmpL <- QMLEL
tmpR <- try(qmleR(yuima = yuima, t = t0 + fracR*length(series)*delta, start = as.list(coef(tmpL)), method=method, lower = lower, upper = upper, rcpp = TRUE))
if (class(tmpR)=="try-error") stop()
cp_prec <- try(CPoint(yuima = yuima, param1=coef(tmpL), param2=coef(tmpR)))
if(class(cp_prec)=="try-error") stop()
diff_prec <- delta*nrow(series)
repeat{
tmpL <- try(qmleL(yuima, start=as.list(coef(tmpL)), t = cp_prec$tau, lower=lower, upper = upper, method=method, rcpp = TRUE))
if(class(tmpL)=="try-error") stop()
tmpR <- try(qmleR(yuima, start=as.list(coef(tmpR)), t = cp_prec$tau, lower=lower, upper = upper, method=method, rcpp = TRUE))
if(class(tmpR)=="try-error") stop()
cp <- try(CPoint(yuima = yuima, param1=coef(tmpL), param2=coef(tmpR)))
if(class(cp)=="try-error") stop()
if (abs(cp$tau - cp_prec$tau)<delta) break
else if (abs(cp$tau - cp_prec$tau)>=diff_prec) stop()
else {
cp_prec <- cp
diff_prec <- abs(cp$tau - cp_prec$tau)
}
}
i <- 1
symb_id <- symb
repeat {
if(symb_id %in% names(yuimaGUIdata$cpYuima)){
symb_id <- paste(symb, i)
i <- i+1
} else break
}
yuimaGUIdata$cpYuima[[symb_id]] <<- list(tau = index(series)[as.integer((cp$tau-t0)/delta)], info = info, series = series, qmleR = tmpR, qmleL = tmpL)
}
getModelNames <- function(){
return(isolate({yuimaGUItable$model}))
}
getModel <- function(symb){
return(isolate({yuimaGUIdata$model[[symb]]}))
}
delModel <- function(symb, n=1){
for(i in length(symb):1){
yuimaGUIdata$model[[symb[i]]][as.numeric(n[i])] <<- NULL
if (length(yuimaGUIdata$model[[symb[i]]])==0)
yuimaGUIdata$model[[symb[i]]] <<- NULL
}
}
delMultiModel <- function(symb, n=1){
for(i in length(symb):1){
yuimaGUIdata$multimodel[[symb[i]]][as.numeric(n[i])] <<- NULL
if (length(yuimaGUIdata$multimodel[[symb[i]]])==0)
yuimaGUIdata$multimodel[[symb[i]]] <<- NULL
}
}
simulateGUI <- function(symbName, modelYuimaGUI, xinit, nsim, nstep, simulate.from, simulate.to, saveTraj, space.discretized, method, session, anchorId, alertId = NULL, true.parameter = NULL){
modelYuima <- modelYuimaGUI$model
model <- modelYuima@model
if(is.null(modelYuimaGUI$info$toLog)) toLog <- FALSE else toLog <- modelYuimaGUI$info$toLog
if(simulate.from >= simulate.to){
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("Unable to simulate ", symbName," by ", modelYuimaGUI$info$modName, ": ending time before starting time.", sep = ""), style = "danger")
return()
}
if(!is.null(names(xinit))) seriesnames <- names(xinit) else seriesnames <- [email protected]
xinit <- as.numeric(xinit)
xinit[toLog==TRUE] <- log(xinit[toLog==TRUE])
if(is.null(true.parameter)){
convert <- TRUE
if (modelYuimaGUI$info$class=="Fractional process") true.parameter <- as.list(modelYuimaGUI$qmle["Estimate",])
else true.parameter <- as.list(modelYuimaGUI$qmle@coef)
data <- modelYuima@[email protected]
data_index <- index(data)
real_delta <- as.numeric(last(data_index)-data_index[1])/(length(data_index)-1)
used_delta <- modelYuima@sampling@delta
if(is.numeric(data_index)){
Initial <- round(digits = 0, simulate.from/real_delta)*used_delta
Terminal <- round(digits = 0, simulate.to/real_delta)*used_delta
} else {
Initial <- round(digits = 0, as.numeric(simulate.from-start(data))/real_delta)*used_delta
Terminal <- round(digits = 0, as.numeric(simulate.to-start(data))/real_delta)*used_delta
}
if (modelYuimaGUI$info$class %in% c("COGARCH", "CARMA") | !is.numeric(nstep))
nstep <- (Terminal-Initial)/used_delta
sampling <- setSampling(Initial = Initial, Terminal = Terminal, n = nstep)
} else {
convert <- FALSE
sampling <- setSampling(Initial = simulate.from, Terminal = simulate.to, n = nstep)
}
if(nsim*sampling@n*length(xinit) > 1000*252*2) saveTraj <- FALSE
is.valid <- TRUE
if (modelYuimaGUI$info$class=="COGARCH") {
noise <- cogarchNoise(yuima = modelYuima, param = true.parameter)
xinit <- c(xinit, as.numeric(last(yuima:::onezoo(noise$Cogarch)))[-1])
increments <- noise$incr.L
}
if (modelYuimaGUI$info$class=="CARMA") {
increments <- CarmaNoise(yuima = modelYuima, param = true.parameter)
x <- try(yuima::simulate(object = model, increment.W = t(increments), xinit = as.numeric(first(modelYuima@[email protected])), true.parameter = true.parameter, sampling = setSampling(Initial = modelYuima@sampling@Initial, delta = used_delta, n = length(increments)), space.discretized = space.discretized, method = method))
if (class(x)=="try-error"){
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = paste("Unable to simulate ", symbName," by ", modelYuimaGUI$info$modName, ". Probably something wrong with the estimation of this model", sep = ""), style = "danger")
return()
}
xinit <- c(xinit, as.numeric(last(yuima:::onezoo(x)))[-1])
}
if (modelYuimaGUI$info$class=="Fractional process") if (true.parameter[["hurst"]]>=1 | true.parameter[["hurst"]]<=0) {
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = "Hurst coefficient must greater than 0 and less than 1", style = "danger")
return()
}
withProgress(message = 'Simulating: ', value = 0, {
for (i in 1:nsim){
incProgress(1/nsim, detail = paste("Simulating:",i,"(/",nsim,")"))
if (modelYuimaGUI$info$class=="COGARCH")
simulation <- try(yuima::simulate(object = model, increment.L = sample(x = increments, size = sampling@n, replace = TRUE), xinit = xinit, true.parameter = true.parameter, sampling = sampling, space.discretized = space.discretized, method = method))
else if (modelYuimaGUI$info$class=="CARMA")
simulation <- try(yuima::simulate(object = model, increment.W = t(sample(x = increments, size = sampling@n, replace = TRUE)), xinit = xinit, true.parameter = true.parameter, sampling = sampling, space.discretized = space.discretized, method = method))
else if (modelYuimaGUI$info$class=="Fractional process")
simulation <- try(yuima::simulate(object = model, xinit = xinit, true.parameter = true.parameter, hurst = true.parameter[["hurst"]], sampling = sampling, space.discretized = space.discretized, method = method))
else if (modelYuimaGUI$info$class=="Point Process")
simulation <- try(yuima::simulate(object = modelYuima, xinit = xinit, true.parameter = true.parameter, sampling = sampling, space.discretized = space.discretized, method = method))
else
simulation <- try(yuima::simulate(object = model, xinit = xinit, true.parameter = true.parameter, sampling = sampling, space.discretized = space.discretized, method = method))
if (class(simulation)=="try-error"){
is.valid <- FALSE
break()
}
else {
dimension <- length(simulation@[email protected])
if (modelYuimaGUI$info$class %in% c("CARMA","COGARCH")) dimension <- dimension - 2
if (saveTraj==TRUE){
x <- do.call(merge,simulation@[email protected])
if(i==1) {
timeindex <- index(x)
x <- as.matrix(x)
trajectory <- matrix(nrow = nrow(x), ncol = nsim*dimension)
colnames(trajectory) <- seq(1:ncol(trajectory))
hist <- NA
}
else
x <- as.matrix(x)
x[,toLog==TRUE] <- exp(x[,toLog==TRUE])
if(any( is.na(x) | !is.finite(x) )){
is.valid <- FALSE
break()
}
colindex <- seq(1+(i-1)*dimension, i*dimension)
trajectory[,colindex] <- x[,1:dimension]
colnames(trajectory)[colindex] <- paste(seriesnames[1:dimension], i, sep = "_sim")
} else {
x <- do.call(c, lapply(simulation@[email protected], FUN = function(x) as.numeric(last(x))))
if(i==1) {
trajectory <- NA
hist <- matrix(nrow = dimension, ncol = nsim, dimnames = list(seriesnames[1:dimension]))
}
hist[,i] <- x
}
}
}
})
if (!is.valid){
if(modelYuimaGUI$info$class %in% c("CARMA","COGARCH")) msg <- paste("Unable to simulate ", symbName," by ", modelYuimaGUI$info$modName, ". Probably something wrong with the estimation of this model", sep = "")
else msg <- paste("Unable to simulate", symbName,"by", modelYuimaGUI$info$modName)
createAlert(session = session, anchorId = anchorId, alertId = alertId, content = msg, style = "danger")
return()
}
if(saveTraj==TRUE){
trajectory <- zoo(trajectory, order.by = timeindex)
if(convert==TRUE){
if(is.numeric(data_index))
index(trajectory) <- as.numeric(timeindex/used_delta*real_delta)
else
index(trajectory) <- as.POSIXct(24*60*60*(timeindex-timeindex[1])/used_delta*real_delta, origin = simulate.from)
}
}
return(list(hist=hist, trajectory=trajectory, nstep = sampling@n[1], simulate.from = simulate.from, simulate.to = simulate.to, delta = sampling@delta))
}
addSimulation <- function(modelYuimaGUI, symbName, xinit, nsim, nstep, simulate.from, simulate.to, saveTraj, seed, sampling, true.parameter = NULL, space.discretized = FALSE, method = "euler", session, anchorId, is.multi = FALSE){
if(!is.na(seed)) set.seed(seed)
if(is.na(seed)) set.seed(NULL)
sim <- simulateGUI(symbName = symbName, modelYuimaGUI = modelYuimaGUI, xinit = xinit, nsim = nsim, nstep = nstep, simulate.from = simulate.from, simulate.to = simulate.to, saveTraj = saveTraj, space.discretized = space.discretized, method = method, session = session, anchorId = anchorId, true.parameter = true.parameter)
if(!is.null(sim)){
if(is.multi==FALSE)
yuimaGUIdata$simulation[[symbName]][[ifelse(is.null(length(yuimaGUIdata$simulation[[symbName]])),1,length(yuimaGUIdata$simulation[[symbName]])+1)]] <<- list(
model = modelYuimaGUI,
trajectory = sim$trajectory,
hist = sim$hist,
info = list(nsim = nsim, nstep = sim$nstep, simulate.from = sim$simulate.from, simulate.to = sim$simulate.to, delta = sim$delta)
)
else
yuimaGUIdata$multisimulation[[symbName]][[ifelse(is.null(length(yuimaGUIdata$multisimulation[[symbName]])),1,length(yuimaGUIdata$multisimulation[[symbName]])+1)]] <<- list(
model = modelYuimaGUI,
trajectory = sim$trajectory,
hist = sim$hist,
info = list(nsim = nsim, nstep = sim$nstep, simulate.from = sim$simulate.from, simulate.to = sim$simulate.to, delta = sim$delta)
)
}
}
delSimulation <- function(symb, n=1, multi=FALSE){
if(multi==FALSE){
for(i in length(symb):1){
yuimaGUIdata$simulation[[symb[i]]][as.numeric(n[i])] <<- NULL
if (length(yuimaGUIdata$simulation[[symb[i]]])==0)
yuimaGUIdata$simulation[[symb[i]]] <<- NULL
}
}
else {
for(i in length(symb):1){
yuimaGUIdata$multisimulation[[symb[i]]][as.numeric(n[i])] <<- NULL
if (length(yuimaGUIdata$multisimulation[[symb[i]]])==0)
yuimaGUIdata$multisimulation[[symb[i]]] <<- NULL
}
}
}
profit_distribution <- function(nOpt, nAss, type, strike, priceAtMaturity, optMarketPrice, assMarketPrice, percCostAss, minCostAss, lotCostOpt, lotMultiplier, shortCostPerYear, t0=Sys.Date(), maturity){
if (nOpt==0 & nAss==0)
return(0)
if (type=="call"){
payoff <- pmax(priceAtMaturity-strike,0)
return(nOpt*(payoff-optMarketPrice)-
nAss*(priceAtMaturity-assMarketPrice)-
pmax(nAss*assMarketPrice*percCostAss, minCostAss)*ifelse(nAss!=0,1,0)-
pmax(nAss*priceAtMaturity*percCostAss, minCostAss)*ifelse(nAss!=0,1,0)-
nOpt/lotMultiplier*lotCostOpt-
shortCostPerYear*(nAss*assMarketPrice)*as.numeric(as.Date(maturity)-as.Date(t0))/365
)
}
if (type=="put"){
payoff <- pmax(strike-priceAtMaturity,0)
return(nOpt*(payoff-optMarketPrice)+
nAss*(priceAtMaturity-assMarketPrice)-
pmax(nAss*assMarketPrice*percCostAss, minCostAss)*ifelse(nAss!=0,1,0)-
pmax(nAss*priceAtMaturity*percCostAss, minCostAss)*ifelse(nAss!=0,1,0)-
nOpt/lotMultiplier*lotCostOpt
)
}
}
addHedging <- function(modelYuimaGUI, symbName, info, xinit, nsim, nstep, simulate.from, simulate.to, session, anchorId){
alertId <- "addHedging_alert"
closeAlert(session, alertId)
sim <- simulateGUI(symbName = symbName, modelYuimaGUI = modelYuimaGUI, xinit = xinit, simulate.from = simulate.from, simulate.to = simulate.to, nstep = nstep, nsim = nsim, saveTraj = FALSE, space.discretized = FALSE, method = "euler", session = session, anchorId = anchorId, alertId = alertId)
if(!is.null(sim)){
today <- simulate.from
profits <- profit_distribution(nOpt=1*info$optLotMult,
nAss=0,
type=info$type,
strike=info$strike,
priceAtMaturity=sim$hist,
optMarketPrice=info$optPrice,
assMarketPrice=info$assPrice,
percCostAss=info$assPercCost,
minCostAss=info$assMinCost,
lotCostOpt=info$optLotCost,
lotMultiplier=info$optLotMult,
shortCostPerYear=info$assRateShortSelling,
t0=today,
maturity=info$maturity)
info$profit <- mean(profits)/(info$optLotMult*info$optPrice+info$optLotCost)
info$stdErr <- sd(profits)/sqrt(length(profits))/(info$optLotMult*info$optPrice+info$optLotCost)
info$nsim <- nsim
info$buy <- ifelse(info$type=="call",NA,0)
info$sell <- ifelse(info$type=="put",NA,0)
info$LotsToBuy <- 1
info$today <- today
yuimaGUIdata$hedging[[length(yuimaGUIdata$hedging)+1]] <<- list(
model = modelYuimaGUI,
hist = sim$hist,
info = info,
symb = symbName
)
}
}
delHedging <- function(n){
yuimaGUIdata$hedging <<- yuimaGUIdata$hedging[-n]
}
MYdist <- function(object, percentage = TRUE){
l <- length(colnames(object))
d <- matrix(ncol = l, nrow = l)
f <- function(x, dens){
res <- c()
getY <- function(xi){
i <- which(dens$x==xi)
if (length(i)!=0) return(dens$y[i])
else {
i_x1 <- which.min(abs(dens$x-xi))
i_x2 <- min(i_x1+1,length(dens$x))
return(0.5*(dens$y[i_x1]+dens$y[i_x2]))
}
}
res <- sapply(X = x, FUN = getY)
return(res)
}
withProgress(message = 'Clustering: ', value = 0, {
k <- 1
for(i in 1:l){
#delta_i <- as.numeric(abs(mean(diff(index(object)[!is.na(object[,i])]), na.rm = TRUE)))
if (percentage == TRUE) data_i <- as.vector(na.omit(Delt(object[,i])))
else data_i <- as.vector(na.omit(diff(object[,i])))
data_i <- data_i[data_i!="Inf"]
dens1 <- density(data_i, na.rm = TRUE)#/sqrt(delta_i)+mean(data_i, na.rm = TRUE)*(1/delta_i-1/sqrt(delta_i)), na.rm = TRUE)
for(j in i:l)
if (i!=j){
incProgress(2/(l*(l-1)), detail = paste(k,"(/", l*(l-1)/2 ,")"))
#delta_j <- as.numeric(abs(mean(diff(index(object)[!is.na(object[,j])]), na.rm = TRUE)))
if (percentage == TRUE) data_j <- as.vector(na.omit(Delt(object[,j])))
else data_j <- as.vector(na.omit(diff(object[,j])))
data_j <- data_j[data_j!="Inf"]
dens2 <- density(data_j, na.rm = TRUE)#/sqrt(delta_j)+mean(data_j, na.rm = TRUE)*(1/delta_j-1/sqrt(delta_j)), na.rm = TRUE)
f_dist <- function(x) {0.5*abs(f(x,dens1)-f(x,dens2))}
dist <- try(integrate(f_dist, lower = min(dens1$x[1],dens2$x[1]), upper = max(last(dens1$x), last(dens2$x)), subdivisions = 100000, rel.tol = 0.01))
d[j,i] <- min(1, ifelse(class(dist)=="try-error", 1, dist$value))
k <- k + 1
}
}
})
rownames(d) <- colnames(object)
colnames(d) <- colnames(object)
return(as.dist(d))
}
CPanalysis <- function(x, method = c("KSdiff", "KSperc"), pvalue = 0.01, symb){
if (pvalue > 0.1){
pvalue <- 0.1
warning("pvalue re-defined: 0.1")
}
if(method=="KSdiff" | method=="KSperc"){
x_incr <- switch (method,
"KSdiff" = na.omit(diff(x)),
"KSperc" = na.omit(Delt(x)))
index_x_incr <- index(x_incr)
x_incr_num <- as.numeric(x_incr)
tau <- NULL
p.value <- NULL
getCPoint <- function(n0, nTot){
if(abs(nTot-n0)<10) return()
grid <- seq(from = n0, to=(nTot-1), by = as.integer(1+(nTot-n0)/100))
ks<-matrix(nrow = length(grid), ncol = 2, dimnames = list(NULL, c("index", "pvalue")))
j <- 1
for (i in grid){
ks[j,"index"] <- i
ks[j, "pvalue"]<- suppressWarnings(ks.test(x_incr_num[n0:i],x_incr_num[(i+1):nTot])$p.value)
j <- j+1
}
if(min(ks[,"pvalue"], na.rm=TRUE) > pvalue) return()
else {
new_n0 <- as.integer(ks[which.min(ks[,"pvalue"]), "index"])
env <- environment(getCPoint)
assign(x = "tau", value = append(x = get("tau", envir = env), values = index_x_incr[new_n0]), envir = env)
assign(x = "p.value", value = append(x = get("p.value", envir = env), values = as.numeric(ks[which(ks[,"index"]==new_n0), "pvalue"])), envir = env)
getCPoint(n0 = n0, nTot = new_n0)
getCPoint(n0 = new_n0+1, nTot = nTot)
}
}
getCPoint(n0 = 1, nTot = length(x_incr_num))
if (is.null(tau)){
tau <- NA
p.value <- NA
}
return (list(tau=tau,pvalue=p.value, method=method, series = x, symb = symb))
}
}
addCPoint_distribution <- function(symb, method = c("KSdiff", "KSperc"), pvalue = 0.01){
temp <- try(CPanalysis(x=getData(symb), method = method, pvalue = pvalue, symb = symb))
if (class(temp)!="try-error") {
i <- 1
symb_id <- symb
repeat {
if(symb_id %in% names(yuimaGUIdata$cp)){
symb_id <- paste(symb, i)
i <- i+1
} else break
}
yuimaGUIdata$cp[[symb_id]] <<- temp
return(list(error=NULL))
} else return(list(error=symb))
}
###Save all available data
saveData <- function() {
dataDownload_series <- reactive({
for (symb in names(yuimaGUIdata$series)){
data <- getData(symb)
if(is.numeric(index(data))) {
if (!exists("data_num", inherits = FALSE)) data_num <- data
else data_num <- merge(data_num, data)
}
else {
if (!exists("data_date", inherits = FALSE)) data_date <- data
else data_date <- merge(data_date, data)
}
}
if (exists("data_date") & !exists("data_num")) return(as.data.frame(data_date[order(index(data_date)), , drop = FALSE]))
if (!exists("data_date") & exists("data_num")) return(as.data.frame(data_num[order(index(data_num)), , drop = FALSE]))
if (exists("data_date") & exists("data_num")) return(rbind.fill(as.data.frame(data_num[order(index(data_num)), , drop = FALSE]), as.data.frame(data_date[order(index(data_date)), , drop = FALSE])))
})
downloadHandler(
filename = "yuimaGUIdata.txt",
content = function(file) {
write.table(dataDownload_series(), file, quote = FALSE)
}
)
}
jumps_shortcut <- function(class, jumps){
switch(class, "Diffusion process" = NA, "Fractional process" = NA,"Compound Poisson" = jumps, "COGARCH"=NA, "CARMA" = NA, "Levy process" = jumps)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/functions.R |
output$video_intro <- renderUI({
HTML('<iframe width="90%" height="250px" src="https://www.youtube.com/embed/XX_bmCrI_gc?rel=0" frameborder="0" allowfullscreen></iframe>')
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/home/home.R |
###Download data and display message
observeEvent(input$finDataGo, priority = 1, {
if (input$symb!=""){
closeAlert(session, "finDataAlert_err")
closeAlert(session, "finDataAlert_warn")
closeAlert(session, "finDataAlert_succ")
symb <- unlist(strsplit(input$symb, split = "[, ]+" , fixed = FALSE))
err <- c()
already_in <- c()
withProgress(message = 'Loading: ', value = 0, {
for (i in symb){
incProgress(1/length(symb), detail = i)
x <- try(window(getSymbols(i, src = input$sources ,auto.assign = FALSE), start = input$dR[1], end = input$dR[2]))
if (class(x)[1]=="try-error")
err <- cbind(err,i)
else {
info <- addData(x, typeIndex = "%Y-%m-%d")
err <- c(err, info$err)
already_in <- c(already_in, info$already_in)
}
}
})
if(!is.null(err))
createAlert(session = session, anchorId = "finDataAlert", alertId = "finDataAlert_err", content = paste("Unable to load following symbols:", paste(err,collapse = " ")), style = "error")
if(!is.null(already_in))
createAlert(session = session, anchorId = "finDataAlert", alertId = "finDataAlert_warn", content = paste("WARNING! Following symbols already loaded:", paste(already_in,collapse = " ")), style = "warning")
if(is.null(err) & is.null(already_in))
createAlert(session = session, anchorId = "finDataAlert", alertId = "finDataAlert_succ", content = paste("All symbols loaded successfully"), style = "success")
}
})
###Display available data
output$database1 <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)!=0)
return(yuimaGUItable$series)
})
###Interactive range of finDataPlot chart
range_finDataPlot <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$finDataPlot_brush)){
range_finDataPlot$x <- c(as.Date(input$finDataPlot_brush$xmin), as.Date(input$finDataPlot_brush$xmax))
range_finDataPlot$y <- c(input$finDataPlot_brush$ymin, input$finDataPlot_brush$ymax)
}
})
observeEvent(input$finDataPlot_dbclick,{
range_finDataPlot$x <- c(NULL, NULL)
range_finDataPlot$y <- c(NULL, NULL)
})
###Display chart of last clicked symbol
observeEvent(input$database1_rows_selected, priority = -1, {
symb <- yuimaGUItable$series$Symb[tail(input$database1_rows_selected,1)]
shinyjs::show("finDataPlot")
shinyjs::show("scale_finDataPlot")
valid_data <- NULL
range_finDataPlot$x <- c(NULL, NULL)
output$finDataPlot <- renderPlot({
if (length(yuimaGUItable$series)==0){
shinyjs::hide("finDataPlot")
shinyjs::hide("scale_finDataPlot")
}
else{
if (!(symb %in% as.character(yuimaGUItable$series[,"Symb"]))){
shinyjs::hide("finDataPlot")
shinyjs::hide("scale_finDataPlot")
}
else {
data <- window(getData(symb), start = range_finDataPlot$x[1], end = range_finDataPlot$x[2])
if(is.null(valid_data) | length(index(data))>3) valid_data <<- data
par(bg="black")
plot.zoo(valid_data, main=symb, log=ifelse(input$scale_finDataPlot=="Linear","","y"), xlab="Index", ylab=NA, col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
grid(col="grey")
}
}
})
})
###Delete Button
observeEvent(input$finDataDelete, priority = 1,{
delData(yuimaGUItable$series$Symb[input$database1_rows_selected])
})
###DeleteAll Button
observeEvent(input$finDataDeleteAll, priority = 1,{
delData(yuimaGUItable$series$Symb[input$database1_rows_all])
})
###Save Button
output$finDataSave <- {
saveData()
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/load_data/finance.R |
###Read file
fileUp_O <- reactive({
if (!is.null(input$yourFile$datapath)){
sep <- input$yourFileSep
if(input$yourFileSep=="default") sep <- ""
skip <- input$yourFileLine-1
if(is.na(skip)) skip <- 0
dec <- input$yourFileDec
if(input$yourFileDec=="") dec <- "."
if(input$yourFileHeader=="Only rows")
z <- read.csv(input$yourFile$datapath ,sep = sep, header = FALSE, row.names = 1, check.names = FALSE, stringsAsFactors = FALSE, dec = dec, na.strings = input$yourFileNA, skip = skip)
if(input$yourFileHeader=="Only columns"){
z <- read.csv(input$yourFile$datapath, sep = sep, header = FALSE, check.names = FALSE, stringsAsFactors = FALSE, dec = dec, na.strings = input$yourFileNA, skip = skip)
z <- data.frame(t(z), row.names = 1, check.names = FALSE)
z <- data.frame(t(z), check.names = FALSE)
}
if (input$yourFileHeader=="Both")
z <- read.csv(input$yourFile$datapath, sep = sep, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE, dec = dec, na.strings = input$yourFileNA, skip = skip)
if (input$yourFileHeader=="None")
z <- read.csv(input$yourFile$datapath, sep = sep, header = FALSE, check.names = FALSE, stringsAsFactors = FALSE, dec = dec, na.strings = input$yourFileNA, skip = skip)
if (input$yourFileHeader=="Default")
z <- read.csv(input$yourFile$datapath, sep = sep, check.names = FALSE, stringsAsFactors = FALSE, dec = dec, na.strings = input$yourFileNA, skip = skip)
if (input$yourFileHeader=="Only rows" | identical(colnames(z),paste("V",seq(1,length(colnames(z))),sep="")))
colnames(z) <- paste("X",seq(1,length(colnames(z))),"_",make.names(input$yourFile$name),sep="")
dec <- isolate({ifelse(input$yourFileDec=="", ".", input$yourFileDec)})
if(dec==".") dec <- "\\."
thnd <- input$yourFileThnd
if(thnd==".") thnd <- "\\."
zz <- data.frame(row.names = rownames(z), x = apply(z, 2, function(x) gsub(pattern = dec, replacement = ".", x = gsub(pattern = thnd, replacement = "", x = as.character(x)))))
colnames(zz) <- colnames(z)
return(zz)
}
})
###Display Index choices: columns of file or transposed file
output$yourFileIndex <- renderUI({
temp <- try(colnames(fileUp_O()))
if (input$yourFileSwitch==TRUE){
temp <- try(rownames(fileUp_O()))
if(class(temp)!="try-error")
if (input$yourFileHeader=="Only columns" | identical(temp,paste("V",seq(1,length(temp)),sep="")))
temp <- paste("X",seq(1,length(temp)),"_",make.names(input$yourFile$name),sep="")
}
if (class(temp)=="try-error")
return(selectInput("yourFileIndex",label = "Index", width = "60%", choices = c("Row Headers"="default","Numeric"="numeric"), selected = "default"))
if (class(temp)!="try-error")
return(selectInput("yourFileIndex",label = "Index", width = "60%", choices = c("Row Headers"="default","Numeric"="numeric",temp), selected = "default"))
})
###File to upload
fileUp <- reactive({
if (!is.null(input$yourFile$datapath)){
z <- fileUp_O()
if (input$yourFileSwitch==TRUE) {
z <- as.data.frame(t(z), check.names = FALSE)
if (identical(colnames(z), as.character(seq(1,length(colnames(z))))))
colnames(z) <- paste("X",seq(1,length(colnames(z))),"_",make.names(input$yourFile$name),sep="")
}
###Display choices for Index Type and set to "numeric" if Index is "numeric"
output$yourFileFUN <- renderUI({
if (!is.null(input$yourFileIndex)){
sel <- "%Y-%m-%d"
if (input$yourFileIndex=="numeric" | !all(is.na(as.numeric(as.character(rownames(z))))) )
sel <- "numeric"
selectInput("yourFileFUN", label = "Index Format", width = "60%", choices = c("Numeric"="numeric", "Year-Month-Day (yyyy-mm-dd)"="%Y-%m-%d", "Month-Day-Year (mm-dd-yyyy)"="%m-%d-%Y", "Month-Day-Year (mm-dd-yy)"="%m-%d-%y", "Day-Month-Year (dd-mm-yyyy)"="%d-%m-%Y", "Day-Month-Year (dd-mm-yy)"="%d-%m-%y", "Year/Month/Day (yyyy/mm/dd)"="%Y/%m/%d", "Month/Day/Year (mm/dd/yyyy)"="%m/%d/%Y", "Month/Day/Year (mm/dd/yy)"="%m/%d/%y", "Day/Month/Year (dd/mm/yyyy)"="%d/%m/%Y", "Day/Month/Year (dd/mm/yy)"="%d/%m/%y"), selected = sel)
}
})
if(input$yourFileIndex!="default" & input$yourFileIndex!="numeric")
z <- data.frame(z, row.names = which(colnames(z)==input$yourFileIndex), check.names = FALSE)
if(input$yourFileIndex=="numeric")
z <- data.frame(z, row.names = seq(1,length(rownames(z))), check.names = FALSE)
return (z)
}
})
###Display Upload Button
output$yourFileButton <- renderUI ({
if (!is.null(input$yourFile$datapath))
return(tags$button(type="button", id="yourFileGo", class = "action-button", em("Load data")))
})
observe({
shinyjs::toggle("yourFileButton", condition = "try-error"!=(class(try(fileUp()))))
})
###Display text "Preview"
output$yourFilePreviewText <- renderText ({
if (!is.null(input$yourFile$datapath))
return("Preview")
})
###Display Preview of file to upload
output$yourFilePreview <- DT::renderDataTable(options=list(scrollX=TRUE, scrollY = 250, scrollCollapse = FALSE, deferRender = TRUE, dom = 'frtiS'), extensions = 'Scroller', selection = "none", rownames = TRUE, {
if (!is.null(input$yourFile$datapath))
return (fileUp())
})
###Upload file
observeEvent(input$yourFileGo, priority = 1, {
closeAlert(session, "yourDataAlert_err")
closeAlert(session, "yourDataAlert_warn")
closeAlert(session, "yourDataAlert_succ")
info <- addData(fileUp(), typeIndex = input$yourFileFUN)
if(!is.null(info$err))
createAlert(session = session, anchorId = "yourDataAlert", alertId = "yourDataAlert_err", content = paste("Unable to load following symbols:", paste(info$err,collapse = " ")), style = "error")
if(!is.null(info$already_in))
createAlert(session = session, anchorId = "yourDataAlert", alertId = "yourDataAlert_warn", content = paste("WARNING! Following symbols already loaded:", paste(info$already_in,collapse = " ")), style = "warning")
if(is.null(info$err) & is.null(info$already_in))
createAlert(session = session, anchorId = "yourDataAlert", alertId = "yourDataAlert_succ", content = paste("All symbols loaded successfully"), style = "success")
})
###Display data available
output$database2 <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)!=0)
return (yuimaGUItable$series)
})
###Delete Button
observeEvent(input$yourFileDelete, priority = 1,{
delData(yuimaGUItable$series$Symb[input$database2_rows_selected])
})
###DeleteAll Button
observeEvent(input$yourFileDeleteAll, priority = 1,{
delData(yuimaGUItable$series$Symb[input$database2_rows_all])
})
###Save Button
output$yourFileSave <- {
saveData()
}
observe({
shinyjs::toggle("buttons_DataIO_file", condition = length(yuimaGUIdata$series)!=0)
shinyjs::toggle("buttons_DataIO_fin", condition = length(yuimaGUIdata$series)!=0)
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/load_data/your_file.R |
###Display estimated models
output$multi_databaseModels <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$multimodel)==0){
NoData <- data.frame("Symb"=NA,"Here will be stored models you estimate in the previous tabs"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$multimodel)
})
multi_rowToPrint <- reactiveValues(id = NULL)
observe(priority = 1, {
multi_rowToPrint$id <<- NULL
n <- nrow(yuimaGUItable$multimodel)
if (n > 0) {
multi_rowToPrint$id <<- n
if (!is.null(input$multi_databaseModels_row_last_clicked)) multi_rowToPrint$id <- min(n, input$multi_databaseModels_row_last_clicked)
}
})
###Print estimated model in Latex
output$multi_estimatedModelsLatex <- renderUI({
if (!is.null(multi_rowToPrint$id)){
id <- rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id]
id1 <- unlist(strsplit(id, split = " "))[1]
id2 <- as.numeric(unlist(strsplit(id, split = " "))[2])
symbs <- yuimaGUIdata$multimodel[[id1]][[id2]]$info$symb
withMathJax(printModelLatex(multi = TRUE, symb = symbs, as.character(yuimaGUItable$multimodel[multi_rowToPrint$id, "Model"]), process = as.character(yuimaGUItable$multimodel[multi_rowToPrint$id, "Class"]), jumps = as.character(yuimaGUItable$multimodel[multi_rowToPrint$id, "Jumps"])))
}
})
###Print Symbol
output$multi_SymbolName <- renderText({
if (!is.null(multi_rowToPrint$id))
rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id]
})
###More Info
output$multi_text_MoreInfo <- renderUI({
id <- unlist(strsplit(rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id], split = " "))
info <- yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info
div(
h3(id[1], " - " , info$modName, class = "hModal"),
h4(
em("series:"), paste(info$symb, collapse = ", "), br(),
em("series to log:"), paste(info$toLog, collapse = ", "), br(),
em("delta:"), max(info$delta), br(),
br(),
em("method:"), info$method, br(),
em("threshold:"), info$threshold, br(),
em("trials:"), info$trials, br(),
em("seed:"), info$seed, br(),
#REMOVE# em("joint:"), info$joint, br(),
#REMOVE# em("aggregation:"), info$aggregation, br(),
#REMOVE# em("threshold:"), info$threshold
class = "hModal"
),
align="center"
)
})
output$multi_table_MoreInfo <- renderTable(digits=5, rownames = TRUE, {
id <- unlist(strsplit(rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id], split = " "))
info <- yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info
if (info$class=="Fractional process") coef <- as.data.frame(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$qmle)
else coef <- as.data.frame(t(summary(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$qmle)@coef))
params <- getAllParams(mod = yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model, class = info$class)
lower <- data.frame(info$lower)
upper <- data.frame(info$upper)
fixed <- data.frame(info$fixed)
start <- data.frame(info$start)
startMin <- data.frame(info$startMin)
startMax <- data.frame(info$startMax)
if(length(lower)==0) lower[1,params[1]] <- NA
if(length(upper)==0) upper[1,params[1]] <- NA
#if(length(fixed)==0) fixed[1,params[1]] <- NA
if(length(start)==0) start[1,params[1]] <- NA
if(length(startMin)==0) startMin[1,params[1]] <- NA
if(length(startMax)==0) startMax[1,params[1]] <- NA
table <- rbind.fill(coef[,unique(colnames(coef))], #fixed,
start, startMin, startMax, lower, upper)
rownames(table) <- c("Estimate", "Std. Error", #"fixed",
"start", "startMin", "startMax", "lower", "upper")
return(t(table))
})
###Print estimates
observe({
if (!is.null(multi_rowToPrint$id)){
symb <- unlist(strsplit(rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id], split = " "))[1]
modN <- as.numeric(unlist(strsplit(rownames(yuimaGUItable$multimodel)[multi_rowToPrint$id], split = " "))[2])
if (yuimaGUIdata$multimodel[[symb]][[modN]]$info$class=="Fractional process") table <- yuimaGUIdata$multimodel[[symb]][[modN]]$qmle
else table <- t(summary(yuimaGUIdata$multimodel[[symb]][[modN]]$qmle)@coef)
outputTable <- changeBase(table = table, yuimaGUI = yuimaGUIdata$multimodel[[symb]][[modN]], newBase = input$multi_baseModels, session = session, choicesUI="multi_baseModels", anchorId = "multi_panel_estimates_alert", alertId = "multi_modelsAlert_conversion")
output$multi_estimatedModelsTable <- renderTable(rownames = TRUE, {
if (!is.null(multi_rowToPrint$id))
return(outputTable)
})
}
})
observe({
shinyjs::toggle("multi_estimates_info", condition = !is.null(input$multi_databaseModels_rows_all))
})
observe({
test <- FALSE
choices <- NULL
if(length(names(yuimaGUIdata$multimodel))!=0) for (i in names(yuimaGUIdata$multimodel)) for (j in 1:length(yuimaGUIdata$multimodel[[i]]))
if(yuimaGUIdata$multimodel[[i]][[j]]$info$class %in% c("Diffusion process", "Compound Poisson", "Levy process", "COGARCH")){
test <- TRUE
choices <- c(choices, paste(i,j))
}
shinyjs::toggle(id = "multi_model_modal_fitting_body", condition = test)
shinyjs::toggle(id = "multi_databaseModels_button_showResults", condition = test)
output$multi_model_modal_model_id <- renderUI({
if (test==TRUE){
selectInput("multi_model_modal_model_id", label = "Model ID", choices = choices)
}
})
output$multi_model_modal_series_id <- renderUI({
if (!is.null(input$multi_model_modal_model_id)){
id <- unlist(strsplit(input$multi_model_modal_model_id, split = " " , fixed = FALSE))
symb <- try(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info$symb)
choices <- 1:length(symb)
names(choices) <- symb
if(class(choices)!='try-error')
selectInput("multi_model_modal_series_id", label = "Series", choices = choices)
}
})
})
observe({
if(!is.null(input$multi_model_modal_model_id)) {
id <- unlist(strsplit(input$multi_model_modal_model_id, split = " " , fixed = FALSE))
type <- isolate({yuimaGUIdata$multimodel})[[id[1]]][[as.numeric(id[2])]]$info$class
shinyjs::toggle(id = "multi_model_modal_plot_intensity", condition = type %in% c("Compound Poisson", "Levy process"))
shinyjs::toggle(id = "multi_model_modal_plot_variance", condition = type %in% c("COGARCH"))
shinyjs::toggle(id = "multi_model_modal_plot_distr", condition = type %in% c("Diffusion process","Compound Poisson", "Levy process"))
shinyjs::toggle(id = "multi_model_modal_plot_test", condition = type %in% c("Diffusion process","Compound Poisson", "Levy process"))
}
})
observe({
if(!is.null(input$multi_model_modal_model_id)){
id <- unlist(strsplit(input$multi_model_modal_model_id, split = " " , fixed = FALSE))
isolated_yuimaGUIdataModel <- isolate({yuimaGUIdata$multimodel})
if(id[1] %in% names(isolated_yuimaGUIdataModel)) if (length(isolated_yuimaGUIdataModel[[id[1]]])>=as.integer(id[2])){
y <- isolated_yuimaGUIdataModel[[id[1]]][[as.numeric(id[2])]]
if (y$info$class=="Diffusion process"){
delta <- y$model@sampling@delta
t <- y$model@sampling@grid[[1]][-length(y$model@sampling@grid[[1]])]
for (i in 1:length(y$model@[email protected])) {
if(i==1) x <- y$model@[email protected][[i]]
else x <- merge(x, y$model@[email protected][[i]])
}
dx <- diff(x)
x <- x[-length(x),]
for (i in names(y$qmle@coef)) assign(i, value = as.numeric(y$qmle@coef[i]))
mu <- sapply(y$model@model@drift, function(x) eval(x))
sigma <- do.call(rbind, (lapply(y$model@model@diffusion, FUN = function(x) sapply(x, function(y) eval(y)))))
sigma_inv <- try(solve(sigma))
if(class(sigma_inv)=="try-error"){
shinyjs::hide("multi_model_modal_plot_distr")
output$multi_model_modal_plot_test <- renderUI({
HTML(paste("<div><h2 class='hModal'>Tool not available for this model.<br/>The diffusion matrix is not invertible.</h2></div>"))
})
} else {
z <- apply(as.matrix((dx-mu*delta)/sqrt(delta)), MARGIN = 1, FUN = function(y) sigma_inv%*%y)
if(class(z)=="numeric") z <- t(z)
if(!is.null(input$multi_model_modal_series_id)) if(nrow(z)>=input$multi_model_modal_series_id){
z_univ <- data.frame("V1" = as.numeric(z[as.numeric(input$multi_model_modal_series_id),]))
shinyjs::show("multi_model_modal_plot_distr")
output$multi_model_modal_plot_distr <- renderPlot({
return(
ggplot(z_univ, aes(x = V1)) +
theme(
plot.title = element_text(size=14, face= "bold", hjust = 0.5),
axis.title=element_text(size=12),
legend.position="none"
) +
stat_function(fun = dnorm, args = list(mean = 0, sd = 1), fill = "blue",color = "blue", geom = 'area', alpha = 0.5) +
geom_density(alpha = 0.5, fill = "green", color = "green") +
xlim(-4, 4) +
labs(fill="", title = "Empirical VS Theoretical Distribution", x = "Standardized Increments", y = "Density")
)
})
output$multi_model_modal_plot_test <- renderUI({
ksTest <- try(ks.test(x = z_univ$V1, "pnorm"))
if(class(ksTest)!="try-error")
HTML(paste("<div><h5 class='hModal'>Kolmogorov-Smirnov p-value (the two distributions coincide): ", format(ksTest$p.value, scientific=T, digits = 2), "</h5></div>"))
})
}
}
}
# else if (y$info$class=="COGARCH"){
#
# dx <- diff(y$model@[email protected][,1])
# v <- sqrt(cogarchNoise(y$model, param = as.list(coef(y$qmle)))[email protected][,"v"])
# v <- v/mean(v)*sd(dx)
# z <- data.frame("dx" = dx, "vplus" = v[-1], "vminus" = -v[-1], "time" = index(dx))
# output$multi_model_modal_plot_variance <- renderPlot({
# return(
# ggplot(z, aes(x = time)) +
# geom_line(aes(y = dx), size = 1, color = "black") +
# geom_line(aes(y = vplus), size = 1, color = "green") +
# geom_line(aes(y = vminus), size = 1, color = "green") +
# scale_color_manual(values=c("black", "green", "green")) +
# theme(
# plot.title = element_text(size=14, face= "bold", hjust = 0.5),
# axis.title=element_text(size=12),
# legend.position="none"
# ) +
# labs(fill="", title = "Empirical VS Estimated Volatility", x = "", y = "Increments")
# )
# })
# }
#
# else if (y$info$class=="Compound Poisson" | y$info$class=="Levy process"){
# if (is.null(y$info$threshold)) threshold <- 0
# else threshold <- ifelse(is.na(y$info$threshold), 0, y$info$threshold)
# x <- as.numeric(y$model@[email protected][[1]])
# dx <- diff(x)
# dx <- dx[abs(dx)>threshold]
# #dx <- dx-sign(dx)*threshold
# for (i in names(y$qmle@coef)) assign(i, value = as.numeric(y$qmle@coef[i]))
# dx <- data.frame("V1" = dx)
# if(y$info$jumps=="Gaussian"){
# output$multi_model_modal_plot_distr <- renderPlot({
# return(
# ggplot(dx, aes(x = V1)) +
# theme(
# plot.title = element_text(size=14, face= "bold", hjust = 0.5),
# axis.title=element_text(size=12),
# legend.position="none"
# ) +
# stat_function(fun = dnorm, args = list(mean = mu_jump, sd = sigma_jump), fill = "blue",color = "blue", geom = 'area', alpha = 0.5) +
# geom_density(alpha = 0.5, fill = "green", color = "green") +
# xlim(-4, 4) +
# labs(fill="", title = "Empirical VS Estimated Distribution", x = "Increments", y = "Density")
# )
# })
# ksTest <- try(ks.test(x = as.numeric(dx$V1), "pnorm", mean = mu_jump, sd = sigma_jump))
# output$multi_model_modal_plot_test <- renderUI({
# if(class(ksTest)!="try-error")
# HTML(paste("<div><h5 class='hModal'>Kolmogorov-Smirnov p-value (the two distributions coincide): ", format(ksTest$p.value, scientific=T, digits = 2), "</h5></div>"))
# })
# }
# if(y$info$jumps=="Uniform"){
# output$multi_model_modal_plot_distr <- renderPlot({
# return(
# ggplot(dx, aes(x = V1)) +
# theme(
# plot.title = element_text(size=14, face= "bold", hjust = 0.5),
# axis.title=element_text(size=12),
# legend.position="none"
# ) +
# stat_function(fun = dunif, args = list(min = a_jump, max = b_jump), fill = "blue",color = "blue", geom = 'area', alpha = 0.5) +
# geom_density(alpha = 0.5, fill = "green", color = "green") +
# xlim(min(dx$V1),max(dx$V1)) +
# labs(fill="", title = "Empirical VS Estimated Distribution", x = "Increments", y = "Density")
# )
# })
# ksTest <- try(ks.test(x = as.numeric(dx$V1), "punif", min = a_jump, max = b_jump))
# output$multi_model_modal_plot_test <- renderUI({
# if(class(ksTest)!="try-error")
# HTML(paste("<div><h5 class='hModal'>Kolmogorov-Smirnov p-value (the two distributions coincide): ", format(ksTest$p.value, scientific=T, digits = 2), "</h5></div>"))
# })
# }
#
#
# delta <- y$model@sampling@delta
# jumps <- ifelse(abs(diff(x))>threshold,1,0)
# jumps[is.na(jumps)] <- 0
# empirical_Lambda <- cumsum(jumps)
# t <- y$model@sampling@grid[[1]][-1]
# theory_Lambda <- cumsum(eval(y$model@model@measure$intensity)*rep(delta, length(t)))
# Lambda <- data.frame(empirical = empirical_Lambda, theory = theory_Lambda, time = index(y$model@[email protected])[-1])
# output$multi_model_modal_plot_intensity <- renderPlot({
# return(
# ggplot(Lambda, aes(x = time)) +
# geom_line(aes(y = empirical), size = 1, color = "green") +
# geom_line(aes(y = theory), size = 1, color = "blue") +
# scale_color_manual(values=c("green", "blue")) +
# theme(
# plot.title = element_text(size=14, face= "bold", hjust = 0.5),
# axis.title=element_text(size=12),
# legend.position="none"
# ) +
# labs(fill="", title = "Empirical VS Estimated Intensity", x = "", y = "Number of Jumps")
# )
#
# })
#
# }
}
}
})
###Delete Model
observeEvent(input$multi_databaseModelsDelete, priority = 1, {
if(!is.null(input$multi_databaseModels_rows_selected) & !is.null(input$multi_databaseModels_row_last_clicked)){
if(input$multi_databaseModels_row_last_clicked %in% input$multi_databaseModels_rows_selected){
rowname <- unlist(strsplit(rownames(yuimaGUItable$multimodel)[input$multi_databaseModels_row_last_clicked], split = " " , fixed = FALSE))
delMultiModel(symb=rowname[1], n=rowname[2])
closeAlert(session, alertId = "modelsAlert_conversion")
}
}
})
###DeleteAll Model
observeEvent(input$multi_databaseModelsDeleteAll, priority = 1, {
if(!is.null(input$multi_databaseModels_rows_all)){
closeAlert(session, alertId = "modelsAlert_conversion")
rowname <- unlist(strsplit(rownames(yuimaGUItable$multimodel)[input$multi_databaseModels_rows_all], split = " " , fixed = FALSE))
delMultiModel(symb=rowname[seq(1,length(rowname),2)], n=rowname[seq(2,length(rowname),2)])
}
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/modeling/multivariate_results.R |
###Model Input depending on Class Input
output$multi_model <- renderUI({
choices <- as.vector(defaultMultiModels[names(defaultMultiModels)==input$multi_modelClass])
if(input$multi_modelClass!="Fractional process")
for(i in names(yuimaGUIdata$usr_multimodel))
if (yuimaGUIdata$usr_multimodel[[i]]$class==input$multi_modelClass) {
if(input$multi_modelClass!="Diffusion process") choices <- c(i, choices)
else if (length(getAllParams(mod = setModelByName(name = i), class = input$multi_modelClass))!=0) choices <- c(i, choices)
}
return (selectInput("multi_model",label = "Model Name", choices = choices, multiple = FALSE))
})
output$multi_jumps <- renderUI({
if (input$multi_modelClass=="Compound Poisson")
return(selectInput("multi_jumps",label = "Jumps", choices = defaultJumps))
if (input$multi_modelClass=="Levy process"){
jump_choices <- defaultJumps
jump_sel <- NULL
if(!is.null(input$multi_model)){
if(input$multi_model=="Geometric Brownian Motion with Jumps") jump_sel <- "Gaussian"
}
return(div(
column(6,selectInput("model_levy_intensity", label = "Intensity", choices = c(#"None",
"Constant"="lambda"))),
column(6,selectInput("multi_jumps",label = "Jumps", choices = jump_choices, selected = jump_sel)))
)
}
})
output$multi_pq_C <- renderUI({
if (input$multi_modelClass=="CARMA")
return(div(
column(6,numericInput("AR_C",label = "AR degree (p)", value = 2, min = 1, step = 1)),
column(6,numericInput("MA_C",label = "MA degree (q)", value = 1, min = 1, step = 1))
))
if (input$multi_modelClass=="COGARCH")
return(div(
column(6,numericInput("AR_C",label = "AR degree (p)", value = 1, min = 1, step = 1)),
column(6,numericInput("MA_C",label = "MA degree (q)", value = 1, min = 1, step = 1))
))
})
###Print last selected multi_model in Latex
output$multi_PrintModelLatex <- renderUI({
shinyjs::hide("multi_titlePrintModelLatex")
if (!is.null(input$multi_model)){
shinyjs::show("multi_titlePrintModelLatex")
class <- isolate({input$multi_modelClass})
return(withMathJax(printModelLatex(multi = TRUE, symb = rownames(multi_seriesToEstimate$table), names = input$multi_model, process = class, jumps = jumps_shortcut(class = class, jumps = input$multi_jumps))))
}
})
###Display available data
output$multi_database3 <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to multi_model
multi_seriesToEstimate <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$multi_buttonSelect_models_Univariate, priority = 1, {
multi_seriesToEstimate$table <<- rbind(multi_seriesToEstimate$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$multi_database3_rows_selected]) & !(rownames(yuimaGUItable$series) %in% rownames(multi_seriesToEstimate$table)),])
})
###SelectAll Button
observeEvent(input$multi_buttonSelectAll_models_Univariate, priority = 1, {
multi_seriesToEstimate$table <<- rbind(multi_seriesToEstimate$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$multi_database3_rows_all]) & !(rownames(yuimaGUItable$series) %in% rownames(multi_seriesToEstimate$table)),])
})
###Display Selected Data
output$multi_database4 <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (nrow(multi_seriesToEstimate$table)==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (multi_seriesToEstimate$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(multi_seriesToEstimate$table)!=0){
if (length(yuimaGUItable$series)==0)
multi_seriesToEstimate$table <<- data.frame()
else
multi_seriesToEstimate$table <<- multi_seriesToEstimate$table[which(as.character(multi_seriesToEstimate$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$multi_buttonDelete_models_Univariate, priority = 1,{
if (!is.null(input$multi_database4_rows_selected))
multi_seriesToEstimate$table <<- multi_seriesToEstimate$table[-input$multi_database4_rows_selected,]
})
###DeleteAll Button
observeEvent(input$multi_buttonDeleteAll_models_Univariate, priority = 1,{
if (!is.null(input$multi_database4_rows_all))
multi_seriesToEstimate$table <<- multi_seriesToEstimate$table[-input$multi_database4_rows_all,]
})
###Interactive range of multi_selectRange chart
range_selectRange <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$multi_selectRange_brush) & !is.null(input$multi_plotsRangeSeries)){
data <- getData(input$multi_plotsRangeSeries)
test <- (length(index(window(data, start = input$multi_selectRange_brush$xmin, end = input$multi_selectRange_brush$xmax))) > 3)
if (test==TRUE){
range_selectRange$x <- c(as.Date(input$multi_selectRange_brush$xmin), as.Date(input$multi_selectRange_brush$xmax))
range_selectRange$y <- c(input$multi_selectRange_brush$ymin, input$multi_selectRange_brush$ymax)
}
}
})
observe({
shinyjs::toggle(id="multi_plotsRangeErrorMessage", condition = nrow(multi_seriesToEstimate$table)==0)
shinyjs::toggle(id="multi_plotsRangeAll", condition = nrow(multi_seriesToEstimate$table)!=0)
})
###Display charts: series and its increments
observe({
symb <- input$multi_plotsRangeSeries
if(!is.null(symb))
if (symb %in% rownames(yuimaGUItable$series)){
data <- getData(symb)
incr <- na.omit(Delt(data, type = "arithmetic"))
condition <- all(is.finite(incr))
shinyjs::toggle("multi_selectRangeReturns", condition = condition)
range_selectRange$x <- NULL
range_selectRange$y <- NULL
start <- as.character(multi_seriesToEstimate$table[input$multi_plotsRangeSeries,"From"])
end <- as.character(multi_seriesToEstimate$table[input$multi_plotsRangeSeries,"To"])
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
output$multi_selectRange <- renderPlot({
if ((symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(multi_seriesToEstimate$table)))){
par(bg="black")
plot.zoo(window(data, start = range_selectRange$x[1], end = range_selectRange$x[2]), main=symb, xlab="Index", ylab=NA, log=switch(input$multi_scale_selectRange,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(data, start = start, end = end), col = "green")
grid(col="grey")
}
})
output$multi_selectRangeReturns <- renderPlot({
if (symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(multi_seriesToEstimate$table)) & condition){
par(bg="black")
plot.zoo( window(incr, start = range_selectRange$x[1], end = range_selectRange$x[2]), main=paste(symb, " - Percentage Increments"), xlab="Index", ylab=NA, log=switch(input$multi_scale_selectRange,"Linear"="","Logarithmic (Y)"="", "Logarithmic (X)"="x", "Logarithmic (XY)"="x"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(incr, start = start, end = end), col = "green")
grid(col="grey")
}
})
}
})
output$multi_plotsRangeSeries <- renderUI({
selectInput("multi_plotsRangeSeries", label = "Series", choices = rownames(multi_seriesToEstimate$table), selected = input$multi_plotsRangeSeries)
})
###Choose Range input set to "Select range from charts" if charts have been brushed
output$multi_chooseRange <- renderUI({
sel <- "full"
if (!is.null(range_selectRange$x)) sel <- "selected"
selectInput("multi_chooseRange", label = "Range", choices = c("Full Range" = "full", "Select Range from Charts" = "selected", "Specify Range" = "specify"), selected = sel)
})
output$multi_chooseRange_specify <- renderUI({
if(!is.null(input$multi_plotsRangeSeries)) {
data <- getData(input$multi_plotsRangeSeries)
if(class(index(data))=="numeric")
return(div(
column(6,numericInput("chooseRange_specify_t0", label = "From", min = start(data), max = end(data), value = start(data))),
column(6,numericInput("chooseRange_specify_t1", label = "To", min = start(data), max = end(data), value = end(data)))
))
if(class(index(data))=="Date")
return(dateRangeInput("chooseRange_specify_date", start = start(data), end = end(data), label = "Specify Range"))
}
})
observe({
shinyjs::toggle(id = "multi_chooseRange_specify", condition = (input$multi_chooseRange)=="specify")
})
###Function to update data range to use to estimate models
updateRange_multi_seriesToEstimate <- function(symb, range = c("full","selected","specify"), type = c("Date", "numeric")){
for (i in symb){
data <- getData(i)
if (range == "full"){
levels(multi_seriesToEstimate$table[,"From"]) <- c(levels(multi_seriesToEstimate$table[,"From"]), as.character(start(data)))
levels(multi_seriesToEstimate$table[,"To"]) <- c(levels(multi_seriesToEstimate$table[,"To"]), as.character(end(data)))
multi_seriesToEstimate$table[i,"From"] <<- as.character(start(data))
multi_seriesToEstimate$table[i,"To"] <<- as.character(end(data))
}
if (range == "selected"){
if(!is.null(range_selectRange$x) & class(index(data))==type){
start <- range_selectRange$x[1]
end <- range_selectRange$x[2]
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(multi_seriesToEstimate$table[,"From"]) <- c(levels(multi_seriesToEstimate$table[,"From"]), as.character(start))
levels(multi_seriesToEstimate$table[,"To"]) <- c(levels(multi_seriesToEstimate$table[,"To"]), as.character(end))
multi_seriesToEstimate$table[i,"From"] <<- as.character(start)
multi_seriesToEstimate$table[i,"To"] <<- as.character(end)
}
}
if (range == "specify"){
if(class(index(data))==type){
if(class(index(data))=="Date"){
start <- input$chooseRange_specify_date[1]
end <- input$chooseRange_specify_date[2]
}
if(class(index(data))=="numeric"){
start <- input$chooseRange_specify_t0
end <- input$chooseRange_specify_t1
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(multi_seriesToEstimate$table[,"From"]) <- c(levels(multi_seriesToEstimate$table[,"From"]), as.character(start))
levels(multi_seriesToEstimate$table[,"To"]) <- c(levels(multi_seriesToEstimate$table[,"To"]), as.character(end))
multi_seriesToEstimate$table[i,"From"] <<- as.character(start)
multi_seriesToEstimate$table[i,"To"] <<- as.character(end)
}
}
}
}
###Apply selected range by double click
observeEvent(input$multi_selectRange_dbclick, priority = 1, {
updateRange_multi_seriesToEstimate(input$multi_plotsRangeSeries, range = "selected", type = class(index(getData(input$multi_plotsRangeSeries))))
})
###Apply selected range
observeEvent(input$multi_buttonApplyRange, priority = 1, {
updateRange_multi_seriesToEstimate(input$multi_plotsRangeSeries, range = input$multi_chooseRange, type = class(index(getData(input$multi_plotsRangeSeries))))
})
###ApplyAll selected range
observeEvent(input$multi_buttonApplyAllRange, priority = 1, {
updateRange_multi_seriesToEstimate(rownames(multi_seriesToEstimate$table), range = input$multi_chooseRange, type = class(index(getData(input$multi_plotsRangeSeries))))
})
prev_dim <- 0
prev_buttonDelta <- 0
prev_buttonAllDelta <- 0
observe({
class <- isolate({input$multi_modelClass})
for (symb in rownames(multi_seriesToEstimate$table)){
if (is.null(yuimaGUIsettings$delta[[symb]])) {
i <- index(getData(symb))
if(is.numeric(i)) yuimaGUIsettings$delta[[symb]] <<- mode(diff(i))
else yuimaGUIsettings$delta[[symb]] <<- mode(diff(i))/100
}
if (is.null(yuimaGUIsettings$toLog[[symb]])) yuimaGUIsettings$toLog[[symb]] <<- FALSE
}
if(!is.null(input$multi_model)) if (class(try(setModelByName(input$multi_model, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = class, jumps = input$multi_jumps), AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA))))!="try-error" & nrow(multi_seriesToEstimate$table)>0){
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]]))
yuimaGUIsettings$estimation[[input$multi_model]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]]))
yuimaGUIsettings$estimation[[input$multi_model]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["fixed"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[input$multi_model]][["fixed"]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["start"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[input$multi_model]][["start"]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["threshold"]]))
yuimaGUIsettings$estimation[[input$multi_model]][["threshold"]] <<- setThreshold(class = class, data = data)
deltas <- NULL
datas <- NULL
for (symb in rownames(multi_seriesToEstimate$table)){
deltas <- c(deltas, yuimaGUIsettings$delta[[symb]])
data <- getData(symb)
if (yuimaGUIsettings$toLog[[symb]]==TRUE) data <- log(data)
if(is.null(datas)) datas <- data
else datas <- merge(datas, data)
}
startMinMax <- defaultBounds(name = input$multi_model,
jumps = jumps_shortcut(class = class, jumps = input$multi_jumps),
intensity = input$model_levy_intensity,
threshold = yuimaGUIsettings$estimation[[input$multi_model]][["threshold"]],
AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA),
strict = FALSE,
data = datas,
delta = deltas)
upperLower <- defaultBounds(name = input$multi_model,
jumps = jumps_shortcut(class = class, jumps = input$multi_jumps),
intensity = input$model_levy_intensity,
threshold = yuimaGUIsettings$estimation[[input$multi_model]][["threshold"]],
AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA),
strict = TRUE,
data = datas,
delta = deltas)
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["startMin"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta | nrow(multi_seriesToEstimate$table)!=prev_dim)
yuimaGUIsettings$estimation[[input$multi_model]][["startMin"]] <<- startMinMax$lower
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["startMax"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta | nrow(multi_seriesToEstimate$table)!=prev_dim)
yuimaGUIsettings$estimation[[input$multi_model]][["startMax"]] <<- startMinMax$upper
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["upper"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta | nrow(multi_seriesToEstimate$table)!=prev_dim)
yuimaGUIsettings$estimation[[input$multi_model]][["upper"]] <<- upperLower$upper
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["lower"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$multi_advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$multi_advancedSettingsButtonApplyAllDelta | nrow(multi_seriesToEstimate$table)!=prev_dim)
yuimaGUIsettings$estimation[[input$multi_model]][["lower"]] <<- upperLower$lower
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["method"]])){
if(class=="COGARCH" | class=="CARMA") yuimaGUIsettings$estimation[[input$multi_model]][["method"]] <<- "SANN"
else yuimaGUIsettings$estimation[[input$multi_model]][["method"]] <<- "L-BFGS-B"
}
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["trials"]]))
yuimaGUIsettings$estimation[[input$multi_model]][["trials"]] <<- 1
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["seed"]]))
yuimaGUIsettings$estimation[[input$multi_model]][["seed"]] <<- NA
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["joint"]]))
yuimaGUIsettings$estimation[[input$multi_model]][["joint"]] <<- FALSE
if (is.null(yuimaGUIsettings$estimation[[input$multi_model]][["aggregation"]]))
yuimaGUIsettings$estimation[[input$multi_model]][["aggregation"]] <<- TRUE
}
prev_dim <<- nrow(multi_seriesToEstimate$table)
prev_buttonDelta <<- input$multi_advancedSettingsButtonApplyDelta
prev_buttonAllDelta <<- input$multi_advancedSettingsButtonApplyAllDelta
})
observe({
valid <- TRUE
if (nrow(multi_seriesToEstimate$table)==0 | is.null(input$multi_model)) valid <- FALSE
else for(mod in input$multi_model) if (class(try(setModelByName(mod, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$multi_modelClass, jumps = input$multi_jumps), AR_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))))=="try-error") valid <- FALSE
shinyjs::toggle(id="multi_advancedSettingsAll", condition = valid)
shinyjs::toggle(id="multi_advancedSettingsErrorMessage", condition = !valid)
})
output$multi_advancedSettingsSeries <- renderUI({
if (nrow(multi_seriesToEstimate$table)!=0)
selectInput(inputId = "multi_advancedSettingsSeries", label = "Series", choices = rownames(multi_seriesToEstimate$table))
})
output$multi_advancedSettingsDelta <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries))
return (numericInput("multi_advancedSettingsDelta", label = paste("delta", input$multi_advancedSettingsSeries), value = yuimaGUIsettings$delta[[input$multi_advancedSettingsSeries]], min = 0))
})
output$multi_advancedSettingsToLog <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries)){
choices <- FALSE
if (all(getData(input$multi_advancedSettingsSeries)>0)) choices <- c(FALSE, TRUE)
return (selectInput("multi_advancedSettingsToLog", label = "Convert to log", choices = choices, selected = yuimaGUIsettings$toLog[[input$multi_advancedSettingsSeries]]))
}
})
output$multi_advancedSettingsModel <- renderUI({
if(!is.null(input$multi_model))
selectInput(inputId = "multi_advancedSettingsModel", label = "Model", choices = input$multi_model)
})
output$multi_advancedSettingsParameter <- renderUI({
if (!is.null(input$multi_model))
if (!is.null(input$multi_advancedSettingsModel)){
mod <- setModelByName(input$multi_advancedSettingsModel, dimension = nrow(multi_seriesToEstimate$table), intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$multi_modelClass, jumps = input$multi_jumps), AR_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))
par <- getAllParams(mod, input$multi_modelClass)
selectInput(inputId = "multi_advancedSettingsParameter", label = "Parameter", choices = par)
}
})
#REMOVE# output$multi_advancedSettingsFixed <- renderUI({
#REMOVE# if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
#REMOVE# numericInput(inputId = "multi_advancedSettingsFixed", label = "fixed", value = ifelse(is.null(yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][[input$multi_advancedSettingsSeries]][["fixed"]][[input$multi_advancedSettingsParameter]]),NA,yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][[input$multi_advancedSettingsSeries]][["fixed"]][[input$multi_advancedSettingsParameter]]))
#REMOVE#})
output$multi_advancedSettingsStart <- renderUI({
if (#REMOVE# !is.null(input$multi_advancedSettingsFixed) &
!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
#REMOVE# if (is.na(input$multi_advancedSettingsFixed))
numericInput(inputId = "multi_advancedSettingsStart", label = "start", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["start"]][[input$multi_advancedSettingsParameter]])
})
output$multi_advancedSettingsStartMin <- renderUI({
input$multi_advancedSettingsButtonApplyDelta
input$multi_advancedSettingsButtonApplyAllDelta
if (#REMOVE# !is.null(input$multi_advancedSettingsFixed) &
!is.null(input$multi_advancedSettingsStart) & !is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
if (#REMOVE# is.na(input$multi_advancedSettingsFixed) &
is.na(input$multi_advancedSettingsStart))
numericInput(inputId = "multi_advancedSettingsStartMin", label = "start: Min", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["startMin"]][[input$multi_advancedSettingsParameter]])
})
output$multi_advancedSettingsStartMax <- renderUI({
input$multi_advancedSettingsButtonApplyDelta
input$multi_advancedSettingsButtonApplyAllDelta
if (#REMOVE# !is.null(input$multi_advancedSettingsFixed) &
!is.null(input$multi_advancedSettingsStart) & !is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
if (#REMOVE# is.na(input$multi_advancedSettingsFixed) &
is.na(input$multi_advancedSettingsStart))
numericInput(inputId = "multi_advancedSettingsStartMax", label = "start: Max", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["startMax"]][[input$multi_advancedSettingsParameter]])
})
output$multi_advancedSettingsLower <- renderUI({
if (#REMOVE# !is.null(input$multi_advancedSettingsFixed) &
!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
#REMOVE# if (is.na(input$multi_advancedSettingsFixed))
if (input$multi_advancedSettingsMethod=="L-BFGS-B" | input$multi_advancedSettingsMethod=="Brent")
numericInput("multi_advancedSettingsLower", label = "lower", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["lower"]][[input$multi_advancedSettingsParameter]])
})
output$multi_advancedSettingsUpper <- renderUI({
if (#REMOVE# !is.null(input$multi_advancedSettingsFixed) &
!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsParameter))
#REMOVE# if (is.na(input$multi_advancedSettingsFixed))
if (input$multi_advancedSettingsMethod=="L-BFGS-B" | input$multi_advancedSettingsMethod=="Brent")
numericInput("multi_advancedSettingsUpper", label = "upper", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["upper"]][[input$multi_advancedSettingsParameter]])
})
#REMOVE# output$multi_advancedSettingsJoint <- renderUI({
#REMOVE# if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries))
#REMOVE# selectInput("multi_advancedSettingsJoint", label = "joint", choices = c(FALSE, TRUE), selected = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["joint"]])
#REMOVE# })
output$multi_advancedSettingsMethod <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries))
selectInput("multi_advancedSettingsMethod", label = "method", choices = c("L-BFGS-B", "Nelder-Mead", "BFGS", "CG", "SANN", "Brent"), selected = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["method"]])
})
#REMOVE# output$multi_advancedSettingsAggregation <- renderUI({
#REMOVE# if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries))
#REMOVE# selectInput("multi_advancedSettingsAggregation", label = "aggregation", choices = c(TRUE, FALSE), selected = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["aggregation"]])
#REMOVE# })
output$multi_advancedSettingsThreshold <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries)) if(isolate({input$multi_modelClass})=="Levy process")
numericInput("multi_advancedSettingsThreshold", label = "threshold", value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["threshold"]])
})
output$multi_advancedSettingsTrials <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries) & !is.null(input$multi_advancedSettingsMethod))
numericInput("multi_advancedSettingsTrials", label = "trials", min = 1, value = ifelse(input$multi_advancedSettingsMethod=="SANN" & yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["method"]]!="SANN",1,yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["trials"]]))
})
output$multi_advancedSettingsSeed <- renderUI({
if (!is.null(input$multi_advancedSettingsModel) & !is.null(input$multi_advancedSettingsSeries))
numericInput("multi_advancedSettingsSeed", label = "seed", min = 1, value = yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["seed"]])
})
observeEvent(input$multi_advancedSettingsButtonApplyDelta, {
yuimaGUIsettings$delta[[input$multi_advancedSettingsSeries]] <<- input$multi_advancedSettingsDelta
yuimaGUIsettings$toLog[[input$multi_advancedSettingsSeries]] <<- input$multi_advancedSettingsToLog
})
observeEvent(input$multi_advancedSettingsButtonApplyAllDelta, {
for (symb in rownames(multi_seriesToEstimate$table)){
yuimaGUIsettings$delta[[symb]] <<- input$multi_advancedSettingsDelta
if (input$multi_advancedSettingsToLog==FALSE) yuimaGUIsettings$toLog[[symb]] <<- input$multi_advancedSettingsToLog
else if (all(getData(symb)>0)) yuimaGUIsettings$toLog[[symb]] <<- input$multi_advancedSettingsToLog
}
})
observeEvent(input$multi_advancedSettingsButtonApplyModel,{
#REMOVE# yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["fixed"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsFixed
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["start"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsStart
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["startMin"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsStartMin
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["startMax"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsStartMax
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["lower"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsLower
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["upper"]][[input$multi_advancedSettingsParameter]] <<- input$multi_advancedSettingsUpper
})
observeEvent(input$multi_advancedSettingsButtonApplyGeneral,{
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["method"]] <<- input$multi_advancedSettingsMethod
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["trials"]] <<- input$multi_advancedSettingsTrials
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["seed"]] <<- input$multi_advancedSettingsSeed
#REMOVE# yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["joint"]] <<- input$multi_advancedSettingsJoint
#REMOVE# yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["aggregation"]] <<- input$multi_advancedSettingsAggregation
yuimaGUIsettings$estimation[[input$multi_advancedSettingsModel]][["threshold"]] <<- input$multi_advancedSettingsThreshold
})
observe({
closeAlert(session = session, alertId = "CARMA_COGARCH_err")
if(!is.null(input$multi_modelClass)) if(input$multi_modelClass=="CARMA" ) if(!is.null(input$AR_C)) if(!is.null(input$MA_C)) if(!is.na(input$AR_C) & !is.na(input$MA_C)) {
if(input$AR_C<=input$MA_C)
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR degree (p) must be greater than MA degree (q)")
if(input$AR_C== 0 | input$MA_C==0)
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR and MA degree (p,q) must be positive")
}
if(!is.null(input$multi_modelClass)) if(input$multi_modelClass=="COGARCH" ) if(!is.null(input$AR_C)) if(!is.null(input$MA_C)) if(!is.na(input$AR_C) & !is.na(input$MA_C)) {
if(input$AR_C<input$MA_C)
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR degree (p) must be greater than or equal to MA degree (q)")
if(input$AR_C== 0 | input$MA_C==0)
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR and MA degree (p,q) must be positive")
}
})
###Estimate models
observeEvent(input$multi_EstimateModels,{
closeAlert(session = session, alertId = "modelsErr")
valid <- TRUE
if(is.null(input$multi_model) | nrow(multi_seriesToEstimate$table)==0) {
valid <- FALSE
} else if (input$multi_modelClass=="Compound Poisson" & is.null(input$multi_jumps)) {
valid <- FALSE
} else for(mod in input$multi_model) if (class(try(setModelByName(mod, dimension = nrow(multi_seriesToEstimate$table), intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$multi_modelClass, jumps = input$multi_jumps), AR_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))))=="try-error") valid <- FALSE
if(!valid){
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "modelsAlert_err", content = "Select some series and (valid) models to estimate", style = "warning")
} else {
deltas <- NULL; datas <- NULL; toLogs <- NULL
for (symb in rownames(multi_seriesToEstimate$table)){
deltas <- c(deltas, yuimaGUIsettings$delta[[symb]])
toLogs <- c(toLogs, yuimaGUIsettings$toLog[[symb]])
data <- getData(symb)
start <- as.character(multi_seriesToEstimate$table[symb,"From"])
end <- as.character(multi_seriesToEstimate$table[symb,"To"])
times <- index(data)
if (class(times)=="numeric")
data <- data[(times >= as.numeric(start)) & (times <= as.numeric(end)), , drop = FALSE]
else
data <- data[(times >= start) & (times <= end), , drop = FALSE]
if(is.null(datas)) datas <- data
else datas <- merge(datas, data)
}
test <- try(setDataGUI(datas, delta = deltas))
if (class(test)=="try-error"){
createAlert(session = session, anchorId = "multi_panel_run_estimation_alert", alertId = "modelsAlert_err", content = "Unable to construct a synchronous grid for the data provided", style = "error")
} else {
withProgress(message = 'Estimating: ',{
for (modName in input$multi_model){
incProgress(1/(length(input$multi_model)), detail = modName)
addModel(
modName = modName,
multi = TRUE,
modClass = input$multi_modelClass,
intensity_levy = input$model_levy_intensity,
AR_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(input$multi_modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA),
jumps = jumps_shortcut(class = input$multi_modelClass, jumps = input$multi_jumps),
symbName = paste(ncol(datas), "Series", sep = ""),
data = datas,
delta = deltas,
toLog = toLogs,
start = yuimaGUIsettings$estimation[[modName]][["start"]],
startMin = yuimaGUIsettings$estimation[[modName]][["startMin"]],
startMax = yuimaGUIsettings$estimation[[modName]][["startMax"]],
method=yuimaGUIsettings$estimation[[modName]][["method"]],
trials=yuimaGUIsettings$estimation[[modName]][["trials"]],
seed = yuimaGUIsettings$estimation[[modName]][["seed"]],
fixed = yuimaGUIsettings$estimation[[modName]][["fixed"]],
lower = yuimaGUIsettings$estimation[[modName]][["lower"]],
upper = yuimaGUIsettings$estimation[[modName]][["upper"]],
joint = yuimaGUIsettings$estimation[[modName]][["joint"]],
aggregation = yuimaGUIsettings$estimation[[modName]][["aggregation"]],
threshold = yuimaGUIsettings$estimation[[modName]][["threshold"]],
session = session,
anchorId = "multi_panel_estimates_alert",
alertId = NULL
)
}
})
updateTabsetPanel(session = session, inputId = "multi_panel_estimates", selected = "Estimates")
}
}
})
observe({
valid <- TRUE
if(is.null(input$multi_model) | nrow(multi_seriesToEstimate$table)==0) valid <- FALSE
else if (input$multi_modelClass=="Compound Poisson" & is.null(input$multi_jumps)) valid <- FALSE
if(valid) closeAlert(session, alertId = "modelsAlert_err")
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/modeling/multivariate_start_estimation.R |
###Display estimated models
output$databaseModels <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$model)==0){
NoData <- data.frame("Symb"=NA,"Here will be stored models you estimate in the previous tabs"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$model)
})
rowToPrint <- reactiveValues(id = NULL)
observe(priority = 1, {
rowToPrint$id <<- NULL
n <- nrow(yuimaGUItable$model)
if (n > 0) {
rowToPrint$id <<- n
if (!is.null(input$databaseModels_row_last_clicked)) rowToPrint$id <- min(n, input$databaseModels_row_last_clicked)
}
})
###Print estimated model in Latex
output$estimatedModelsLatex <- renderUI({
if (!is.null(rowToPrint$id))
withMathJax(printModelLatex(as.character(yuimaGUItable$model[rowToPrint$id, "Model"]), process = as.character(yuimaGUItable$model[rowToPrint$id, "Class"]), jumps = as.character(yuimaGUItable$model[rowToPrint$id, "Jumps"])))
})
###Print Symbol
output$SymbolName <- renderText({
if (!is.null(rowToPrint$id))
rownames(yuimaGUItable$model)[rowToPrint$id]
})
###More Info
output$text_MoreInfo <- renderUI({
id <- unlist(strsplit(rownames(yuimaGUItable$model)[rowToPrint$id], split = " "))
info <- yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$info
div(
h3(id[1], " - " , info$modName, class = "hModal"),
h4(
em("series:"), info$symb, br(),
em("series to log:"), info$toLog, br(),
em("delta:"), info$delta, br(),
br(),
em("method:"), info$method, br(),
em("threshold:"), info$threshold, br(),
em("trials:"), info$trials, br(),
em("seed:"), info$seed, br(),
#REMOVE# em("joint:"), info$joint, br(),
#REMOVE# em("aggregation:"), info$aggregation, br(),
#REMOVE# em("threshold:"), info$threshold
class = "hModal"
),
align="center"
)
})
output$table_MoreInfo <- renderTable(digits=5, rownames = TRUE, {
id <- unlist(strsplit(rownames(yuimaGUItable$model)[rowToPrint$id], split = " "))
info <- yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$info
if (info$class=="Fractional process") coef <- as.data.frame(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$qmle)
else coef <- as.data.frame(t(summary(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$qmle)@coef))
params <- getAllParams(mod = yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model, class = info$class)
lower <- data.frame(info$lower)
upper <- data.frame(info$upper)
fixed <- data.frame(info$fixed)
start <- data.frame(info$start)
startMin <- data.frame(info$startMin)
startMax <- data.frame(info$startMax)
if(length(lower)==0) lower[1,params[1]] <- NA
if(length(upper)==0) upper[1,params[1]] <- NA
#if(length(fixed)==0) fixed[1,params[1]] <- NA
if(length(start)==0) start[1,params[1]] <- NA
if(length(startMin)==0) startMin[1,params[1]] <- NA
if(length(startMax)==0) startMax[1,params[1]] <- NA
table <- rbind.fill(coef[,unique(colnames(coef))], #fixed,
start, startMin, startMax, lower, upper)
rownames(table) <- c("Estimate", "Std. Error", #"fixed",
"start", "startMin", "startMax", "lower", "upper")
return(t(table))
})
###Print estimates
observe({
if (!is.null(rowToPrint$id)){
symb <- unlist(strsplit(rownames(yuimaGUItable$model)[rowToPrint$id], split = " "))[1]
modN <- as.numeric(unlist(strsplit(rownames(yuimaGUItable$model)[rowToPrint$id], split = " "))[2])
if (yuimaGUIdata$model[[symb]][[modN]]$info$class=="Fractional process") table <- yuimaGUIdata$model[[symb]][[modN]]$qmle
else table <- t(summary(yuimaGUIdata$model[[symb]][[modN]]$qmle)@coef)
outputTable <- changeBase(table = table, yuimaGUI = yuimaGUIdata$model[[symb]][[modN]], newBase = input$baseModels, session = session, choicesUI="baseModels", anchorId = "panel_estimates_alert", alertId = "modelsAlert_conversion")
output$estimatedModelsTable <- renderTable(rownames = TRUE, {
if (!is.null(rowToPrint$id))
return(outputTable)
})
}
})
observe({
shinyjs::toggle("estimates_info", condition = !is.null(input$databaseModels_rows_all))
})
observe({
test <- FALSE
choices <- NULL
if(length(names(yuimaGUIdata$model))!=0) for (i in names(yuimaGUIdata$model)) for (j in 1:length(yuimaGUIdata$model[[i]]))
if(yuimaGUIdata$model[[i]][[j]]$info$class %in% c("Diffusion process", "Compound Poisson", "Levy process", "COGARCH")){
test <- TRUE
choices <- c(choices, paste(i,j))
}
shinyjs::toggle(id = "model_modal_fitting_body", condition = test)
shinyjs::toggle(id = "databaseModels_button_showResults", condition = test)
output$model_modal_model_id <- renderUI({
if (test==TRUE){
selectInput("model_modal_model_id", label = "Model ID", choices = choices)
}
})
})
observe({
if(!is.null(input$model_modal_model_id)) {
id <- unlist(strsplit(input$model_modal_model_id, split = " " , fixed = FALSE))
type <- isolate({yuimaGUIdata$model})[[id[1]]][[as.numeric(id[2])]]$info$class
shinyjs::toggle(id = "model_modal_plot_intensity", condition = type %in% c("Compound Poisson", "Levy process"))
shinyjs::toggle(id = "model_modal_plot_variance", condition = type %in% c("COGARCH"))
shinyjs::toggle(id = "model_modal_plot_distr", condition = type %in% c("Diffusion process","Compound Poisson", "Levy process"))
shinyjs::toggle(id = "model_modal_plot_test", condition = type %in% c("Diffusion process","Compound Poisson", "Levy process"))
}
})
observeEvent(input$model_modal_model_id,{
if(!is.null(input$model_modal_model_id)){
id <- unlist(strsplit(input$model_modal_model_id, split = " " , fixed = FALSE))
isolated_yuimaGUIdataModel <- isolate({yuimaGUIdata$model})
if(id[1] %in% names(isolated_yuimaGUIdataModel)) if (length(isolated_yuimaGUIdataModel[[id[1]]])>=as.integer(id[2])){
y <- isolated_yuimaGUIdataModel[[id[1]]][[as.numeric(id[2])]]
if (y$info$class=="Diffusion process"){
delta <- y$model@sampling@delta
t <- y$model@sampling@grid[[1]][-length(y$model@sampling@grid[[1]])]
x <- as.numeric(y$model@[email protected][[1]])
dx <- diff(x)
x <- x[-length(x)]
for (i in names(y$qmle@coef)) assign(i, value = as.numeric(y$qmle@coef[i]))
z <- (dx-eval(y$model@model@drift)*delta)/(eval(y$model@model@diffusion[[1]])*sqrt(delta))
z <- data.frame("V1" = z)
output$model_modal_plot_distr <- renderPlot({
return(
ggplot(z, aes(x = V1)) +
theme(
plot.title = element_text(size=14, face= "bold", hjust = 0.5),
axis.title=element_text(size=12),
legend.position="none"
) +
stat_function(fun = dnorm, args = list(mean = 0, sd = 1), fill = "blue",color = "blue", geom = 'area', alpha = 0.5) +
geom_density(alpha = 0.5, fill = "green", color = "green") +
xlim(-4, 4) +
labs(fill="", title = "Empirical VS Theoretical Distribution", x = "Standardized Increments", y = "Density")
)
})
ksTest <- try(ks.test(x = as.numeric(z$V1), "pnorm"))
output$model_modal_plot_test <- renderUI({
if(class(ksTest)!="try-error")
HTML(paste("<div><h5 class='hModal'>Kolmogorov-Smirnov p-value (the two distributions coincide): ", format(ksTest$p.value, scientific=T, digits = 2), "</h5></div>"))
})
}
else if (y$info$class=="COGARCH"){
dx <- diff(y$model@[email protected][,1])
v <- sqrt(cogarchNoise(y$model, param = as.list(coef(y$qmle)))[email protected][,"v"])
v <- v/mean(v)*sd(dx)
z <- data.frame("dx" = dx, "vplus" = v[-1], "vminus" = -v[-1], "time" = index(dx))
output$model_modal_plot_variance <- renderPlot({
return(
ggplot(z, aes(x = time)) +
geom_line(aes(y = dx), size = 1, color = "black") +
geom_line(aes(y = vplus), size = 1, color = "green") +
geom_line(aes(y = vminus), size = 1, color = "green") +
scale_color_manual(values=c("black", "green", "green")) +
theme(
plot.title = element_text(size=14, face= "bold", hjust = 0.5),
axis.title=element_text(size=12),
legend.position="none"
) +
labs(fill="", title = "Empirical VS Estimated Volatility", x = "", y = "Increments")
)
})
}
else if (y$info$class=="Compound Poisson" | y$info$class=="Levy process"){
if (is.null(y$info$threshold)) threshold <- 0
else threshold <- ifelse(is.na(y$info$threshold), 0, y$info$threshold)
x <- as.numeric(y$model@[email protected][[1]])
dx <- diff(x)
dx <- dx[abs(dx)>threshold]
#dx <- dx-sign(dx)*threshold
for (i in names(y$qmle@coef)) assign(i, value = as.numeric(y$qmle@coef[i]))
dx <- data.frame("V1" = dx)
switch(y$info$jumps,
"Gaussian" = {
dfun <- dnorm
pfun <- "pnorm"
args <- list(mean = mu_jump, sd = sigma_jump)
},
"Constant" = {
dfun <- dconst
pfun <- "pconst"
args <- list(k = k_jump)
pconst <- function(q, k){q>=k}
},
"Uniform" = {
dfun <- dunif
pfun <- "punif"
args <- list(min = a_jump, max = b_jump)
},
"Inverse Gaussian" = {
dfun <- dIG
pfun <- "pIG"
args <- list(delta = delta_jump, gamma = gamma_jump)
},
"Normal Inverse Gaussian" = {
dfun <- dNIG.gui
pfun <- "pNIG.gui"
args <- list(alpha = alpha_jump, beta = beta_jump, delta = delta_jump, mu = mu_jump)
},
"Hyperbolic" = {
dfun <- dhyp.gui
pfun <- "phyp.gui"
args <- list(alpha = alpha_jump, beta = beta_jump, delta = delta_jump, mu = mu_jump)
},
"Student t" = {
dfun <- dt
pfun <- "pt"
args <- list(df = nu_jump, ncp = mu_jump)
},
"Variance Gamma" = {
dfun <- dVG.gui
pfun <- "pVG.gui"
args <- list(lambda = lambda_jump, alpha = alpha_jump, beta = beta_jump, mu = mu_jump)
},
"Generalized Hyperbolic" = {
dfun <- dghyp.gui
pfun <- "pghyp.gui"
args <- list(lambda = lambda_jump, alpha = alpha_jump, delta = delta_jump, beta = beta_jump, mu = mu_jump)
}
)
if(exists('args') & exists('dfun') & exists('pfun')){
output$model_modal_plot_distr <- renderPlot({
return(
ggplot(dx, aes(x = V1)) +
theme(
plot.title = element_text(size=14, face= "bold", hjust = 0.5),
axis.title=element_text(size=12),
legend.position="none"
) +
stat_function(fun = dfun, args = args, fill = "blue",color = "blue", geom = 'area', alpha = 0.5) +
geom_density(alpha = 0.5, fill = "green", color = "green") +
xlim(-4, 4) +
labs(fill="", title = "Empirical VS Estimated Distribution", x = "Increments", y = "Density")
)
})
ksTest <- try(do.call(what = 'ks.test', args = append( list(x = as.numeric(dx$V1), y = pfun), lapply(args, FUN = function(x) x)) ))
output$model_modal_plot_test <- renderUI({
if(class(ksTest)!="try-error")
HTML(paste("<div><h5 class='hModal'>Kolmogorov-Smirnov p-value (the two distributions coincide): ", format(ksTest$p.value, scientific=T, digits = 2), "</h5></div>"))
})
}
delta <- y$model@sampling@delta
jumps <- ifelse(abs(diff(x))>threshold,1,0)
jumps[is.na(jumps)] <- 0
empirical_Lambda <- cumsum(jumps)
t <- y$model@sampling@grid[[1]][-1]
theory_Lambda <- cumsum(eval(y$model@model@measure$intensity)*rep(delta, length(t)))
Lambda <- data.frame(empirical = empirical_Lambda, theory = theory_Lambda, time = index(y$model@[email protected])[-1])
output$model_modal_plot_intensity <- renderPlot({
return(
ggplot(Lambda, aes(x = time)) +
geom_line(aes(y = empirical), size = 1, color = "green") +
geom_line(aes(y = theory), size = 1, color = "blue") +
scale_color_manual(values=c("green", "blue")) +
theme(
plot.title = element_text(size=14, face= "bold", hjust = 0.5),
axis.title=element_text(size=12),
legend.position="none"
) +
labs(fill="", title = "Empirical VS Estimated Intensity", x = "", y = "Number of Jumps")
)
})
}
}
}
})
###Delete Model
observeEvent(input$databaseModelsDelete, priority = 1, {
if(!is.null(input$databaseModels_rows_selected) & !is.null(input$databaseModels_row_last_clicked)){
if(input$databaseModels_row_last_clicked %in% input$databaseModels_rows_selected){
rowname <- unlist(strsplit(rownames(yuimaGUItable$model)[input$databaseModels_row_last_clicked], split = " " , fixed = FALSE))
delModel(symb=rowname[1], n=rowname[2])
closeAlert(session, alertId = "modelsAlert_conversion")
}
}
})
###DeleteAll Model
observeEvent(input$databaseModelsDeleteAll, priority = 1, {
if(!is.null(input$databaseModels_rows_all)){
closeAlert(session, alertId = "modelsAlert_conversion")
rowname <- unlist(strsplit(rownames(yuimaGUItable$model)[input$databaseModels_rows_all], split = " " , fixed = FALSE))
delModel(symb=rowname[seq(1,length(rowname),2)], n=rowname[seq(2,length(rowname),2)])
}
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/modeling/univariate_results.R |
output$usr_modelClass_latex <- renderUI({
if (input$usr_modelClass=="Diffusion process")
return(withMathJax("$$dX=a(t,X,\\theta)\\;dt\\;+\\;b(t,X,\\theta)\\;dW$$"))
if (input$usr_modelClass=="Fractional process")
return(withMathJax("$$dX=a(t,X,\\theta)\\;dt\\;+\\;b(t,X,\\theta)\\;dW^H$$"))
if (input$usr_modelClass=="Compound Poisson")
return(withMathJax("$$X_t = X_0+\\sum_{i=0}^{N_t} Y_i \\; : \\;\\;\\; N_t \\sim Poi\\Bigl(\\int_0^t \\lambda(t)dt\\Bigl)$$"))
if (input$usr_modelClass=="Levy process")
return(withMathJax("$$dX_t = \\mu X_t \\; dt + \\sigma X_t \\; dW_t + X_t \\; dZ_t$$"))
})
observe({
if (input$usr_modelClass=="Fractional process") createAlert(session = session, anchorId = "panel_set_model_alert", alertId = "alert_fracinfo", style = "info", content = "Fractional process you set here will be available for simulation purposes, but not for estimation.")
else closeAlert(session = session, alertId = "alert_fracinfo")
})
output$usr_model_coeff <- renderUI({
if (input$usr_modelClass=="Diffusion process")
return(
div(align="center",
column(6, textInput("usr_model_coeff_drift", width = "70%", label = withMathJax("$$a(t,X,\\theta)$$"))),
column(6, textInput("usr_model_coeff_diff", width = "70%", label = withMathJax("$$b(t,X,\\theta)$$")))
)
)
if (input$usr_modelClass=="Fractional process")
return(
div(align="center",
column(6, textInput("usr_model_coeff_drift", width = "70%", label = withMathJax("$$a(t,X,\\theta)$$"))),
column(6, textInput("usr_model_coeff_diff", width = "70%", label = withMathJax("$$b(t,X,\\theta)$$")))
)
)
if (input$usr_modelClass=="Compound Poisson")
return(
div(align="center",
textInput("usr_model_coeff_intensity", width = "45%", label = withMathJax("$$\\lambda(t)$$"))
)
)
if (input$usr_modelClass=="Levy process")
return(
div(align="center",
fluidRow(column(12,textInput("usr_model_coeff_intensity", width = "45%", label = withMathJax("$$\\lambda(t)$$")))),
fluidRow(
column(6, textInput("usr_model_coeff_drift", width = "70%", label = withMathJax("$$a(t,X,\\theta)$$"))),
column(6, textInput("usr_model_coeff_diff", width = "70%", label = withMathJax("$$b(t,X,\\theta)$$")))
)
)
)
})
observeEvent(input$usr_model_button_save, {
entered <- FALSE
switch(input$usr_modelClass,
"Diffusion process" = {
if (input$usr_model_name!="" & (input$usr_model_coeff_drift!="" | input$usr_model_coeff_diff!="")){
mod <- try(setModel(drift = tolower(input$usr_model_coeff_drift), diffusion = tolower(input$usr_model_coeff_diff), solve.variable = "x"))
if(class(mod)!="try-error") yuimaGUIdata$usr_model[[input$usr_model_name]] <<- list(object=mod, class=input$usr_modelClass)
entered <- TRUE
}
},
"Fractional process" = {
if (input$usr_model_name!="" & (input$usr_model_coeff_drift!="" | input$usr_model_coeff_diff!="")){
mod <- try(setModel(drift = tolower(input$usr_model_coeff_drift), diffusion = tolower(input$usr_model_coeff_diff), hurst = NA, solve.variable = "x"))
if(class(mod)!="try-error") yuimaGUIdata$usr_model[[input$usr_model_name]] <<- list(object=mod, class=input$usr_modelClass)
entered <- TRUE
}
},
"Compound Poisson" = {
if (input$usr_model_name!="" & (input$usr_model_coeff_intensity!="")){
mod <- try(setPoisson(intensity = tolower(input$usr_model_coeff_intensity), df = "", solve.variable = "x"))
if(class(mod)!="try-error") yuimaGUIdata$usr_model[[input$usr_model_name]] <<- list(intensity=tolower(input$usr_model_coeff_intensity), class=input$usr_modelClass)
entered <- TRUE
}
},
"Levy process" = {
if (input$usr_model_name!=""){
mod <- try(setModel(drift=input$usr_model_coeff_drift, diffusion=input$usr_model_coeff_diff, measure.type = ifelse(is.na(input$usr_model_coeff_intensity), "code", "CP"), measure = list(intensity = input$usr_model_coeff_intensity, df = ""), solve.variable = "x"))
if(class(mod)!="try-error") yuimaGUIdata$usr_model[[input$usr_model_name]] <<- list(intensity=tolower(input$usr_model_coeff_intensity), drift = input$usr_model_coeff_drift, diffusion = input$usr_model_coeff_diff, class=input$usr_modelClass)
entered <- TRUE
}
}
)
if (entered){
yuimaGUIsettings$estimation[[input$usr_model_name]] <<- list()
closeAlert(session, "alert_savingModels")
if(class(mod)!="try-error") createAlert(session = session, anchorId = "panel_set_model_alert", alertId = "alert_savingModels", style = "success", content = "Model saved successfully")
else createAlert(session = session, anchorId = "panel_set_model_alert", alertId = "alert_savingModels", style = "error", content = "Model is not correctly specified")
}
})
observe({
for(mod in names(yuimaGUIsettings$estimation))
if (!(mod %in% c(names(yuimaGUIdata$usr_model), names(defaultModels))))
yuimaGUIsettings$estimation[[mod]] <<- list()
})
output$usr_model_saved <- renderUI({
if (length(names(yuimaGUIdata$usr_model))!=0)
selectInput("usr_model_saved", label = "Saved Models", choices = names(yuimaGUIdata$usr_model), selected = tail(names(yuimaGUIdata$usr_model),1))
})
output$usr_model_saved_latex <- renderUI({
input$usr_model_button_save
if (!is.null(input$usr_model_saved)) if (input$usr_model_saved %in% names(yuimaGUIdata$usr_model))
withMathJax(printModelLatex(input$usr_model_saved, process = yuimaGUIdata$usr_model[[input$usr_model_saved]]$class))
})
observeEvent(input$usr_model_button_delete, {
for (i in input$usr_model_saved)
yuimaGUIdata$usr_model[i] <<- NULL
})
observe({
shinyjs::toggle("usr_model_saved_div", condition = length(names(yuimaGUIdata$usr_model))!=0)
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/modeling/univariate_set_model.R |
###Model Input depending on Class Input
output$model <- renderUI({
choices <- as.vector(defaultModels[names(defaultModels)==input$modelClass])
if(input$modelClass!="Fractional process")
for(i in names(yuimaGUIdata$usr_model))
if (yuimaGUIdata$usr_model[[i]]$class==input$modelClass) {
if(input$modelClass!="Diffusion process") choices <- c(i, choices)
else if (length(getAllParams(mod = setModelByName(name = i), class = input$modelClass))!=0) choices <- c(i, choices)
}
return (selectInput("model",label = "Model Name", choices = choices, multiple = TRUE))
})
output$jumps <- renderUI({
if (input$modelClass=="Compound Poisson")
return(selectInput("jumps",label = "Jumps", choices = defaultJumps))
if (input$modelClass=="Levy process"){
jump_choices <- defaultJumps
jump_sel <- NULL
if(!is.null(input$model)){
if(input$model=="Geometric Brownian Motion with Jumps") jump_sel <- "Gaussian"
}
return(div(
column(6,selectInput("model_levy_intensity", label = "Intensity", choices = c(#"None",
"Constant"="lambda"))),
column(6,selectInput("jumps",label = "Jumps", choices = jump_choices, selected = jump_sel)))
)
}
})
output$pq_C <- renderUI({
if (input$modelClass=="CARMA")
return(div(
column(6,numericInput("AR_C",label = "AR degree (p)", value = 2, min = 1, step = 1)),
column(6,numericInput("MA_C",label = "MA degree (q)", value = 1, min = 1, step = 1))
))
if (input$modelClass=="COGARCH")
return(div(
column(6,numericInput("AR_C",label = "AR degree (p)", value = 1, min = 1, step = 1)),
column(6,numericInput("MA_C",label = "MA degree (q)", value = 1, min = 1, step = 1))
))
})
###Print last selected model in Latex
output$PrintModelLatex <- renderUI({
shinyjs::hide("titlePrintModelLatex")
if (!is.null(input$model)){
shinyjs::show("titlePrintModelLatex")
class <- isolate({input$modelClass})
return(withMathJax(printModelLatex(names = input$model, process = class, jumps = jumps_shortcut(class = class, jumps = input$jumps))))
}
})
###Display available data
output$database3 <- DT::renderDataTable(options=list(scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', selection = "multiple", rownames = FALSE,{
if (length(yuimaGUItable$series)==0){
NoData <- data.frame("Symb"=NA,"Please load some data first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$series)
})
###Table of selected data to model
seriesToEstimate <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$buttonSelect_models_Univariate, priority = 1, {
seriesToEstimate$table <<- rbind(seriesToEstimate$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$database3_rows_selected]) & !(rownames(yuimaGUItable$series) %in% rownames(seriesToEstimate$table)),])
})
###SelectAll Button
observeEvent(input$buttonSelectAll_models_Univariate, priority = 1, {
seriesToEstimate$table <<- rbind(seriesToEstimate$table, yuimaGUItable$series[(rownames(yuimaGUItable$series) %in% rownames(yuimaGUItable$series)[input$database3_rows_all]) & !(rownames(yuimaGUItable$series) %in% rownames(seriesToEstimate$table)),])
})
###Display Selected Data
output$database4 <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = FALSE, selection = "multiple",{
if (nrow(seriesToEstimate$table)==0){
NoData <- data.frame("Symb"=NA,"Select from table beside"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (seriesToEstimate$table)
})
###Control selected data to be in yuimaGUIdata$series
observe({
if(length(seriesToEstimate$table)!=0){
if (length(yuimaGUItable$series)==0)
seriesToEstimate$table <<- data.frame()
else
seriesToEstimate$table <<- seriesToEstimate$table[which(as.character(seriesToEstimate$table[,"Symb"]) %in% as.character(yuimaGUItable$series[,"Symb"])),]
}
})
###Delete Button
observeEvent(input$buttonDelete_models_Univariate, priority = 1,{
if (!is.null(input$database4_rows_selected))
seriesToEstimate$table <<- seriesToEstimate$table[-input$database4_rows_selected,]
})
###DeleteAll Button
observeEvent(input$buttonDeleteAll_models_Univariate, priority = 1,{
if (!is.null(input$database4_rows_all))
seriesToEstimate$table <<- seriesToEstimate$table[-input$database4_rows_all,]
})
###Interactive range of selectRange chart
range_selectRange <- reactiveValues(x=NULL, y=NULL)
observe({
if (!is.null(input$selectRange_brush) & !is.null(input$plotsRangeSeries)){
data <- getData(input$plotsRangeSeries)
test <- (length(index(window(data, start = input$selectRange_brush$xmin, end = input$selectRange_brush$xmax))) > 3)
if (test==TRUE){
range_selectRange$x <- c(as.Date(input$selectRange_brush$xmin), as.Date(input$selectRange_brush$xmax))
range_selectRange$y <- c(input$selectRange_brush$ymin, input$selectRange_brush$ymax)
}
}
})
observe({
shinyjs::toggle(id="plotsRangeErrorMessage", condition = nrow(seriesToEstimate$table)==0)
shinyjs::toggle(id="plotsRangeAll", condition = nrow(seriesToEstimate$table)!=0)
})
###Display charts: series and its increments
observe({
symb <- input$plotsRangeSeries
if(!is.null(symb))
if (symb %in% rownames(yuimaGUItable$series)){
data <- getData(symb)
incr <- na.omit(Delt(data, type = "arithmetic"))
condition <- all(is.finite(incr))
shinyjs::toggle("selectRangeReturns", condition = condition)
range_selectRange$x <- NULL
range_selectRange$y <- NULL
start <- as.character(seriesToEstimate$table[input$plotsRangeSeries,"From"])
end <- as.character(seriesToEstimate$table[input$plotsRangeSeries,"To"])
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
output$selectRange <- renderPlot({
if ((symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(seriesToEstimate$table)))){
par(bg="black")
plot.zoo(window(data, start = range_selectRange$x[1], end = range_selectRange$x[2]), main=symb, xlab="Index", ylab=NA, log=switch(input$scale_selectRange,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(data, start = start, end = end), col = "green")
grid(col="grey")
}
})
output$selectRangeReturns <- renderPlot({
if (symb %in% rownames(yuimaGUItable$series) & (symb %in% rownames(seriesToEstimate$table)) & condition){
par(bg="black")
plot.zoo( window(incr, start = range_selectRange$x[1], end = range_selectRange$x[2]), main=paste(symb, " - Percentage Increments"), xlab="Index", ylab=NA, log=switch(input$scale_selectRange,"Linear"="","Logarithmic (Y)"="", "Logarithmic (X)"="x", "Logarithmic (XY)"="x"), col="grey", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(window(incr, start = start, end = end), col = "green")
grid(col="grey")
}
})
}
})
output$plotsRangeSeries <- renderUI({
selectInput("plotsRangeSeries", label = "Series", choices = rownames(seriesToEstimate$table), selected = input$plotsRangeSeries)
})
###Choose Range input set to "Select range from charts" if charts have been brushed
output$chooseRange <- renderUI({
sel <- "full"
if (!is.null(range_selectRange$x)) sel <- "selected"
selectInput("chooseRange", label = "Range", choices = c("Full Range" = "full", "Select Range from Charts" = "selected", "Specify Range" = "specify"), selected = sel)
})
output$chooseRange_specify <- renderUI({
if(!is.null(input$plotsRangeSeries)) {
data <- getData(input$plotsRangeSeries)
if(class(index(data))=="numeric")
return(div(
column(6,numericInput("chooseRange_specify_t0", label = "From", min = start(data), max = end(data), value = start(data))),
column(6,numericInput("chooseRange_specify_t1", label = "To", min = start(data), max = end(data), value = end(data)))
))
if(class(index(data))=="Date")
return(dateRangeInput("chooseRange_specify_date", start = start(data), end = end(data), label = "Specify Range"))
}
})
observe({
shinyjs::toggle(id = "chooseRange_specify", condition = (input$chooseRange)=="specify")
})
###Function to update data range to use to estimate models
updateRange_seriesToEstimate <- function(symb, range = c("full","selected","specify"), type = c("Date", "numeric")){
for (i in symb){
data <- getData(i)
if (range == "full"){
levels(seriesToEstimate$table[,"From"]) <- c(levels(seriesToEstimate$table[,"From"]), as.character(start(data)))
levels(seriesToEstimate$table[,"To"]) <- c(levels(seriesToEstimate$table[,"To"]), as.character(end(data)))
seriesToEstimate$table[i,"From"] <<- as.character(start(data))
seriesToEstimate$table[i,"To"] <<- as.character(end(data))
}
if (range == "selected"){
if(!is.null(range_selectRange$x) & class(index(data))==type){
start <- range_selectRange$x[1]
end <- range_selectRange$x[2]
if(class(index(data))=="numeric"){
start <- as.numeric(start)
end <- as.numeric(end)
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(seriesToEstimate$table[,"From"]) <- c(levels(seriesToEstimate$table[,"From"]), as.character(start))
levels(seriesToEstimate$table[,"To"]) <- c(levels(seriesToEstimate$table[,"To"]), as.character(end))
seriesToEstimate$table[i,"From"] <<- as.character(start)
seriesToEstimate$table[i,"To"] <<- as.character(end)
}
}
if (range == "specify"){
if(class(index(data))==type){
if(class(index(data))=="Date"){
start <- input$chooseRange_specify_date[1]
end <- input$chooseRange_specify_date[2]
}
if(class(index(data))=="numeric"){
start <- input$chooseRange_specify_t0
end <- input$chooseRange_specify_t1
}
start <- max(start(data),start)
end <- min(end(data), end)
levels(seriesToEstimate$table[,"From"]) <- c(levels(seriesToEstimate$table[,"From"]), as.character(start))
levels(seriesToEstimate$table[,"To"]) <- c(levels(seriesToEstimate$table[,"To"]), as.character(end))
seriesToEstimate$table[i,"From"] <<- as.character(start)
seriesToEstimate$table[i,"To"] <<- as.character(end)
}
}
}
}
###Apply selected range by double click
observeEvent(input$selectRange_dbclick, priority = 1, {
updateRange_seriesToEstimate(input$plotsRangeSeries, range = "selected", type = class(index(getData(input$plotsRangeSeries))))
})
###Apply selected range
observeEvent(input$buttonApplyRange, priority = 1, {
updateRange_seriesToEstimate(input$plotsRangeSeries, range = input$chooseRange, type = class(index(getData(input$plotsRangeSeries))))
})
###ApplyAll selected range
observeEvent(input$buttonApplyAllRange, priority = 1, {
updateRange_seriesToEstimate(rownames(seriesToEstimate$table), range = input$chooseRange, type = class(index(getData(input$plotsRangeSeries))))
})
prev_buttonDelta <- 0
prev_buttonAllDelta <- 0
observe({
class <- isolate({input$modelClass})
for (symb in rownames(seriesToEstimate$table)){
if (is.null(yuimaGUIsettings$delta[[symb]])) {
i <- index(getData(symb))
if(is.numeric(i)) yuimaGUIsettings$delta[[symb]] <<- mode(diff(i))
else yuimaGUIsettings$delta[[symb]] <<- 0.01
}
if (is.null(yuimaGUIsettings$toLog[[symb]])) yuimaGUIsettings$toLog[[symb]] <<- FALSE
data <- getData(symb)
if (yuimaGUIsettings$toLog[[symb]]==TRUE) data <- log(data)
for (modName in input$model){
if (class(try(setModelByName(modName, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = class, jumps = input$jumps), AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA))))!="try-error"){
if (is.null(yuimaGUIsettings$estimation[[modName]]))
yuimaGUIsettings$estimation[[modName]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]]))
yuimaGUIsettings$estimation[[modName]][[symb]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["fixed"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["fixed"]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["start"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["start"]] <<- list()
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]] <<- setThreshold(class = class, data = data)
startMinMax <- defaultBounds(name = modName,
jumps = jumps_shortcut(class = class, jumps = input$jumps),
intensity = input$model_levy_intensity,
threshold = yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]],
AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA),
strict = FALSE,
data = data,
delta = yuimaGUIsettings$delta[[symb]])
upperLower <- defaultBounds(name = modName,
jumps = jumps_shortcut(class = class, jumps = input$jumps),
intensity = input$model_levy_intensity,
threshold = yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]],
AR_C = ifelse(class %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(class %in% c("CARMA","COGARCH"), input$MA_C, NA),
strict = TRUE,
data = data,
delta = yuimaGUIsettings$delta[[symb]])
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["startMin"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["startMin"]] <<- startMinMax$lower
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["startMax"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["startMax"]] <<- startMinMax$upper
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["upper"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["upper"]] <<- upperLower$upper
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["lower"]]) | !(class %in% c("Diffusion process", "Fractional process")) | prev_buttonDelta!=input$advancedSettingsButtonApplyDelta | prev_buttonAllDelta!=input$advancedSettingsButtonApplyAllDelta)
yuimaGUIsettings$estimation[[modName]][[symb]][["lower"]] <<- upperLower$lower
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["method"]])){
if(class=="COGARCH" | class=="CARMA") yuimaGUIsettings$estimation[[modName]][[symb]][["method"]] <<- "SANN"
else yuimaGUIsettings$estimation[[modName]][[symb]][["method"]] <<- "L-BFGS-B"
}
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["trials"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["trials"]] <<- 1
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["seed"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["seed"]] <<- NA
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["joint"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["joint"]] <<- FALSE
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["aggregation"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["aggregation"]] <<- TRUE
if (is.null(yuimaGUIsettings$estimation[[modName]][[symb]][["timeout"]]))
yuimaGUIsettings$estimation[[modName]][[symb]][["timeout"]] <<- Inf
}
}
}
prev_buttonDelta <<- input$advancedSettingsButtonApplyDelta
prev_buttonAllDelta <<- input$advancedSettingsButtonApplyAllDelta
})
observe({
valid <- TRUE
if (nrow(seriesToEstimate$table)==0 | is.null(input$model)) valid <- FALSE
else for(mod in input$model) if (class(try(setModelByName(mod, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$modelClass, jumps = input$jumps), AR_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))))=="try-error") valid <- FALSE
shinyjs::toggle(id="advancedSettingsAll", condition = valid)
shinyjs::toggle(id="advancedSettingsErrorMessage", condition = !valid)
})
output$advancedSettingsSeries <- renderUI({
if (nrow(seriesToEstimate$table)!=0)
selectInput(inputId = "advancedSettingsSeries", label = "Series", choices = rownames(seriesToEstimate$table))
})
output$advancedSettingsDelta <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
return (numericInput("advancedSettingsDelta", label = paste("delta", input$advancedSettingsSeries), value = yuimaGUIsettings$delta[[input$advancedSettingsSeries]], min = 0))
})
output$advancedSettingsToLog <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries)){
choices <- FALSE
if (all(getData(input$advancedSettingsSeries)>0)) choices <- c(FALSE, TRUE)
return (selectInput("advancedSettingsToLog", label = "Convert to log", choices = choices, selected = yuimaGUIsettings$toLog[[input$advancedSettingsSeries]]))
}
})
output$advancedSettingsModel <- renderUI({
if(!is.null(input$model))
selectInput(inputId = "advancedSettingsModel", label = "Model", choices = input$model)
})
output$advancedSettingsParameter <- renderUI({
if (!is.null(input$model))
if (!is.null(input$advancedSettingsModel)){
mod <- setModelByName(input$advancedSettingsModel, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$modelClass, jumps = input$jumps), AR_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))
par <- getAllParams(mod, input$modelClass)
selectInput(inputId = "advancedSettingsParameter", label = "Parameter", choices = par)
}
})
#REMOVE# output$advancedSettingsFixed <- renderUI({
#REMOVE# if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
#REMOVE# numericInput(inputId = "advancedSettingsFixed", label = "fixed", value = ifelse(is.null(yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["fixed"]][[input$advancedSettingsParameter]]),NA,yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["fixed"]][[input$advancedSettingsParameter]]))
#REMOVE#})
output$advancedSettingsStart <- renderUI({
if (#REMOVE# !is.null(input$advancedSettingsFixed) &
!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
#REMOVE# if (is.na(input$advancedSettingsFixed))
numericInput(inputId = "advancedSettingsStart", label = "start", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["start"]][[input$advancedSettingsParameter]])
})
output$advancedSettingsStartMin <- renderUI({
input$advancedSettingsButtonApplyDelta
input$advancedSettingsButtonApplyAllDelta
if (#REMOVE# !is.null(input$advancedSettingsFixed) &
!is.null(input$advancedSettingsStart) & !is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
if (#REMOVE# is.na(input$advancedSettingsFixed) &
is.na(input$advancedSettingsStart))
numericInput(inputId = "advancedSettingsStartMin", label = "start: Min", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["startMin"]][[input$advancedSettingsParameter]])
})
output$advancedSettingsStartMax <- renderUI({
input$advancedSettingsButtonApplyDelta
input$advancedSettingsButtonApplyAllDelta
if (#REMOVE# !is.null(input$advancedSettingsFixed) &
!is.null(input$advancedSettingsStart) & !is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
if (#REMOVE# is.na(input$advancedSettingsFixed) &
is.na(input$advancedSettingsStart))
numericInput(inputId = "advancedSettingsStartMax", label = "start: Max", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["startMax"]][[input$advancedSettingsParameter]])
})
output$advancedSettingsLower <- renderUI({
if (#REMOVE# !is.null(input$advancedSettingsFixed) &
!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
#REMOVE# if (is.na(input$advancedSettingsFixed))
if (input$advancedSettingsMethod=="L-BFGS-B" | input$advancedSettingsMethod=="Brent")
numericInput("advancedSettingsLower", label = "lower", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["lower"]][[input$advancedSettingsParameter]])
})
output$advancedSettingsUpper <- renderUI({
if (#REMOVE# !is.null(input$advancedSettingsFixed) &
!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsParameter))
#REMOVE# if (is.na(input$advancedSettingsFixed))
if (input$advancedSettingsMethod=="L-BFGS-B" | input$advancedSettingsMethod=="Brent")
numericInput("advancedSettingsUpper", label = "upper", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["upper"]][[input$advancedSettingsParameter]])
})
#REMOVE# output$advancedSettingsJoint <- renderUI({
#REMOVE# if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
#REMOVE# selectInput("advancedSettingsJoint", label = "joint", choices = c(FALSE, TRUE), selected = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["joint"]])
#REMOVE# })
output$advancedSettingsMethod <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
selectInput("advancedSettingsMethod", label = "method", choices = c("L-BFGS-B", "Nelder-Mead", "BFGS", "CG", "SANN", "Brent"), selected = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["method"]])
})
#REMOVE# output$advancedSettingsAggregation <- renderUI({
#REMOVE# if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
#REMOVE# selectInput("advancedSettingsAggregation", label = "aggregation", choices = c(TRUE, FALSE), selected = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["aggregation"]])
#REMOVE# })
output$advancedSettingsThreshold <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries)) if(isolate({input$modelClass})=="Levy process")
numericInput("advancedSettingsThreshold", label = "threshold", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["threshold"]])
})
output$advancedSettingsTrials <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries) & !is.null(input$advancedSettingsMethod))
numericInput("advancedSettingsTrials", label = "trials", min = 1, value = ifelse(input$advancedSettingsMethod=="SANN" & yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["method"]]!="SANN",1,yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["trials"]]))
})
output$advancedSettingsSeed <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
numericInput("advancedSettingsSeed", label = "seed", min = 1, value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["seed"]])
})
output$advancedSettingsTimeout <- renderUI({
if (!is.null(input$advancedSettingsModel) & !is.null(input$advancedSettingsSeries))
numericInput("advancedSettingsTimeout", label = "timeout (s)", value = yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["timeout"]])
})
observeEvent(input$advancedSettingsButtonApplyDelta, {
yuimaGUIsettings$delta[[input$advancedSettingsSeries]] <<- input$advancedSettingsDelta
yuimaGUIsettings$toLog[[input$advancedSettingsSeries]] <<- input$advancedSettingsToLog
})
observeEvent(input$advancedSettingsButtonApplyAllDelta, {
for (symb in rownames(seriesToEstimate$table)){
yuimaGUIsettings$delta[[symb]] <<- input$advancedSettingsDelta
if (input$advancedSettingsToLog==FALSE) yuimaGUIsettings$toLog[[symb]] <<- input$advancedSettingsToLog
else if (all(getData(symb)>0)) yuimaGUIsettings$toLog[[symb]] <<- input$advancedSettingsToLog
}
})
observeEvent(input$advancedSettingsButtonApplyModel,{
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["fixed"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsFixed
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["start"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStart
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["startMin"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStartMin
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["startMax"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStartMax
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["lower"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsLower
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["upper"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsUpper
})
observeEvent(input$advancedSettingsButtonApplyAllModel,{
for (symb in rownames(seriesToEstimate$table)){
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["fixed"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsFixed
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["start"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStart
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["startMin"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStartMin
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["startMax"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsStartMax
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["lower"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsLower
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["upper"]][[input$advancedSettingsParameter]] <<- input$advancedSettingsUpper
}
})
observeEvent(input$advancedSettingsButtonApplyGeneral,{
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["method"]] <<- input$advancedSettingsMethod
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["trials"]] <<- input$advancedSettingsTrials
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["seed"]] <<- input$advancedSettingsSeed
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["joint"]] <<- input$advancedSettingsJoint
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["aggregation"]] <<- input$advancedSettingsAggregation
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["threshold"]] <<- input$advancedSettingsThreshold
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[input$advancedSettingsSeries]][["timeout"]] <<- input$advancedSettingsTimeout
})
observeEvent(input$advancedSettingsButtonApplyAllModelGeneral,{
for (symb in rownames(seriesToEstimate$table)){
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["method"]] <<- input$advancedSettingsMethod
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["trials"]] <<- input$advancedSettingsTrials
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["seed"]] <<- input$advancedSettingsSeed
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["joint"]] <<- input$advancedSettingsJoint
#REMOVE# yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["aggregation"]] <<- input$advancedSettingsAggregation
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["threshold"]] <<- input$advancedSettingsThreshold
yuimaGUIsettings$estimation[[input$advancedSettingsModel]][[symb]][["timeout"]] <<- input$advancedSettingsTimeout
}
})
observeEvent(input$advancedSettingsButtonApplyAllGeneral,{
for (mod in input$model){
for (symb in rownames(seriesToEstimate$table)){
yuimaGUIsettings$estimation[[mod]][[symb]][["method"]] <<- input$advancedSettingsMethod
yuimaGUIsettings$estimation[[mod]][[symb]][["trials"]] <<- input$advancedSettingsTrials
yuimaGUIsettings$estimation[[mod]][[symb]][["seed"]] <<- input$advancedSettingsSeed
#REMOVE# yuimaGUIsettings$estimation[[mod]][[symb]][["joint"]] <<- input$advancedSettingsJoint
#REMOVE# yuimaGUIsettings$estimation[[mod]][[symb]][["aggregation"]] <<- input$advancedSettingsAggregation
yuimaGUIsettings$estimation[[mod]][[symb]][["threshold"]] <<- input$advancedSettingsThreshold
yuimaGUIsettings$estimation[[mod]][[symb]][["timeout"]] <<- input$advancedSettingsTimeout
}
}
})
observe({
closeAlert(session = session, alertId = "CARMA_COGARCH_err")
if(!is.null(input$modelClass)) if(input$modelClass=="CARMA" ) if(!is.null(input$AR_C)) if(!is.null(input$MA_C)) if(!is.na(input$AR_C) & !is.na(input$MA_C)) {
if(input$AR_C<=input$MA_C)
createAlert(session = session, anchorId = "panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR degree (p) must be greater than MA degree (q)")
if(input$AR_C== 0 | input$MA_C==0)
createAlert(session = session, anchorId = "panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR and MA degree (p,q) must be positive")
}
if(!is.null(input$modelClass)) if(input$modelClass=="COGARCH" ) if(!is.null(input$AR_C)) if(!is.null(input$MA_C)) if(!is.na(input$AR_C) & !is.na(input$MA_C)) {
if(input$AR_C<input$MA_C)
createAlert(session = session, anchorId = "panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR degree (p) must be greater than or equal to MA degree (q)")
if(input$AR_C== 0 | input$MA_C==0)
createAlert(session = session, anchorId = "panel_run_estimation_alert", alertId = "CARMA_COGARCH_err", style = "error", content = "AR and MA degree (p,q) must be positive")
}
})
###Estimate models
observeEvent(input$EstimateModels,{
closeAlert(session = session, alertId = "modelsErr")
valid <- TRUE
if(is.null(input$model) | nrow(seriesToEstimate$table)==0) valid <- FALSE
else if (input$modelClass=="Compound Poisson" & is.null(input$jumps)) valid <- FALSE
else for(mod in input$model) if (class(try(setModelByName(mod, intensity = input$model_levy_intensity, jumps = jumps_shortcut(class = input$modelClass, jumps = input$jumps), AR_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA), MA_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA))))=="try-error") valid <- FALSE
if(!valid){
createAlert(session = session, anchorId = "panel_run_estimation_alert", alertId = "modelsAlert_err", content = "Select some series and (valid) models to estimate", style = "warning")
}
if(valid){
withProgress(message = 'Estimating: ',{
for (modName in input$model){
for (i in rownames(seriesToEstimate$table)){
symb <- as.character(seriesToEstimate$table[i,"Symb"])
incProgress(1/(length(input$model)*nrow(seriesToEstimate$table)), detail = paste(symb,"-",modName))
data <- getData(symb)
start <- as.character(seriesToEstimate$table[i,"From"])
end <- as.character(seriesToEstimate$table[i,"To"])
times <- index(data)
if (class(times)=="numeric")
data <- data[(times >= as.numeric(start)) & (times <= as.numeric(end)), , drop = FALSE]
else
data <- data[(times >= start) & (times <= end), , drop = FALSE]
addModel(
timeout = ifelse(is.na(yuimaGUIsettings$estimation[[modName]][[symb]][["timeout"]]), Inf, yuimaGUIsettings$estimation[[modName]][[symb]][["timeout"]]),
modName = modName,
modClass = input$modelClass,
intensity_levy = input$model_levy_intensity,
AR_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$AR_C, NA),
MA_C = ifelse(input$modelClass %in% c("CARMA","COGARCH"), input$MA_C, NA),
jumps = jumps_shortcut(class = input$modelClass, jumps = input$jumps),
symbName = symb,
data = data,
delta = yuimaGUIsettings$delta[[symb]],
toLog = yuimaGUIsettings$toLog[[symb]],
start = yuimaGUIsettings$estimation[[modName]][[symb]][["start"]],
startMin = yuimaGUIsettings$estimation[[modName]][[symb]][["startMin"]],
startMax = yuimaGUIsettings$estimation[[modName]][[symb]][["startMax"]],
method=yuimaGUIsettings$estimation[[modName]][[symb]][["method"]],
trials=yuimaGUIsettings$estimation[[modName]][[symb]][["trials"]],
seed = yuimaGUIsettings$estimation[[modName]][[symb]][["seed"]],
fixed = yuimaGUIsettings$estimation[[modName]][[symb]][["fixed"]],
lower = yuimaGUIsettings$estimation[[modName]][[symb]][["lower"]],
upper = yuimaGUIsettings$estimation[[modName]][[symb]][["upper"]],
joint = yuimaGUIsettings$estimation[[modName]][[symb]][["joint"]],
aggregation = yuimaGUIsettings$estimation[[modName]][[symb]][["aggregation"]],
threshold = yuimaGUIsettings$estimation[[modName]][[symb]][["threshold"]],
session = session,
anchorId = "panel_estimates_alert",
alertId = NULL
)
}
}
})
updateTabsetPanel(session = session, inputId = "panel_estimates", selected = "Estimates")
}
})
observe({
valid <- TRUE
if(is.null(input$model) | nrow(seriesToEstimate$table)==0) valid <- FALSE
else if (input$modelClass=="Compound Poisson" & is.null(input$jumps)) valid <- FALSE
if(valid) closeAlert(session, alertId = "modelsAlert_err")
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/modeling/univariate_start_estimation.R |
#shinyapp web app
if(!exists("yuimaGUIdata"))
yuimaGUIdata <- reactiveValues(series=list(),
model=list(), multimodel=list(),
usr_model = list(), usr_multimodel = list(),
simulation=list(), multisimulation=list(),
usr_simulation = list(), usr_multisimulation = list(),
cp=list(),
cpYuima=list(),
llag = list(),
cluster = list(),
hedging = list())
yuimaGUItable <- reactiveValues(series=data.frame(),
model=data.frame(), multimodel=data.frame(),
simulation=data.frame(), multisimulation=data.frame(),
hedging=data.frame())
yuimaGUIsettings <- list(simulation = list(), estimation = list(), delta = list(), toLog = list())
output$saveSession <- {
downloadHandler(
filename = "session.yuimaGUI",
content = function(file) {
save("yuimaGUIdata", file = file)
}
)
}
observeEvent(input$loadSession, {
if (!is.null(input$loadSession$datapath)){
try(load(input$loadSession$datapath))
for(i in names(yuimaGUIdata)) yuimaGUIdata[[i]] <<- yuimaGUIdata[[i]]
}
})
observeEvent(yuimaGUIdata$series, priority = 10, {
yuimaGUItable$series <<- data.frame()
for (symb in names(yuimaGUIdata$series)){
test <- try(rbind(yuimaGUItable$series, data.frame(Symb = as.character(symb), From = as.character(start(yuimaGUIdata$series[[symb]])), To = as.character(end(yuimaGUIdata$series[[symb]])))))
if (class(test)!="try-error")
yuimaGUItable$series <<- test
else
yuimaGUIdata$series <<- yuimaGUIdata$series[-which(names(yuimaGUIdata$series)==symb)]
}
if (length(yuimaGUItable$series)!=0)
rownames(yuimaGUItable$series) <<- yuimaGUItable$series[,"Symb"]
})
observeEvent(yuimaGUIdata$model, priority = 10, {
yuimaGUItable$model <<- data.frame()
for (symb in names(yuimaGUIdata$model)){
for (i in 1:length(yuimaGUIdata$model[[symb]])){
newRow <- data.frame(
Symb = symb,
Class = yuimaGUIdata$model[[symb]][[i]]$info$class,
Model = yuimaGUIdata$model[[symb]][[i]]$info$modName,
Jumps = yuimaGUIdata$model[[symb]][[i]]$info$jumps,
From = as.character(start(yuimaGUIdata$model[[symb]][[i]]$model@[email protected])),
To = as.character(end(yuimaGUIdata$model[[symb]][[i]]$model@[email protected])),
AIC = yuimaGUIdata$model[[symb]][[i]]$aic,
BIC = yuimaGUIdata$model[[symb]][[i]]$bic,
stringsAsFactors = FALSE)
rownames(newRow) <- as.character(paste(symb," ", i, sep=""))
yuimaGUItable$model <<- rbind(yuimaGUItable$model, newRow)
}
}
})
observeEvent(yuimaGUIdata$multimodel, priority = 10, {
yuimaGUItable$multimodel <<- data.frame()
for (symb in names(yuimaGUIdata$multimodel)){
for (i in 1:length(yuimaGUIdata$multimodel[[symb]])){
newRow <- data.frame(
Symb = symb,
Class = yuimaGUIdata$multimodel[[symb]][[i]]$info$class,
Model = yuimaGUIdata$multimodel[[symb]][[i]]$info$modName,
Jumps = yuimaGUIdata$multimodel[[symb]][[i]]$info$jumps,
From = as.character(start(yuimaGUIdata$multimodel[[symb]][[i]]$model@[email protected])),
To = as.character(end(yuimaGUIdata$multimodel[[symb]][[i]]$model@[email protected])),
AIC = yuimaGUIdata$multimodel[[symb]][[i]]$aic,
BIC = yuimaGUIdata$multimodel[[symb]][[i]]$bic,
stringsAsFactors = FALSE)
rownames(newRow) <- as.character(paste(symb," ", i, sep=""))
yuimaGUItable$multimodel <<- rbind(yuimaGUItable$multimodel, newRow)
}
}
})
observeEvent(yuimaGUIdata$simulation, priority = 10, {
yuimaGUItable$simulation <<- data.frame()
for (symb in names(yuimaGUIdata$simulation)){
for (i in 1:length(yuimaGUIdata$simulation[[symb]])){
estimated.from <- NA
estimated.to <- NA
if (!is.null(yuimaGUIdata$simulation[[symb]][[i]]$model$model@[email protected])){
estimated.from <- as.character(start(yuimaGUIdata$simulation[[symb]][[i]]$model$model@[email protected]))
estimated.to <- as.character(end(yuimaGUIdata$simulation[[symb]][[i]]$model$model@[email protected]))
}
newRow <- data.frame(
"Symb" = symb,
"Class" = yuimaGUIdata$simulation[[symb]][[i]]$model$info$class,
"Model" = yuimaGUIdata$simulation[[symb]][[i]]$model$info$modName,
"Jumps" = yuimaGUIdata$simulation[[symb]][[i]]$model$info$jumps,
"N sim" = yuimaGUIdata$simulation[[symb]][[i]]$info$nsim,
"N step" = yuimaGUIdata$simulation[[symb]][[i]]$info$nstep,
"delta" = yuimaGUIdata$simulation[[symb]][[i]]$info$delta,
"Simulated from" = as.character(yuimaGUIdata$simulation[[symb]][[i]]$info$simulate.from),
"Simulated to" = as.character(yuimaGUIdata$simulation[[symb]][[i]]$info$simulate.to),
"Estimated from" = estimated.from,
"Estimated to" = estimated.to,
check.names = FALSE, stringsAsFactors = FALSE)
rownames(newRow) <- as.character(paste(symb," ", i, sep=""))
yuimaGUItable$simulation <<- rbind(yuimaGUItable$simulation, newRow)
}
}
})
observeEvent(yuimaGUIdata$multisimulation, priority = 10, {
yuimaGUItable$multisimulation <<- data.frame()
for (symb in names(yuimaGUIdata$multisimulation)){
for (i in 1:length(yuimaGUIdata$multisimulation[[symb]])){
estimated.from <- NA
estimated.to <- NA
if (!is.null(yuimaGUIdata$multisimulation[[symb]][[i]]$model$model@[email protected])){
estimated.from <- as.character(start(yuimaGUIdata$multisimulation[[symb]][[i]]$model$model@[email protected]))
estimated.to <- as.character(end(yuimaGUIdata$multisimulation[[symb]][[i]]$model$model@[email protected]))
}
newRow <- data.frame(
"Symb" = symb,
"Class" = yuimaGUIdata$multisimulation[[symb]][[i]]$model$info$class,
"Model" = yuimaGUIdata$multisimulation[[symb]][[i]]$model$info$modName,
"Jumps" = yuimaGUIdata$multisimulation[[symb]][[i]]$model$info$jumps,
"N sim" = yuimaGUIdata$multisimulation[[symb]][[i]]$info$nsim,
"N step" = yuimaGUIdata$multisimulation[[symb]][[i]]$info$nstep,
"delta" = yuimaGUIdata$multisimulation[[symb]][[i]]$info$delta,
"Simulated from" = as.character(yuimaGUIdata$multisimulation[[symb]][[i]]$info$simulate.from),
"Simulated to" = as.character(yuimaGUIdata$multisimulation[[symb]][[i]]$info$simulate.to),
"Estimated from" = estimated.from,
"Estimated to" = estimated.to,
check.names = FALSE, stringsAsFactors = FALSE)
rownames(newRow) <- as.character(paste(symb," ", i, sep=""))
yuimaGUItable$multisimulation <<- rbind(yuimaGUItable$multisimulation, newRow)
}
}
})
observeEvent(yuimaGUIdata$series, priority = 10, {
n <- names(yuimaGUIdata$series)
for (i in names(yuimaGUIsettings$estimation)) if(!(i %in% n)) yuimaGUIsettings$estimation[[i]] <<- NULL
for (i in names(yuimaGUIsettings$delta)) if(!(i %in% n)) yuimaGUIsettings$delta[[i]] <<- NULL
})
observeEvent(yuimaGUIdata$hedging, priority = 10, {
yuimaGUItable$hedging <<- data.frame()
if (length(yuimaGUIdata$hedging)!=0){
for (i in 1:length(yuimaGUIdata$hedging)){
newRow <- data.frame(
"Symb" = yuimaGUIdata$hedging[[i]]$symb,
"Number of Simulations" = yuimaGUIdata$hedging[[i]]$info$nsim,
"Average Return (%)" = round(yuimaGUIdata$hedging[[i]]$info$profit*100,2),
"Option Lots_to_Buy" = yuimaGUIdata$hedging[[i]]$info$LotsToBuy,
"Assets to Buy" = yuimaGUIdata$hedging[[i]]$info$buy,
"Assets to Sell" = yuimaGUIdata$hedging[[i]]$info$sell,
"Asset Price" = yuimaGUIdata$hedging[[i]]$info$assPrice,
"Option Price" = yuimaGUIdata$hedging[[i]]$info$optPrice,
"Option Type" = yuimaGUIdata$hedging[[i]]$info$type,
"Strike" = yuimaGUIdata$hedging[[i]]$info$strike,
"Maturity" = yuimaGUIdata$hedging[[i]]$info$maturity,
"Lot Multiplier"=yuimaGUIdata$hedging[[i]]$info$optLotMult,
"Trading_Cost per Lot"=yuimaGUIdata$hedging[[i]]$info$optLotCost,
"Asset Trading_Cost (%)"=yuimaGUIdata$hedging[[i]]$info$assPercCost*100,
"Asset Min Trading_Cost"=yuimaGUIdata$hedging[[i]]$info$assMinCost,
"Asset Yearly_Short_Rate (%)"=yuimaGUIdata$hedging[[i]]$info$assRateShortSelling*100,
"Model" = yuimaGUIdata$hedging[[i]]$model$info$modName,
"Estimated from" = start(yuimaGUIdata$hedging[[i]]$model$model@[email protected]),
"Estimated to" = end(yuimaGUIdata$hedging[[i]]$model$model@[email protected]),
"AIC" = yuimaGUIdata$hedging[[i]]$model$aic,
"BIC" = yuimaGUIdata$hedging[[i]]$model$bic,
check.names = FALSE, stringsAsFactors = FALSE)
yuimaGUItable$hedging <<- rbind.fill(yuimaGUItable$hedging, newRow)
}
}
})
defaultModels <- c("Diffusion process"="Geometric Brownian Motion",
"Diffusion process"="Brownian Motion",
"Diffusion process"="Ornstein-Uhlenbeck (OU)",
"Diffusion process"="Vasicek model (VAS)",
"Diffusion process"="Constant elasticity of variance (CEV)",
"Diffusion process"= "Cox-Ingersoll-Ross (CIR)",
"Diffusion process"="Chan-Karolyi-Longstaff-Sanders (CKLS)",
"Diffusion process"="Hyperbolic (Barndorff-Nielsen)",
"Diffusion process"="Hyperbolic (Bibby and Sorensen)",
"Compound Poisson" = "Constant Intensity",
"Compound Poisson" = "Linear Intensity",
"Compound Poisson" = "Power Low Intensity",
"Compound Poisson" = "Exponentially Decaying Intensity",
"Compound Poisson" = "Periodic Intensity",
"Point Process" = "Hawkes",
"Point Process" = "Hawkes Power Law Kernel",
#"Fractional process"="Frac. Geometric Brownian Motion",
#"Fractional process"="Frac. Brownian Motion",
"Fractional process"="Frac. Ornstein-Uhlenbeck (OU)",
"CARMA" = "Carma(p,q)",
"COGARCH" = "Cogarch(p,q)",
"Levy process" = "Geometric Brownian Motion with Jumps"
)
defaultMultiModels <- c("Diffusion process" = "Correlated Brownian Motion")
defaultJumps <- c("Gaussian",
"Constant",
"Uniform",
"Student t",
"Variance Gamma",
"Inverse Gaussian",
"Normal Inverse Gaussian",
"Hyperbolic",
"Generalized Hyperbolic")
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/settings.R |
output$multi_simulate_databaseModels <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
if (length(yuimaGUItable$multimodel)==0){
NoData <- data.frame("Symb"=NA,"Please estimate some models first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$multimodel)
})
multi_modelsToSimulate <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$multi_simulate_button_selectModels, priority = 1, {
multi_modelsToSimulate$table <<- rbind.fill(multi_modelsToSimulate$table, yuimaGUItable$multimodel[(rownames(yuimaGUItable$multimodel) %in% rownames(yuimaGUItable$multimodel)[input$multi_simulate_databaseModels_rows_selected]) & !(rownames(yuimaGUItable$multimodel) %in% rownames(multi_modelsToSimulate$table)),])
})
###SelectAll Button
observeEvent(input$multi_simulate_button_selectAllModels, priority = 1, {
multi_modelsToSimulate$table <<- rbind.fill(multi_modelsToSimulate$table, yuimaGUItable$multimodel[(rownames(yuimaGUItable$multimodel) %in% rownames(yuimaGUItable$multimodel)[input$multi_simulate_databaseModels_rows_all]) & !(rownames(yuimaGUItable$multimodel) %in% rownames(multi_modelsToSimulate$table)),])
})
observe({
if("AIC" %in% colnames(multi_modelsToSimulate$table))
multi_modelsToSimulate$table[,"AIC"] <<- as.numeric(as.character(multi_modelsToSimulate$table[,"AIC"]))
if("BIC" %in% colnames(multi_modelsToSimulate$table))
multi_modelsToSimulate$table[,"BIC"] <<- as.numeric(as.character(multi_modelsToSimulate$table[,"BIC"]))
})
###Control selected models to be in yuimaGUIdata$multimodel or user defined
observe({
if(length(rownames(multi_modelsToSimulate$table))!=0){
names.valid <- c(names(yuimaGUIdata$usr_multisimulation), rownames(yuimaGUItable$multimodel))
col <- colnames(multi_modelsToSimulate$table)
updatedtable <- data.frame(multi_modelsToSimulate$table[which(rownames(multi_modelsToSimulate$table) %in% names.valid),], row.names = rownames(multi_modelsToSimulate$table)[rownames(multi_modelsToSimulate$table) %in% names.valid])
colnames(updatedtable) <- col
multi_modelsToSimulate$table <<- updatedtable
}
})
output$multi_simulate_selectedModels <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollX=TRUE, scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
if (length(rownames(multi_modelsToSimulate$table))==0){
NoData <- data.frame("Symb"=NA,"Please select models from the table above"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (multi_modelsToSimulate$table)
})
###Delete Button
observeEvent(input$multi_simulation_button_deleteModels, priority = 1,{
if (!is.null(input$multi_simulate_selectedModels_rows_selected))
multi_modelsToSimulate$table <<- multi_modelsToSimulate$table[-input$multi_simulate_selectedModels_rows_selected,]
})
###DeleteAll Button
observeEvent(input$multi_simulation_button_deleteAllModels, priority = 1,{
if (!is.null(input$multi_simulate_selectedModels_rows_all))
multi_modelsToSimulate$table <<- multi_modelsToSimulate$table[-input$multi_simulate_selectedModels_rows_all,]
})
observe({
shinyjs::toggle(id="multi_simulate_setSimulation_errorMessage", condition = length(rownames(multi_modelsToSimulate$table))==0)
shinyjs::toggle(id="multi_simulate_setSimulation_body", condition = length(rownames(multi_modelsToSimulate$table))!=0)
})
observe({
shinyjs::toggle(id="multi_simulate_advancedSettings_errorMessage", condition = length(rownames(multi_modelsToSimulate$table))==0)
shinyjs::toggle(id="multi_simulate_advancedSettings_body", condition = length(rownames(multi_modelsToSimulate$table))!=0)
})
observe({
for (modID in rownames(multi_modelsToSimulate$table)[input$multi_simulate_selectedModels_rows_all]){
if (modID %in% names(yuimaGUIdata$usr_multisimulation)){
if (is.null(yuimaGUIsettings$simulation[[modID]]))
yuimaGUIsettings$simulation[[modID]] <<- list()
if (is.null(yuimaGUIsettings$simulation[[modID]][["xinit"]])){
dim <- yuimaGUIdata$usr_multisimulation[[modID]]$Dimension
if(!is.null(dim))
yuimaGUIsettings$simulation[[modID]][["xinit"]] <<- as.data.frame(t(data.frame(rep(1, dim), row.names = paste("x", 1:dim, sep = ""))))
else {
###add other models
}
}
if (is.null(yuimaGUIsettings$simulation[[modID]][["nstep"]]))
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- 1000
if (is.null(yuimaGUIsettings$simulation[[modID]][["nsim"]]))
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["t0"]]))
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- 0
if (is.null(yuimaGUIsettings$simulation[[modID]][["t1"]]))
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["traj"]]))
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- TRUE
if (is.null(yuimaGUIsettings$simulation[[modID]][["seed"]]))
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- NA
}
if (modID %in% rownames(yuimaGUItable$multimodel)){
id <- unlist(strsplit(modID, split = " "))
if (is.null(yuimaGUIsettings$simulation[[modID]]))
yuimaGUIsettings$simulation[[modID]] <<- list()
if (is.null(yuimaGUIsettings$simulation[[modID]][["xinit"]])){
xinit <- last(as.xts(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))
toLog <- yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info$toLog
xinit[1,toLog==TRUE] <- exp(xinit[1,toLog==TRUE])
yuimaGUIsettings$simulation[[modID]][["xinit"]] <<- xinit
}
if (is.null(yuimaGUIsettings$simulation[[modID]][["nstep"]]))
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- NA
if (is.null(yuimaGUIsettings$simulation[[modID]][["nsim"]]))
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["t0"]]))
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- end(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected])
if (is.null(yuimaGUIsettings$simulation[[modID]][["t1"]]))
if(is.numeric(index(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected])))
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- 2*end(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected])-start(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected])
else
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- end(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected])+365
if (is.null(yuimaGUIsettings$simulation[[modID]][["traj"]]))
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- TRUE
if (is.null(yuimaGUIsettings$simulation[[modID]][["seed"]]))
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- NA
}
}
})
output$multi_simulate_modelID <- renderUI({
selectInput("multi_simulate_modelID", label = "Simulation ID", choices = rownames(multi_modelsToSimulate$table))
})
output$multi_simulate_advancedSettings_modelID <- renderUI({
selectInput("multi_simulate_advancedSettings_modelID", label = "Simulation ID", choices = rownames(multi_modelsToSimulate$table))
})
output$multi_simulate_seed <- renderUI({
if(!is.null(input$multi_simulate_advancedSettings_modelID))
numericInput("multi_simulate_seed", label = "RNG seed", step = 1, min = 0, value = yuimaGUIsettings$simulation[[input$multi_simulate_advancedSettings_modelID]][["seed"]])
})
output$multi_simulate_traj <- renderUI({
if(!is.null(input$multi_simulate_advancedSettings_modelID))
selectInput("multi_simulate_traj", label = "Save trajectory", choices = c(TRUE,FALSE), selected = yuimaGUIsettings$simulation[[input$multi_simulate_advancedSettings_modelID]][["traj"]])
})
output$multi_simulate_nsim <- renderUI({
if(!is.null(input$multi_simulate_modelID))
numericInput("multi_simulate_nsim", label = "Number of simulations", value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["nsim"]], min = 1, step = 1)
})
output$multi_simulate_nstep <- renderUI({
if(!is.null(input$multi_simulate_modelID)){
id <- unlist(strsplit(input$multi_simulate_modelID, split = " "))
if (input$multi_simulate_modelID %in% names(yuimaGUIdata$usr_multisimulation)){
numericInput("multi_simulate_nstep", label = "Number of steps per simulation", value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["nstep"]], min = 1, step = 1)
} else if (!(isolate({yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info$class}) %in% c("COGARCH", "CARMA")))
numericInput("multi_simulate_nstep", label = "Number of steps per simulation", value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["nstep"]], min = 1, step = 1)
}
})
output$multi_simulate_xinit_symb <- renderUI({
if(!is.null(input$multi_simulate_modelID))
selectInput("multi_simulate_xinit_symb", label = "Symb", choices = names(yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["xinit"]]))
})
observe({
if(!is.null(input$multi_simulate_modelID) & !is.null(input$multi_simulate_xinit_symb)) if (input$multi_simulate_xinit_symb %in% names(yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["xinit"]]))
output$multi_simulate_xinit <- renderUI({
isolate({numericInput("multi_simulate_xinit", label = "Initial value", value = as.numeric(yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["xinit"]][,input$multi_simulate_xinit_symb]))})
})
})
output$multi_simulate_range <- renderUI({
if(!is.null(input$multi_simulate_modelID)){
if (input$multi_simulate_modelID %in% names(yuimaGUIdata$usr_multisimulation)){
return(div(
column(6,numericInput("multi_simulate_rangeNumeric_t0", label = "From", min = 0 ,value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]])),
column(6,numericInput("multi_simulate_rangeNumeric_t1", label = "To", min = 0, value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]]))
))
}
if (input$multi_simulate_modelID %in% rownames(yuimaGUItable$multimodel)){
id <- unlist(strsplit(input$multi_simulate_modelID, split = " "))
if (class(index(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date")
dateRangeInput("multi_simulate_rangeDate", label = "Simulation interval", start = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]], end = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]])
else{
div(
column(6,numericInput("multi_simulate_rangeNumeric_t0", label = "From", min = 0 ,value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]])),
column(6,numericInput("multi_simulate_rangeNumeric_t1", label = "To", min = 0, value = yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]]))
)
}
}
}
})
observeEvent(input$multi_simulate_button_apply_advancedSettings, {
yuimaGUIsettings$simulation[[input$multi_simulate_advancedSettings_modelID]][["seed"]] <<- input$multi_simulate_seed
yuimaGUIsettings$simulation[[input$multi_simulate_advancedSettings_modelID]][["traj"]] <<- input$multi_simulate_traj
})
observeEvent(input$multi_simulate_button_applyAll_advancedSettings, {
for (modID in rownames(multi_modelsToSimulate$table)){
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- input$multi_simulate_seed
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- input$multi_simulate_traj
}
})
observeEvent(input$multi_simulate_button_apply_nsim, {
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["nsim"]] <<- input$multi_simulate_nsim
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["nstep"]] <<- input$multi_simulate_nstep
})
observeEvent(input$multi_simulate_button_applyAll_nsim, {
for (modID in rownames(multi_modelsToSimulate$table)){
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- input$multi_simulate_nsim
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- input$multi_simulate_nstep
}
})
observeEvent(input$multi_simulate_button_apply_xinit, {
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["xinit"]][,input$multi_simulate_xinit_symb] <<- input$multi_simulate_xinit
})
observeEvent(input$multi_simulate_button_applyAll_xinit, {
for (modID in rownames(multi_modelsToSimulate$table))
if(input$multi_simulate_xinit_symb %in% names(yuimaGUIsettings$simulation[[modID]][["xinit"]]))
yuimaGUIsettings$simulation[[modID]][["xinit"]][,input$multi_simulate_xinit_symb] <<- input$multi_simulate_xinit
})
observeEvent(input$multi_simulate_button_apply_range, {
if (input$multi_simulate_modelID %in% names(yuimaGUIdata$usr_multisimulation)){
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]] <<- input$multi_simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]] <<- input$multi_simulate_rangeNumeric_t1
}
if (input$multi_simulate_modelID %in% rownames(yuimaGUItable$multimodel)){
id <- unlist(strsplit(input$multi_simulate_modelID, split = " "))
if (class(index(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date"){
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]] <<- input$multi_simulate_rangeDate[1]
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]] <<- input$multi_simulate_rangeDate[2]
}
else{
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t0"]] <<- input$multi_simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[input$multi_simulate_modelID]][["t1"]] <<- input$multi_simulate_rangeNumeric_t1
}
}
})
observeEvent(input$multi_simulate_button_applyAll_range, {
for (modID in rownames(multi_modelsToSimulate$table)){
if (modID %in% names(yuimaGUIdata$usr_multisimulation)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$multi_simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$multi_simulate_rangeNumeric_t1
}
if (modID %in% rownames(yuimaGUItable$multimodel)){
id <- unlist(strsplit(modID, split = " "))
if (class(index(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date" & !is.null(input$multi_simulate_rangeDate)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$multi_simulate_rangeDate[1]
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$multi_simulate_rangeDate[2]
}
if (class(index(yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="numeric" & !is.null(input$multi_simulate_rangeNumeric_t0)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$multi_simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$multi_simulate_rangeNumeric_t1
}
}
}
})
observe({
if (!is.null(multi_modelsToSimulate$table)) if (nrow(multi_modelsToSimulate$table)!=0) {
closeAlert(session, alertId = "multi_simulate_alert_buttonEstimate1")
closeAlert(session, alertId = "multi_simulate_alert_buttonEstimate2")
}
})
observeEvent(input$multi_simulate_simulateModels, {
if (is.null(multi_modelsToSimulate$table)) {
if (input$panel_multi_simulations=="Estimated models") createAlert(session = session, anchorId = "panel_multi_simulate_model_alert", alertId = "multi_simulate_alert_buttonEstimate1", content = "Table 'Selected Models' is empty", style = "warning")
if (input$panel_multi_simulations=="Non-estimated models") createAlert(session = session, anchorId = "panel_multi_simulate_equation_alert", alertId = "multi_simulate_alert_buttonEstimate2", content = "Table 'Selected Models' is empty", style = "warning")
} else if (nrow(multi_modelsToSimulate$table)==0) {
if (input$panel_multi_simulations=="Estimated models") createAlert(session = session, anchorId = "panel_multi_simulate_model_alert", alertId = "multi_simulate_alert_buttonEstimate1", content = "Table 'Selected Models' is empty", style = "warning")
if (input$panel_multi_simulations=="Non-estimated models") createAlert(session = session, anchorId = "panel_multi_simulate_equation_alert", alertId = "multi_simulate_alert_buttonEstimate2", content = "Table 'Selected Models' is empty", style = "warning")
}
else{
withProgress(message = 'Simulating: ', value = 0, {
for (modID in rownames(multi_modelsToSimulate$table)){
if(modID %in% names(yuimaGUIdata$usr_multisimulation)){
incProgress(1/nrow(multi_modelsToSimulate$table), detail = paste(modID,"-",yuimaGUIdata$usr_multisimulation[[modID]][["Model"]]))
jumps <- ifelse(is.null(yuimaGUIdata$usr_multisimulation[[modID]][["Jumps"]]),NA, yuimaGUIdata$usr_multisimulation[[modID]][["Jumps"]])
dimension <- ifelse(is.null(yuimaGUIdata$usr_multisimulation[[modID]][["Dimension"]]),NA, yuimaGUIdata$usr_multisimulation[[modID]][["Dimension"]])
modName <- yuimaGUIdata$usr_multisimulation[[modID]][["Model"]]
modelYuimaGUI <- list(model = setYuima(model = setModelByName(name = modName, jumps = jumps, dimension = dimension)),
info = list(class = yuimaGUIdata$usr_multisimulation[[modID]][["Class"]],
modName = modName,
jumps = jumps,
dimension = dimension,
symb = colnames(yuimaGUIsettings$simulation[[modID]][["xinit"]])
)
)
addSimulation(
modelYuimaGUI = modelYuimaGUI,
symbName = modID,
xinit = yuimaGUIsettings$simulation[[modID]][["xinit"]],
true.parameter = lapply(yuimaGUIdata$usr_multisimulation[[modID]][["true.param"]], FUN = function(x) as.numeric(x)),
nsim = yuimaGUIsettings$simulation[[modID]][["nsim"]],
nstep = yuimaGUIsettings$simulation[[modID]][["nstep"]],
simulate.from = yuimaGUIsettings$simulation[[modID]][["t0"]],
simulate.to = yuimaGUIsettings$simulation[[modID]][["t1"]],
saveTraj = yuimaGUIsettings$simulation[[modID]][["traj"]],
seed = yuimaGUIsettings$simulation[[modID]][["seed"]],
session = session,
anchorId = "panel_multi_simulations_alert",
is.multi = TRUE
)
}
else if(modID %in% rownames(yuimaGUItable$multimodel)){
id <- unlist(strsplit(modID, split = " "))
incProgress(1/nrow(multi_modelsToSimulate$table), detail = paste(id[1],"-",yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]]$info$modName))
addSimulation(
modelYuimaGUI = yuimaGUIdata$multimodel[[id[1]]][[as.numeric(id[2])]],
symbName = id[1],
xinit = yuimaGUIsettings$simulation[[modID]][["xinit"]],
nsim = yuimaGUIsettings$simulation[[modID]][["nsim"]],
nstep = yuimaGUIsettings$simulation[[modID]][["nstep"]],
simulate.from = yuimaGUIsettings$simulation[[modID]][["t0"]],
simulate.to = yuimaGUIsettings$simulation[[modID]][["t1"]],
saveTraj = yuimaGUIsettings$simulation[[modID]][["traj"]],
seed = yuimaGUIsettings$simulation[[modID]][["seed"]],
session = session,
anchorId = "panel_multi_simulations_alert",
is.multi = TRUE
)
}
}
})
updateTabsetPanel(session = session, inputId = "panel_multi_simulations", selected = "Simulations")
}
})
observe({
shinyjs::toggle("multi_div_simulations", condition = (input$panel_multi_simulations!="Simulations"))
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/multivariate_estimated.R |
output$multi_simulate_PrintModelLatex <- renderUI({
if (!is.null(input$multi_simulate_model_usr_selectModel)){
return(withMathJax(printModelLatex(multi = TRUE, names = input$multi_simulate_model_usr_selectModel, process = isolate({input$multi_simulate_model_usr_selectClass}), dimension = input$multi_simulate_model_usr_selectDimension, jumps = jumps_shortcut(class = isolate({input$multi_simulate_model_usr_selectClass}), jumps = input$multi_simulate_model_usr_selectJumps))))
}
})
output$multi_simulate_model_usr_selectModel <- renderUI({
choices <- as.vector(defaultMultiModels[names(defaultMultiModels)==input$multi_simulate_model_usr_selectClass])
sel <- choices[1]
for(i in names(yuimaGUIdata$multiusr_model))
if (yuimaGUIdata$multiusr_model[[i]]$class==input$multi_simulate_model_usr_selectClass)
choices <- c(i, choices)
selectInput("multi_simulate_model_usr_selectModel", label = "Model Name", choices = choices, selected = sel)
})
output$multi_simulate_model_usr_selectJumps <- renderUI({
if(input$multi_simulate_model_usr_selectClass %in% c("Compound Poisson", "Levy process"))
return(selectInput("multi_simulate_model_usr_selectJumps",label = "Jumps", choices = defaultJumps))
})
output$multi_simulate_model_usr_selectDimension <- renderUI({
if(!is.null(input$multi_simulate_model_usr_selectModel))
if(input$multi_simulate_model_usr_selectModel %in% c("Correlated Brownian Motion"))
return(numericInput("multi_simulate_model_usr_selectDimension",label = "Dimension", value = 2, step = 1))
})
output$multi_simulate_model_usr_selectParam <- renderUI({
valid <- TRUE
if (is.null(input$multi_simulate_model_usr_selectModel)) valid <- FALSE
else if (isolate({input$multi_simulate_model_usr_selectClass=="Compound Poisson"}) & is.null(input$multi_simulate_model_usr_selectJumps)) valid <- FALSE
else if (isolate({input$multi_simulate_model_usr_selectModel %in% c("Correlated Brownian Motion")}) & is.null(input$multi_simulate_model_usr_selectDimension)) valid <- FALSE
if (valid) {
mod <- setModelByName(input$multi_simulate_model_usr_selectModel, dimension = input$multi_simulate_model_usr_selectDimension, jumps = jumps_shortcut(class = isolate({input$multi_simulate_model_usr_selectClass}), jumps = input$multi_simulate_model_usr_selectJumps))
choices <- getAllParams(mod = mod, class = input$multi_simulate_model_usr_selectClass)
if (input$multi_simulate_model_usr_selectClass=="Fractional process") choices <- c(choices, "hurst")
return(selectInput("multi_simulate_model_usr_selectParam", label = "Parameter", choices = choices))
}
})
output$multi_simulate_model_usr_param <- renderUI({
numericInput("multi_simulate_model_usr_param", label = "Parameter value", value = NA)
})
output$multi_simulate_model_usr_ID <- renderUI({
textInput("multi_simulate_model_usr_ID", label = "Simulation ID")
})
observeEvent(input$multi_simulate_model_usr_button_save, {
if(input$multi_simulate_model_usr_ID!=""){
id <- gsub(pattern = " ", x = input$multi_simulate_model_usr_ID, replacement = "")
if (is.null(yuimaGUIdata$usr_multisimulation[[id]])){
yuimaGUIdata$usr_multisimulation[[id]] <<- list()
}
yuimaGUIdata$usr_multisimulation[[id]][["Class"]] <<- input$multi_simulate_model_usr_selectClass
yuimaGUIdata$usr_multisimulation[[id]][["Model"]] <<- input$multi_simulate_model_usr_selectModel
yuimaGUIdata$usr_multisimulation[[id]][["Jumps"]] <<- input$multi_simulate_model_usr_selectJumps
yuimaGUIdata$usr_multisimulation[[id]][["Dimension"]] <<- input$multi_simulate_model_usr_selectDimension
if (is.null(yuimaGUIdata$usr_multisimulation[[id]][["true.param"]])){
yuimaGUIdata$usr_multisimulation[[id]][["true.param"]] <<- list()
}
mod <- setModelByName(input$multi_simulate_model_usr_selectModel, jumps = input$multi_simulate_model_usr_selectJumps, dimension = input$multi_simulate_model_usr_selectDimension)
allparam <- getAllParams(mod = mod, class = input$multi_simulate_model_usr_selectClass)
if (length(allparam)==0)
yuimaGUIdata$usr_multisimulation[[id]]["true.param"] <<- NULL
if (length(allparam)!=0){
for(i in c(allparam, names(yuimaGUIdata$usr_multisimulation[[id]][["true.param"]]))){
if (!(i %in% names(yuimaGUIdata$usr_multisimulation[[id]][["true.param"]])))
yuimaGUIdata$usr_multisimulation[[id]][["true.param"]][[i]] <<- "MISSING"
if(!(i %in% allparam))
yuimaGUIdata$usr_multisimulation[[id]][["true.param"]][i] <<- NULL
}
}
}
})
observe({
if (!is.null(input$multi_simulate_model_usr_ID) & !is.null(input$multi_simulate_model_usr_selectParam) & !is.null(input$multi_simulate_model_usr_param)){
id <- gsub(pattern = " ", x = input$multi_simulate_model_usr_ID, replacement = "")
if (!is.null(yuimaGUIdata$usr_multisimulation[[id]])){
valid <- TRUE
if(yuimaGUIdata$usr_multisimulation[[id]][["Model"]]!=input$multi_simulate_model_usr_selectModel | input$multi_simulate_model_usr_selectParam=="")
valid <- FALSE
else if (yuimaGUIdata$usr_multisimulation[[id]][["Class"]] %in% c("Compound Poisson", "Levy process")) if (yuimaGUIdata$usr_multisimulation[[id]][["Jumps"]]!=input$multi_simulate_model_usr_selectJumps)
valid <- FALSE
if (valid)
yuimaGUIdata$usr_multisimulation[[id]][["true.param"]][[input$multi_simulate_model_usr_selectParam]] <- ifelse(is.na(input$multi_simulate_model_usr_param),"MISSING",input$multi_simulate_model_usr_param)
}
}
})
observe({
for(i in names(yuimaGUIdata$usr_multisimulation))
if (!(yuimaGUIdata$usr_multisimulation[[i]]$Model %in% c(defaultMultiModels, names(yuimaGUIdata$multiusr_model))))
yuimaGUIdata$usr_multisimulation[i] <<- NULL
})
output$multi_simulate_model_usr_table <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollX=TRUE, scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
table <- data.frame()
for (i in names(yuimaGUIdata$usr_multisimulation)){
newRow <- as.data.frame(yuimaGUIdata$usr_multisimulation[[i]])
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
if (length(table)==0){
NoData <- data.frame("Model"=NA, "Parameters"=NA)
return(NoData[-1,])
}
return (data.frame(table, row.names = names(yuimaGUIdata$usr_multisimulation)))
})
observeEvent(input$multi_simulate_model_usr_button_select, {
if (!is.null(input$multi_simulate_model_usr_table_rows_selected)){
table <- data.frame()
for (i in names(yuimaGUIdata$usr_multisimulation)[input$multi_simulate_model_usr_table_rows_selected]){
if ("MISSING" %in% yuimaGUIdata$usr_multisimulation[[i]][["true.param"]]){
createAlert(session = session, anchorId = "panel_multi_simulate_equation_alert", alertId = "multi_simulate_alert_usr_button_select", content = "There are still missing values in selected models", style = "error")
}
else {
closeAlert(session, "multi_simulate_alert_usr_button_select")
newRow <- as.data.frame(yuimaGUIdata$usr_multisimulation[[i]], row.names=i)
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
}
if (length(rownames(table))!=0)
multi_modelsToSimulate$table <<- multi_modelsToSimulate$table[-which(rownames(multi_modelsToSimulate$table) %in% rownames(table)),]
multi_modelsToSimulate$table <<- rbind.fill(multi_modelsToSimulate$table, table)
}
})
observeEvent(input$multi_simulate_model_usr_button_selectAll, {
if (!is.null(input$multi_simulate_model_usr_table_rows_all)){
table <- data.frame()
for (i in names(yuimaGUIdata$usr_multisimulation)[input$multi_simulate_model_usr_table_rows_all]){
if ("MISSING" %in% yuimaGUIdata$usr_multisimulation[[i]][["true.param"]]){
createAlert(session = session, anchorId = "panel_multi_simulate_equation_alert", alertId = "multi_simulate_alert_usr_button_select", content = "There are still missing values in selected models", style = "error")
}
else {
closeAlert(session, "multi_simulate_alert_usr_button_select")
newRow <- as.data.frame(yuimaGUIdata$usr_multisimulation[[i]], row.names=i)
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
}
if (length(rownames(table))!=0)
multi_modelsToSimulate$table <<- multi_modelsToSimulate$table[-which(rownames(multi_modelsToSimulate$table) %in% rownames(table)),]
multi_modelsToSimulate$table <<- rbind.fill(multi_modelsToSimulate$table, table)
}
})
observeEvent(input$multi_simulate_model_usr_button_delete, {
if (!is.null(input$multi_simulate_model_usr_table_rows_selected)){
for (i in input$multi_simulate_model_usr_table_rows_selected){
yuimaGUIdata$usr_multisimulation[i] <- NULL
}
}
})
observeEvent(input$multi_simulate_model_usr_button_deleteAll, {
if (!is.null(input$multi_simulate_model_usr_table_rows_all)){
for (i in input$multi_simulate_model_usr_table_rows_all){
yuimaGUIdata$usr_multisimulation[i] <- NULL
}
}
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/multivariate_non_estimated.R |
###Create simulations table
output$multi_simulate_monitor_table <- DT::renderDataTable(options=list(scrollY = 200, scrollX=TRUE, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$multisimulation)==0){
NoData <- data.frame("Symb"=NA,"Here will be stored simulations you run in the previous tabs"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$multisimulation)
})
observe({
shinyjs::toggle("multi_simulate_monitor_button_showSimulation", condition = (length(names(yuimaGUIdata$multisimulation))!=0))
})
###Delete Simulation
observeEvent(input$multi_simulate_monitor_button_delete, priority = 1, {
if(!is.null(input$multi_simulate_monitor_table_rows_selected) & !is.null(input$multi_simulate_monitor_table_row_last_clicked)){
if(input$multi_simulate_monitor_table_row_last_clicked %in% input$multi_simulate_monitor_table_rows_selected){
rowname <- unlist(strsplit(rownames(yuimaGUItable$multisimulation)[input$multi_simulate_monitor_table_row_last_clicked], split = " " , fixed = FALSE))
delSimulation(symb=rowname[1], n=rowname[2], multi = TRUE)
}
}
})
###DeleteAll Simulation
observeEvent(input$multi_simulate_monitor_button_deleteAll, priority = 1, {
if(!is.null(input$multi_simulate_monitor_table_rows_all)){
rowname <- unlist(strsplit(rownames(yuimaGUItable$multisimulation)[input$multi_simulate_monitor_table_rows_all], split = " " , fixed = FALSE))
delSimulation(symb=rowname[seq(1,length(rowname),2)], n=rowname[seq(2,length(rowname),2)], multi = TRUE)
}
})
output$multi_simulate_showSimulation_simID <- renderUI({
selectInput(inputId = "multi_simulate_showSimulation_simID", label = "Simulation ID", choices = rownames(yuimaGUItable$multisimulation))
})
multi_observationTime <- reactiveValues(x = numeric())
observeEvent(input$multi_simulate_showSimulation_simID,{
id <- unlist(strsplit(input$multi_simulate_showSimulation_simID, split = " "))
if(!is.na(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]]))
multi_observationTime$x <<- as.numeric(end(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory))
})
observe({
if (!is.null(input$multi_simulate_showSimulation_plot_click$x) & !is.null(input$multi_simulate_showSimulation_simID)){
id <- unlist(strsplit(input$multi_simulate_showSimulation_simID, split = " "))
if(!is.na(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]]))
multi_observationTime$x <<- input$multi_simulate_showSimulation_plot_click$x
}
})
params_multi_simulate_showSimulation_plot <- reactiveValues(id = NULL, y = NULL, z = NULL)
output$multi_simulate_showSimulation_plot_series1 <- renderUI({
if(!is.null(input$multi_simulate_showSimulation_simID))
if(input$multi_simulate_showSimulation_simID %in% rownames(yuimaGUItable$multisimulation)){
id <- unlist(strsplit(input$multi_simulate_showSimulation_simID, split = " "))
shinyjs::toggle("multi_simulate_showSimulation_plot_div", condition = !is.na(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[1]))
choices <- yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$model$info$symb
params_multi_simulate_showSimulation_plot$id <<- input$multi_simulate_showSimulation_simID
params_multi_simulate_showSimulation_plot$y <<- choices[1]
selectInput("multi_simulate_showSimulation_plot_series1", label="y-Axis", choices = choices, selected = choices[1])
}
})
output$multi_simulate_showSimulation_plot_series2 <- renderUI({
if(!is.null(input$multi_simulate_showSimulation_simID))
if(input$multi_simulate_showSimulation_simID %in% rownames(yuimaGUItable$multisimulation)){
id <- unlist(strsplit(input$multi_simulate_showSimulation_simID, split = " "))
choices <- yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$model$info$symb
params_multi_simulate_showSimulation_plot$z <<- last(choices)
selectInput("multi_simulate_showSimulation_plot_series2", label="z-Axis", choices = choices, selected = last(choices))
}
})
observe({
params_multi_simulate_showSimulation_plot$y <<- input$multi_simulate_showSimulation_plot_series1
params_multi_simulate_showSimulation_plot$z <<- input$multi_simulate_showSimulation_plot_series2
})
observeEvent(params_multi_simulate_showSimulation_plot$id,{
if(!is.null(params_multi_simulate_showSimulation_plot$id) & !is.null(params_multi_simulate_showSimulation_plot$y) & !is.null(params_multi_simulate_showSimulation_plot$z) ){
id <- unlist(strsplit(params_multi_simulate_showSimulation_plot$id, split = " "))
if(!is.na(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]])){
filtered.colnames <- gsub(colnames(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory), pattern = "_sim.*", replacement = "")
seriesnames <- unique(filtered.colnames)
d <- data.frame(sapply(seriesnames, function(x) {matrix(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[, filtered.colnames==x])}))
x <- index(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory)
n.x <- length(x)
n.row.d <- nrow(d)
d$x6428347364932 <- rep(x, n.row.d/n.x) #avoid index name matching a real seriesname
d$ID432793740239 <- rep(seq(1:(n.row.d/n.x)), each = n.x) #avoid id name matching a real seriesname
output$multi_simulate_showSimulation_plot <- renderPlotly({
y <- params_multi_simulate_showSimulation_plot$y
z <- params_multi_simulate_showSimulation_plot$z
if(y!=z)
plot_ly(d, x=~x6428347364932, y=d[,y], z=d[,z], split = ~ID432793740239, type="scatter3d", mode="lines", source = "multi_simulate_showSimulation_plot") %>%
layout(
showlegend = FALSE,
title = paste("ID:", isolate({params_multi_simulate_showSimulation_plot$id})),
scene = list (
xaxis = list(
title = ""
),
yaxis = list(
title = y
),
zaxis = list(
title = z
)
)
)
else
plot_ly(d, x=~x6428347364932, y=d[,y], split = ~ID432793740239, type="scatter", mode="lines", source = "multi_simulate_showSimulation_plot") %>%
layout(
showlegend = FALSE,
title = paste("ID:", isolate({params_multi_simulate_showSimulation_plot$id})),
xaxis = list(
title = ""
),
yaxis = list(
title = y
)
)
})
}
}
})
multi_simulation_hist <- reactiveValues(values=NULL, x=NULL, y=NULL, z=NULL)
observe({
if(!is.null(params_multi_simulate_showSimulation_plot$id) & !is.null(params_multi_simulate_showSimulation_plot$y) & !is.null(params_multi_simulate_showSimulation_plot$z) ){
id <- unlist(strsplit(params_multi_simulate_showSimulation_plot$id, split = " "))
if(id[1] %in% names(yuimaGUIdata$multisimulation)) if(length(yuimaGUIdata$multisimulation[[id[1]]])>=as.numeric(id[2])) if(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$info$nsim > 1){
if(is.na(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$hist[1])){
shinyjs::toggle("multi_simulate_showSimulation_hist_div", condition = yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$info$nsim > 1)
filtered.colnames <- gsub(colnames(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory), pattern = "_sim.*", replacement = "")
seriesnames <- unique(filtered.colnames)
x <- index(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory)
click <- event_data(event = "plotly_click", source = "multi_simulate_showSimulation_plot")
if (!is.null(click)) extract.x <- as.character(click$x[1])
else extract.x <- as.character(last(x))
extract.index <- which(as.character(index(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory))==extract.x)
d <- data.frame(sapply(seriesnames, function(x) {matrix(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory[extract.index, filtered.colnames==x])}))
multi_simulation_hist$x <<- extract.x
multi_simulation_hist$values <<- d
}
else {
multi_simulation_hist$x <<- yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$info$simulate.to
multi_simulation_hist$values <<- t(yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$hist)
}
multi_simulation_hist$y <<- params_multi_simulate_showSimulation_plot$y
multi_simulation_hist$z <<- params_multi_simulate_showSimulation_plot$z
}
}
})
observeEvent(multi_simulation_hist$values, {
output$multi_simulate_showSimulation_hist <- renderPlotly({
if(multi_simulation_hist$y!=multi_simulation_hist$z){
plot_ly(x=multi_simulation_hist$values[,multi_simulation_hist$y], y=multi_simulation_hist$values[,multi_simulation_hist$z], type = "histogram2d") %>%
layout(
title = paste("Distribution at index =", multi_simulation_hist$x),
xaxis = list(
title = multi_simulation_hist$y
),
yaxis = list(
title = multi_simulation_hist$z
)
)
}
else
plot_ly(x=multi_simulation_hist$values[,multi_simulation_hist$y], type = "histogram") %>%
layout(
title = paste("Distribution at index =", multi_simulation_hist$x),
xaxis = list(
title = multi_simulation_hist$y
),
yaxis = list(
title = "Frequency"
)
)
})
}, once = TRUE)
output$multi_simulate_showSimulation_hist_text <- renderUI({
if(length(multi_simulation_hist$values)!=0 & !is.null(input$multi_simulate_showSimulation_hist_probability_slider)){
val1 <- as.numeric(multi_simulation_hist$values[,multi_simulation_hist$y])
qq1 <- quantile(val1, probs = input$multi_simulate_showSimulation_hist_probability_slider/100)
val2 <- as.numeric(multi_simulation_hist$values[,multi_simulation_hist$z])
qq2 <- quantile(val2, probs = input$multi_simulate_showSimulation_hist_probability_slider/100)
HTML(paste("<div>", multi_simulation_hist$y , "<br/>", "Lower:", qq1[1],"<br/>", "Upper: ", qq1[2], "<br/>", "Mean: ", mean(val1[val1>=qq1[1] & val1<=qq1[2]]), "</div>",
"<br/>",
"<div>", multi_simulation_hist$z , "<br/>", "Lower:", qq2[1],"<br/>", "Upper: ", qq2[2], "<br/>", "Mean: ", mean(val2[val2>=qq2[1] & val2<=qq2[2]]), "</div>"))
}
})
###Save Trajectory Button
output$multi_simulate_showSimulation_button_saveTrajectory <- {
dataDownload_multi_traj <- reactive({
id <- unlist(strsplit(input$multi_simulate_showSimulation_simID, split = " "))
x <- yuimaGUIdata$multisimulation[[id[1]]][[as.numeric(id[2])]]$trajectory
d <- data.frame(x, row.names = index(x))
return(d)
})
downloadHandler(
filename = function() {
paste(input$multi_simulate_showSimulation_simID, ".txt", sep="")
},
content = function(file) {
write.table(dataDownload_multi_traj(), file, row.names = TRUE, col.names = TRUE, quote = TRUE)
}
)
}
###Save Histogram Button
output$multi_simulate_showSimulation_button_saveHist <- {
dataDownload_multi_hist <- reactive({
multi_simulation_hist$values
})
downloadHandler(
filename = function() {
paste(input$multi_simulate_showSimulation_simID, "_hist",".txt", sep="")
},
content = function(file) {
write.table(dataDownload_multi_hist(), file, row.names = FALSE, col.names = TRUE)
}
)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/multivariate_results.R |
output$simulate_databaseModels <- DT::renderDataTable(options=list(scrollY = 200, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
if (length(yuimaGUItable$model)==0){
NoData <- data.frame("Symb"=NA,"Please estimate some models first"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$model)
})
modelsToSimulate <- reactiveValues(table=data.frame())
###Select Button
observeEvent(input$simulate_button_selectModels, priority = 1, {
modelsToSimulate$table <<- rbind.fill(modelsToSimulate$table, yuimaGUItable$model[(rownames(yuimaGUItable$model) %in% rownames(yuimaGUItable$model)[input$simulate_databaseModels_rows_selected]) & !(rownames(yuimaGUItable$model) %in% rownames(modelsToSimulate$table)),])
})
###SelectAll Button
observeEvent(input$simulate_button_selectAllModels, priority = 1, {
modelsToSimulate$table <<- rbind.fill(modelsToSimulate$table, yuimaGUItable$model[(rownames(yuimaGUItable$model) %in% rownames(yuimaGUItable$model)[input$simulate_databaseModels_rows_all]) & !(rownames(yuimaGUItable$model) %in% rownames(modelsToSimulate$table)),])
})
observe({
if("AIC" %in% colnames(modelsToSimulate$table))
modelsToSimulate$table[,"AIC"] <<- as.numeric(as.character(modelsToSimulate$table[,"AIC"]))
if("BIC" %in% colnames(modelsToSimulate$table))
modelsToSimulate$table[,"BIC"] <<- as.numeric(as.character(modelsToSimulate$table[,"BIC"]))
})
###Control selected models to be in yuimaGUIdata$model or user defined
observe({
if(length(rownames(modelsToSimulate$table))!=0){
names.valid <- c(names(yuimaGUIdata$usr_simulation), rownames(yuimaGUItable$model))
col <- colnames(modelsToSimulate$table)
updatedtable <- data.frame(modelsToSimulate$table[which(rownames(modelsToSimulate$table) %in% names.valid),], row.names = rownames(modelsToSimulate$table)[rownames(modelsToSimulate$table) %in% names.valid])
colnames(updatedtable) <- col
modelsToSimulate$table <<- updatedtable
}
})
output$simulate_selectedModels <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollX=TRUE, scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
if (length(rownames(modelsToSimulate$table))==0){
NoData <- data.frame("Symb"=NA,"Please select models from the table above"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (modelsToSimulate$table)
})
###Delete Button
observeEvent(input$simulation_button_deleteModels, priority = 1,{
if (!is.null(input$simulate_selectedModels_rows_selected))
modelsToSimulate$table <<- modelsToSimulate$table[-input$simulate_selectedModels_rows_selected,]
})
###DeleteAll Button
observeEvent(input$simulation_button_deleteAllModels, priority = 1,{
if (!is.null(input$simulate_selectedModels_rows_all))
modelsToSimulate$table <<- modelsToSimulate$table[-input$simulate_selectedModels_rows_all,]
})
observe({
shinyjs::toggle(id="simulate_setSimulation_errorMessage", condition = length(rownames(modelsToSimulate$table))==0)
shinyjs::toggle(id="simulate_setSimulation_body", condition = length(rownames(modelsToSimulate$table))!=0)
})
observe({
shinyjs::toggle(id="simulate_advancedSettings_errorMessage", condition = length(rownames(modelsToSimulate$table))==0)
shinyjs::toggle(id="simulate_advancedSettings_body", condition = length(rownames(modelsToSimulate$table))!=0)
})
observe({
for (modID in rownames(modelsToSimulate$table)[input$simulate_selectedModels_rows_all]){
if (modID %in% names(yuimaGUIdata$usr_simulation)){
if (is.null(yuimaGUIsettings$simulation[[modID]]))
yuimaGUIsettings$simulation[[modID]] <<- list()
if (is.null(yuimaGUIsettings$simulation[[modID]][["xinit"]]))
yuimaGUIsettings$simulation[[modID]][["xinit"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["nstep"]]))
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- 1000
if (is.null(yuimaGUIsettings$simulation[[modID]][["nsim"]]))
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["t0"]]))
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- 0
if (is.null(yuimaGUIsettings$simulation[[modID]][["t1"]]))
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["traj"]]))
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- TRUE
if (is.null(yuimaGUIsettings$simulation[[modID]][["seed"]]))
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- NA
}
if (modID %in% rownames(yuimaGUItable$model)){
id <- unlist(strsplit(modID, split = " "))
if (is.null(yuimaGUIsettings$simulation[[modID]]))
yuimaGUIsettings$simulation[[modID]] <<- list()
if (is.null(yuimaGUIsettings$simulation[[modID]][["xinit"]])){
xinit <- as.numeric(tail(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected],1))[1]
toLog <- yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$info$toLog
if(toLog==TRUE) xinit <- exp(xinit)
yuimaGUIsettings$simulation[[modID]][["xinit"]] <<- xinit
}
if (is.null(yuimaGUIsettings$simulation[[modID]][["nstep"]]))
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- NA
if (is.null(yuimaGUIsettings$simulation[[modID]][["nsim"]]))
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- 1
if (is.null(yuimaGUIsettings$simulation[[modID]][["t0"]]))
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- end(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected])
if (is.null(yuimaGUIsettings$simulation[[modID]][["t1"]]))
if(is.numeric(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected])))
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- 2*end(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected])-start(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected])
else
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- end(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected])+365
if (is.null(yuimaGUIsettings$simulation[[modID]][["traj"]]))
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- TRUE
if (is.null(yuimaGUIsettings$simulation[[modID]][["seed"]]))
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- NA
}
}
})
output$simulate_modelID <- renderUI({
selectInput("simulate_modelID", label = "Simulation ID", choices = rownames(modelsToSimulate$table))
})
output$simulate_advancedSettings_modelID <- renderUI({
selectInput("simulate_advancedSettings_modelID", label = "Simulation ID", choices = rownames(modelsToSimulate$table))
})
output$simulate_seed <- renderUI({
if(!is.null(input$simulate_advancedSettings_modelID))
numericInput("simulate_seed", label = "RNG seed", step = 1, min = 0, value = yuimaGUIsettings$simulation[[input$simulate_advancedSettings_modelID]][["seed"]])
})
output$simulate_traj <- renderUI({
if(!is.null(input$simulate_advancedSettings_modelID))
selectInput("simulate_traj", label = "Save trajectory", choices = c(TRUE,FALSE), selected = yuimaGUIsettings$simulation[[input$simulate_advancedSettings_modelID]][["traj"]])
})
output$simulate_nsim <- renderUI({
if(!is.null(input$simulate_modelID))
numericInput("simulate_nsim", label = "Number of simulations", value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["nsim"]], min = 1, step = 1)
})
output$simulate_nstep <- renderUI({
if(!is.null(input$simulate_modelID)){
id <- unlist(strsplit(input$simulate_modelID, split = " "))
if (input$simulate_modelID %in% names(yuimaGUIdata$usr_simulation)){
numericInput("simulate_nstep", label = "Number of steps per simulation", value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["nstep"]], min = 1, step = 1)
} else if (!(isolate({yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$info$class}) %in% c("COGARCH", "CARMA")))
numericInput("simulate_nstep", label = "Number of steps per simulation", value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["nstep"]], min = 1, step = 1)
}
})
output$simulate_xinit <- renderUI({
if(!is.null(input$simulate_modelID))
numericInput("simulate_xinit", label = "Initial value", value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["xinit"]])
})
output$simulate_range <- renderUI({
if(!is.null(input$simulate_modelID)){
if (input$simulate_modelID %in% names(yuimaGUIdata$usr_simulation)){
return(div(
column(6,numericInput("simulate_rangeNumeric_t0", label = "From", min = 0 ,value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]])),
column(6,numericInput("simulate_rangeNumeric_t1", label = "To", min = 0, value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]]))
))
}
if (input$simulate_modelID %in% rownames(yuimaGUItable$model)){
id <- unlist(strsplit(input$simulate_modelID, split = " "))
if (class(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date")
dateRangeInput("simulate_rangeDate", label = "Simulation interval", start = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]], end = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]])
else{
div(
column(6,numericInput("simulate_rangeNumeric_t0", label = "From", min = 0 ,value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]])),
column(6,numericInput("simulate_rangeNumeric_t1", label = "To", min = 0, value = yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]]))
)
}
}
}
})
observeEvent(input$simulate_button_apply_advancedSettings, {
yuimaGUIsettings$simulation[[input$simulate_advancedSettings_modelID]][["seed"]] <<- input$simulate_seed
yuimaGUIsettings$simulation[[input$simulate_advancedSettings_modelID]][["traj"]] <<- input$simulate_traj
})
observeEvent(input$simulate_button_applyAll_advancedSettings, {
for (modID in rownames(modelsToSimulate$table)){
yuimaGUIsettings$simulation[[modID]][["seed"]] <<- input$simulate_seed
yuimaGUIsettings$simulation[[modID]][["traj"]] <<- input$simulate_traj
}
})
observeEvent(input$simulate_button_apply_nsim, {
yuimaGUIsettings$simulation[[input$simulate_modelID]][["nsim"]] <<- input$simulate_nsim
yuimaGUIsettings$simulation[[input$simulate_modelID]][["nstep"]] <<- input$simulate_nstep
})
observeEvent(input$simulate_button_applyAll_nsim, {
for (modID in rownames(modelsToSimulate$table)){
yuimaGUIsettings$simulation[[modID]][["nsim"]] <<- input$simulate_nsim
yuimaGUIsettings$simulation[[modID]][["nstep"]] <<- input$simulate_nstep
}
})
observeEvent(input$simulate_button_apply_xinit, {
yuimaGUIsettings$simulation[[input$simulate_modelID]][["xinit"]] <<- input$simulate_xinit
})
observeEvent(input$simulate_button_applyAll_xinit, {
for (modID in rownames(modelsToSimulate$table))
yuimaGUIsettings$simulation[[modID]][["xinit"]] <<- input$simulate_xinit
})
observeEvent(input$simulate_button_apply_range, {
if (input$simulate_modelID %in% names(yuimaGUIdata$usr_simulation)){
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]] <<- input$simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]] <<- input$simulate_rangeNumeric_t1
}
if (input$simulate_modelID %in% rownames(yuimaGUItable$model)){
id <- unlist(strsplit(input$simulate_modelID, split = " "))
if (class(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date"){
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]] <<- input$simulate_rangeDate[1]
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]] <<- input$simulate_rangeDate[2]
}
else{
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t0"]] <<- input$simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[input$simulate_modelID]][["t1"]] <<- input$simulate_rangeNumeric_t1
}
}
})
observeEvent(input$simulate_button_applyAll_range, {
for (modID in rownames(modelsToSimulate$table)){
if (modID %in% names(yuimaGUIdata$usr_simulation)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$simulate_rangeNumeric_t1
}
if (modID %in% rownames(yuimaGUItable$model)){
id <- unlist(strsplit(modID, split = " "))
if (class(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="Date" & !is.null(input$simulate_rangeDate)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$simulate_rangeDate[1]
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$simulate_rangeDate[2]
}
if (class(index(yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$model@[email protected]))=="numeric" & !is.null(input$simulate_rangeNumeric_t0)){
yuimaGUIsettings$simulation[[modID]][["t0"]] <<- input$simulate_rangeNumeric_t0
yuimaGUIsettings$simulation[[modID]][["t1"]] <<- input$simulate_rangeNumeric_t1
}
}
}
})
observe({
if (!is.null(modelsToSimulate$table)) if (nrow(modelsToSimulate$table)!=0) {
closeAlert(session, alertId = "simulate_alert_buttonEstimate1")
closeAlert(session, alertId = "simulate_alert_buttonEstimate2")
}
})
observeEvent(input$simulate_simulateModels, {
if (is.null(modelsToSimulate$table)) {
if (input$panel_simulations=="Estimated models") createAlert(session = session, anchorId = "panel_simulate_model_alert", alertId = "simulate_alert_buttonEstimate1", content = "Table 'Selected Models' is empty", style = "warning")
if (input$panel_simulations=="Non-estimated models") createAlert(session = session, anchorId = "panel_simulate_equation_alert", alertId = "simulate_alert_buttonEstimate2", content = "Table 'Selected Models' is empty", style = "warning")
} else if (nrow(modelsToSimulate$table)==0) {
if (input$panel_simulations=="Estimated models") createAlert(session = session, anchorId = "panel_simulate_model_alert", alertId = "simulate_alert_buttonEstimate1", content = "Table 'Selected Models' is empty", style = "warning")
if (input$panel_simulations=="Non-estimated models") createAlert(session = session, anchorId = "panel_simulate_equation_alert", alertId = "simulate_alert_buttonEstimate2", content = "Table 'Selected Models' is empty", style = "warning")
}
else{
withProgress(message = 'Simulating: ', value = 0, {
for (modID in rownames(modelsToSimulate$table)){
if(modID %in% names(yuimaGUIdata$usr_simulation)){
incProgress(1/nrow(modelsToSimulate$table), detail = paste(modID,"-",yuimaGUIdata$usr_simulation[[modID]][["Model"]]))
jumps <- ifelse(is.null(yuimaGUIdata$usr_simulation[[modID]][["Jumps"]]),NA, yuimaGUIdata$usr_simulation[[modID]][["Jumps"]])
modName <- yuimaGUIdata$usr_simulation[[modID]][["Model"]]
if(yuimaGUIdata$usr_simulation[[modID]][["Class"]]=='Point Process')
modelYuimaGUI <- list(model = setModelByName(name = modName, jumps = jumps),
info = list(class = yuimaGUIdata$usr_simulation[[modID]][["Class"]],
modName = modName,
jumps = jumps
)
)
else
modelYuimaGUI <- list(model = setYuima(model = setModelByName(name = modName, jumps = jumps)),
info = list(class = yuimaGUIdata$usr_simulation[[modID]][["Class"]],
modName = modName,
jumps = jumps
)
)
addSimulation(
modelYuimaGUI = modelYuimaGUI,
symbName = modID,
xinit = yuimaGUIsettings$simulation[[modID]][["xinit"]],
true.parameter = yuimaGUIdata$usr_simulation[[modID]][["true.param"]],
nsim = yuimaGUIsettings$simulation[[modID]][["nsim"]],
nstep = yuimaGUIsettings$simulation[[modID]][["nstep"]],
simulate.from = yuimaGUIsettings$simulation[[modID]][["t0"]],
simulate.to = yuimaGUIsettings$simulation[[modID]][["t1"]],
saveTraj = yuimaGUIsettings$simulation[[modID]][["traj"]],
seed = yuimaGUIsettings$simulation[[modID]][["seed"]],
session = session,
anchorId = "panel_simulations_alert"
)
}
else if(modID %in% rownames(yuimaGUItable$model)){
id <- unlist(strsplit(modID, split = " "))
incProgress(1/nrow(modelsToSimulate$table), detail = paste(id[1],"-",yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]]$info$modName))
addSimulation(
modelYuimaGUI = yuimaGUIdata$model[[id[1]]][[as.numeric(id[2])]],
symbName = id[1],
xinit = yuimaGUIsettings$simulation[[modID]][["xinit"]],
nsim = yuimaGUIsettings$simulation[[modID]][["nsim"]],
nstep = yuimaGUIsettings$simulation[[modID]][["nstep"]],
simulate.from = yuimaGUIsettings$simulation[[modID]][["t0"]],
simulate.to = yuimaGUIsettings$simulation[[modID]][["t1"]],
saveTraj = yuimaGUIsettings$simulation[[modID]][["traj"]],
seed = yuimaGUIsettings$simulation[[modID]][["seed"]],
session = session,
anchorId = "panel_simulations_alert"
)
}
}
})
updateTabsetPanel(session = session, inputId = "panel_simulations", selected = "Simulations")
}
})
observe({
shinyjs::toggle("div_simulations", condition = (input$panel_simulations!="Simulations"))
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/univariate_estimated.R |
output$simulate_PrintModelLatex <- renderUI({
if (!is.null(input$simulate_model_usr_selectModel)){
return(withMathJax(printModelLatex(names = input$simulate_model_usr_selectModel, process = isolate({input$simulate_model_usr_selectClass}), jumps = jumps_shortcut(class = isolate({input$simulate_model_usr_selectClass}), jumps = input$simulate_model_usr_selectJumps))))
}
})
output$simulate_model_usr_selectModel <- renderUI({
choices <- as.vector(defaultModels[names(defaultModels)==input$simulate_model_usr_selectClass])
sel <- choices[1]
for(i in names(yuimaGUIdata$usr_model))
if (yuimaGUIdata$usr_model[[i]]$class==input$simulate_model_usr_selectClass)
choices <- c(i, choices)
selectInput("simulate_model_usr_selectModel", label = "Model Name", choices = choices, selected = sel)
})
output$simulate_model_usr_selectJumps <- renderUI({
if(input$simulate_model_usr_selectClass %in% c("Compound Poisson", "Levy process"))
return(selectInput("simulate_model_usr_selectJumps",label = "Jumps", choices = defaultJumps))
})
output$simulate_model_usr_selectParam <- renderUI({
valid <- TRUE
if (is.null(input$simulate_model_usr_selectModel)) valid <- FALSE
else if (isolate({input$simulate_model_usr_selectClass=="Compound Poisson"}) & is.null(input$simulate_model_usr_selectJumps)) valid <- FALSE
if (valid) {
mod <- setModelByName(input$simulate_model_usr_selectModel, jumps = jumps_shortcut(class = isolate({input$simulate_model_usr_selectClass}), jumps = input$simulate_model_usr_selectJumps))
choices <- getAllParams(mod = mod, class = input$simulate_model_usr_selectClass)
return(selectInput("simulate_model_usr_selectParam", label = "Parameter", choices = choices))
}
})
output$simulate_model_usr_param <- renderUI({
numericInput("simulate_model_usr_param", label = "Parameter value", value = NA)
})
output$simulate_model_usr_ID <- renderUI({
textInput("simulate_model_usr_ID", label = "Simulation ID")
})
observeEvent(input$simulate_model_usr_button_save, {
if(input$simulate_model_usr_ID!=""){
id <- gsub(pattern = " ", x = input$simulate_model_usr_ID, replacement = "")
if (is.null(yuimaGUIdata$usr_simulation[[id]])){
yuimaGUIdata$usr_simulation[[id]] <<- list()
}
yuimaGUIdata$usr_simulation[[id]][["Class"]] <<- input$simulate_model_usr_selectClass
yuimaGUIdata$usr_simulation[[id]][["Model"]] <<- input$simulate_model_usr_selectModel
yuimaGUIdata$usr_simulation[[id]][["Jumps"]] <<- input$simulate_model_usr_selectJumps
if(!(input$simulate_model_usr_selectClass %in% c("Compound Poisson", "Levy process"))) yuimaGUIdata$usr_simulation[[id]][["Jumps"]] <<- NA
if (is.null(yuimaGUIdata$usr_simulation[[id]][["true.param"]])){
yuimaGUIdata$usr_simulation[[id]][["true.param"]] <<- list()
}
mod <- setModelByName(input$simulate_model_usr_selectModel, jumps = input$simulate_model_usr_selectJumps)
allparam <- getAllParams(mod = mod, class = input$simulate_model_usr_selectClass)
if (length(allparam)==0)
yuimaGUIdata$usr_simulation[[id]]["true.param"] <<- NULL
if (length(allparam)!=0){
for(i in c(allparam, names(yuimaGUIdata$usr_simulation[[id]][["true.param"]]))){
if (!(i %in% names(yuimaGUIdata$usr_simulation[[id]][["true.param"]])))
yuimaGUIdata$usr_simulation[[id]][["true.param"]][[i]] <<- "MISSING"
if(!(i %in% allparam))
yuimaGUIdata$usr_simulation[[id]][["true.param"]][i] <<- NULL
}
}
}
})
observe({
if (!is.null(input$simulate_model_usr_ID) & !is.null(input$simulate_model_usr_selectParam) & !is.null(input$simulate_model_usr_param)){
id <- gsub(pattern = " ", x = input$simulate_model_usr_ID, replacement = "")
if (!is.null(yuimaGUIdata$usr_simulation[[id]])){
valid <- TRUE
if(yuimaGUIdata$usr_simulation[[id]][["Model"]]!=input$simulate_model_usr_selectModel | input$simulate_model_usr_selectParam=="")
valid <- FALSE
else if (yuimaGUIdata$usr_simulation[[id]][["Class"]] %in% c("Compound Poisson", "Levy process")) if (yuimaGUIdata$usr_simulation[[id]][["Jumps"]]!=input$simulate_model_usr_selectJumps)
valid <- FALSE
if (valid)
yuimaGUIdata$usr_simulation[[id]][["true.param"]][[input$simulate_model_usr_selectParam]] <- ifelse(is.na(input$simulate_model_usr_param),"MISSING",input$simulate_model_usr_param)
}
}
})
observe({
for(i in names(yuimaGUIdata$usr_simulation))
if (!(yuimaGUIdata$usr_simulation[[i]]$Model %in% c(defaultModels, names(yuimaGUIdata$usr_model))))
yuimaGUIdata$usr_simulation[i] <<- NULL
})
output$simulate_model_usr_table <- DT::renderDataTable(options=list(order = list(1, 'desc'), scrollX=TRUE, scrollY = 150, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "multiple",{
table <- data.frame()
for (i in names(yuimaGUIdata$usr_simulation)){
newRow <- as.data.frame(yuimaGUIdata$usr_simulation[[i]])
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
if (length(table)==0){
NoData <- data.frame("Model"=NA, "Parameters"=NA)
return(NoData[-1,])
}
return (data.frame(table, row.names = names(yuimaGUIdata$usr_simulation)))
})
observeEvent(input$simulate_model_usr_button_select, {
if (!is.null(input$simulate_model_usr_table_rows_selected)){
table <- data.frame()
for (i in names(yuimaGUIdata$usr_simulation)[input$simulate_model_usr_table_rows_selected]){
if ("MISSING" %in% yuimaGUIdata$usr_simulation[[i]][["true.param"]]){
createAlert(session = session, anchorId = "panel_simulate_equation_alert", alertId = "simulate_alert_usr_button_select", content = "There are still missing values in selected models", style = "error")
}
else {
closeAlert(session, "simulate_alert_usr_button_select")
newRow <- as.data.frame(yuimaGUIdata$usr_simulation[[i]], row.names=i)
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
}
if (length(rownames(table))!=0)
modelsToSimulate$table <<- modelsToSimulate$table[-which(rownames(modelsToSimulate$table) %in% rownames(table)),]
modelsToSimulate$table <<- rbind.fill(modelsToSimulate$table, table)
}
})
observeEvent(input$simulate_model_usr_button_selectAll, {
if (!is.null(input$simulate_model_usr_table_rows_all)){
table <- data.frame()
for (i in names(yuimaGUIdata$usr_simulation)[input$simulate_model_usr_table_rows_all]){
if ("MISSING" %in% yuimaGUIdata$usr_simulation[[i]][["true.param"]]){
createAlert(session = session, anchorId = "panel_simulate_equation_alert", alertId = "simulate_alert_usr_button_select", content = "There are still missing values in selected models", style = "error")
}
else {
closeAlert(session, "simulate_alert_usr_button_select")
newRow <- as.data.frame(yuimaGUIdata$usr_simulation[[i]], row.names=i)
colnames(newRow) <- gsub(pattern = "true.param.", x = colnames(newRow), replacement = "")
table <- rbind.fill(table, newRow)
}
}
if (length(rownames(table))!=0)
modelsToSimulate$table <<- modelsToSimulate$table[-which(rownames(modelsToSimulate$table) %in% rownames(table)),]
modelsToSimulate$table <<- rbind.fill(modelsToSimulate$table, table)
}
})
observeEvent(input$simulate_model_usr_button_delete, {
if (!is.null(input$simulate_model_usr_table_rows_selected)){
for (i in input$simulate_model_usr_table_rows_selected){
yuimaGUIdata$usr_simulation[i] <- NULL
}
}
})
observeEvent(input$simulate_model_usr_button_deleteAll, {
if (!is.null(input$simulate_model_usr_table_rows_all)){
for (i in input$simulate_model_usr_table_rows_all){
yuimaGUIdata$usr_simulation[i] <- NULL
}
}
})
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/univariate_non_estimated.R |
###Create simulations table
output$simulate_monitor_table <- DT::renderDataTable(options=list(scrollY = 200, scrollX=TRUE, scrollCollapse = FALSE, deferRender = FALSE, dom = 'frtS'), extensions = 'Scroller', rownames = TRUE, selection = "single",{
if (length(yuimaGUItable$simulation)==0){
NoData <- data.frame("Symb"=NA,"Here will be stored simulations you run in the previous tabs"=NA, check.names = FALSE)
return(NoData[-1,])
}
return (yuimaGUItable$simulation)
})
observe({
shinyjs::toggle("simulate_monitor_button_showSimulation", condition = (length(names(yuimaGUIdata$simulation))!=0))
})
###Delete Simulation
observeEvent(input$simulate_monitor_button_delete, priority = 1, {
if(!is.null(input$simulate_monitor_table_rows_selected) & !is.null(input$simulate_monitor_table_row_last_clicked)){
if(input$simulate_monitor_table_row_last_clicked %in% input$simulate_monitor_table_rows_selected){
rowname <- unlist(strsplit(rownames(yuimaGUItable$simulation)[input$simulate_monitor_table_row_last_clicked], split = " " , fixed = FALSE))
delSimulation(symb=rowname[1], n=rowname[2])
}
}
})
###DeleteAll Simulation
observeEvent(input$simulate_monitor_button_deleteAll, priority = 1, {
if(!is.null(input$simulate_monitor_table_rows_all)){
rowname <- unlist(strsplit(rownames(yuimaGUItable$simulation)[input$simulate_monitor_table_rows_all], split = " " , fixed = FALSE))
delSimulation(symb=rowname[seq(1,length(rowname),2)], n=rowname[seq(2,length(rowname),2)])
}
})
output$simulate_showSimulation_simID <- renderUI({
selectInput(inputId = "simulate_showSimulation_simID", label = "Simulation ID", choices = rownames(yuimaGUItable$simulation))
})
observationTime <- reactiveValues(x = numeric())
observeEvent(input$simulate_showSimulation_simID,{
id <- unlist(strsplit(input$simulate_showSimulation_simID, split = " "))
if(!is.na(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]]))
observationTime$x <<- as.numeric(end(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory))
})
observe({
if (!is.null(input$simulate_showSimulation_plot_click$x) & !is.null(input$simulate_showSimulation_simID)){
id <- unlist(strsplit(input$simulate_showSimulation_simID, split = " "))
if(!is.na(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]]))
observationTime$x <<- input$simulate_showSimulation_plot_click$x
}
})
observe({
if(!is.null(input$simulate_showSimulation_simID)){
if(input$simulate_showSimulation_simID %in% rownames(yuimaGUItable$simulation)){
id <- unlist(strsplit(input$simulate_showSimulation_simID, split = " "))
shinyjs::toggle("simulate_showSimulation_plot_div", condition = !is.na(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory[1]))
if(!is.na(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory[[1]])){
output$simulate_showSimulation_plot <- renderPlot({
if(input$simulate_showSimulation_simID %in% rownames(yuimaGUItable$simulation)){
par(bg="black")
plot(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory, screens = 1, main = "Trajectory", xlab = "Index", ylab = "", log=switch(input$simulate_showSimulation_plot_scale,"Linear"="","Logarithmic (Y)"="y", "Logarithmic (X)"="x", "Logarithmic (XY)"="xy"), col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
abline(v = observationTime$x, col="yellow")
grid(col="grey")
}
})
}
}
}
})
simulation_hist <- reactiveValues(distribution=list(), values=vector())
observe({
if(!is.null(input$simulate_showSimulation_simID)){
if(input$simulate_showSimulation_simID %in% rownames(yuimaGUItable$simulation)) {
id <- unlist(strsplit(input$simulate_showSimulation_simID, split = " "))
shinyjs::toggle("simulate_showSimulation_hist_div", condition = yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$info$nsim > 1)
if(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$info$nsim > 1){
if(!is.na(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$hist[1])){
if(!is.null(input$simulate_showSimulation_hist_nBins)){
output$simulate_showSimulation_hist <- renderPlot({
par(bg="black")
simulation_hist$values <<- yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$hist
simulation_hist$distribution[[1]] <<- hist(yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$hist, freq = FALSE ,main = "Histogram", xlab = "", breaks = input$simulate_showSimulation_hist_nBins, col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(density(simulation_hist$values), col = "orange")
grid()
})
}
}
else{
if(!is.null(input$simulate_showSimulation_hist_nBins)){
traj <- yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory
if(class(index(traj))[1]=="numeric"){
data <- traj[which(abs(index(traj)-observationTime$x) == min(abs(index(traj)-observationTime$x))),]
} else {
x <- as.Date(as.POSIXct.numeric(observationTime$x, origin = "1970-01-01"))
data <- traj[which(abs(as.Date(index(traj))-x) == min(abs(as.Date(index(traj))-x))),]
}
output$simulate_showSimulation_hist <- renderPlot({
par(bg="black")
simulation_hist$values <<- data
simulation_hist$distribution[[1]] <<- hist(data, freq = FALSE ,main = "Histogram", xlab = "", breaks = input$simulate_showSimulation_hist_nBins, col="green", col.axis="grey", col.lab="grey", col.main="grey", fg="black")
lines(density(simulation_hist$values), col = "orange")
grid()
})
}
}
}
}
}
})
output$simulate_showSimulation_hist_text <- renderUI({
if(length(simulation_hist$values)!=0 & !is.null(input$simulate_showSimulation_hist_probability_slider)){
val <- as.numeric(simulation_hist$values)
qq <- quantile(val, probs = input$simulate_showSimulation_hist_probability_slider/100)
HTML(paste("<div>", "Lower:", qq[1],"<br/>", "Upper: ", qq[2], "<br/>", "Mean: ", mean(val[val>=qq[1] & val<=qq[2]]), "</div>"))
}
})
###Save Trajectory Button
output$simulate_showSimulation_button_saveTrajectory <- {
dataDownload_traj <- reactive({
id <- unlist(strsplit(input$simulate_showSimulation_simID, split = " "))
x <- yuimaGUIdata$simulation[[id[1]]][[as.numeric(id[2])]]$trajectory
d <- data.frame(x, row.names = index(x))
colnames(d) <- paste(id[1],id[2],"_",seq(1, ncol(d)), sep = "")
return(d)
})
downloadHandler(
filename = function() {
paste(input$simulate_showSimulation_simID, ".txt", sep="")
},
content = function(file) {
write.table(dataDownload_traj(), file, row.names = TRUE, col.names = TRUE, quote = TRUE)
}
)
}
###Save Histogram Button
output$simulate_showSimulation_button_saveHist <- {
dataDownload_hist <- reactive({
h <- simulation_hist$distribution[[1]]
data.frame(midPoints = h$mids, counts = h$counts, frequency=h$counts/sum(h$counts),density = h$density)
})
downloadHandler(
filename = function() {
paste(input$simulate_showSimulation_simID, "_hist",".txt", sep="")
},
content = function(file) {
write.table(dataDownload_hist(), file, row.names = FALSE, col.names = TRUE)
}
)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server/simulation/univariate_results.R |
options(shiny.maxRequestSize = 100*1024^2)
options("getSymbols.warning4.0"=FALSE)
server <- function(input, output, session) {
### comment this for web app version ###
session$onSessionEnded(function() {
stopApp()
})
### comment this for web app version ###
debug.gui <- function(){}
debug(debug.gui)
source("server/settings.R", local = TRUE)
source("server/functions.R", local = TRUE)
source("server/home/home.R", local = TRUE)
source("server/load_data/finance.R", local = TRUE)
source("server/load_data/your_file.R", local = TRUE)
source("server/eda/clustering.R", local = TRUE)
source("server/eda/changepoint_non_parametric.R", local = TRUE)
source("server/eda/changepoint_parametric.R", local = TRUE)
source("server/eda/llag_and_corr.R", local = TRUE)
source("server/modeling/univariate_start_estimation.R", local = TRUE)
source("server/modeling/univariate_set_model.R", local = TRUE)
source("server/modeling/univariate_results.R", local = TRUE)
source("server/modeling/multivariate_start_estimation.R", local = TRUE)
source("server/modeling/multivariate_results.R", local = TRUE)
source("server/simulation/univariate_estimated.R", local = TRUE)
source("server/simulation/univariate_non_estimated.R", local = TRUE)
source("server/simulation/univariate_results.R", local = TRUE)
source("server/simulation/multivariate_estimated.R", local = TRUE)
source("server/simulation/multivariate_non_estimated.R", local = TRUE)
source("server/simulation/multivariate_results.R", local = TRUE)
source("server/finance/profit_and_loss.R", local = TRUE)
}
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/server.R |
tabItem(tabName = "changepoint",
fluidRow(
column(12,
h3("Change Point Estimation",class = "hTitle"),
h4("Select the data you wish to estimate change points for.", br(),
"Choose the algorithm you want to use for estimation.", br(),
"The results will be displayed below by plotting the series and the detected change points."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,tabsetPanel(id = "panel_cpoint", type = "tabs",
tabPanel(title = "Nonparametric",
fluidRow(column(12,bsAlert("nonparametric_changepoint_alert"))),
fluidRow(column(12,
column(4,
h4("Available data"),
DT::dataTableOutput("changepoint_table_select")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("changepoint_table_selected")
),
column(4,br(),br(),br(),br(),
div(align="center", selectInput("changepoint_method", "Method", choices = c("Percentage Increments Distribution"="KSperc", "Increments Distribution"="KSdiff"))),
div(align="center", numericInput("changepoint_pvalue", label = "p-value", value=0.01, min=0, max=1))
)
)),
br(),
fluidRow(column(12,
column(2,actionButton("changepoint_button_select",label = "Select", align = "center")),
bsTooltip("changepoint_button_select", title = "Select data", placement = "top"),
column(2,actionButton("changepoint_button_selectAll",label = "Select All", align = "center")),
bsTooltip("changepoint_button_selectAll", title = "Select all data that are displayed", placement = "top"),
column(2,actionButton("changepoint_button_delete",label = "Delete", align = "center")),
bsTooltip("changepoint_button_delete", title = "Delete selected data", placement = "top"),
column(2,actionButton("changepoint_button_deleteAll",label = "Delete All", align = "center")),
bsTooltip("changepoint_button_deleteAll", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("changepoint_button_startEstimation", label = "Start Estimation", align = "center"))
)),
br(),br(),
fluidRow(column(12,shinyjs::hidden(div(id="changepoint_charts",
hr(class = "hrHeader"),
uiOutput("changepoint_symb", align="center"),
div(fluidRow(
column(6, div(align = "left", selectInput("changepoint_scale", label = "Scale", choices=c("Linear","Logarithmic (Y)","Logarithmic (X)", "Logarithmic (XY)"), width = "150px"))),
column(6, div(align = "right", br(), a(id = "linkChangePointInfo", "Change Points Info", style = "font-size: 140%;", href = "#")))
)),
bsModal(id = "ChangePointInfo", trigger = "linkChangePointInfo", title = "Change Points Info",
column(12,
fluidRow(uiOutput("text_ChangePointInfo")),
br(),
fluidRow(div(tableOutput("table_ChangePointInfo"), align="center"))
)
),
fluidRow(
column(6,plotOutput("changepoint_plot_series", brush = brushOpts(id = "changePoint_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "changePoint_dbclick")),
column(6,plotOutput("changepoint_plot_incr", brush = brushOpts(id = "changePoint_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "changePoint_dbclick"))
),br(),
fluidRow(
column(8),
column(2,actionButton("changepoint_button_delete_estimated",label = "Delete", align = "center")),
bsTooltip("changepoint_button_delete_estimated", title = "Delete selected series", placement = "top"),
column(2,actionButton("changepoint_button_deleteAll_estimated",label = "Delete All", align = "center")),
bsTooltip("changepoint_button_deleteAll_estimated", title = "Delete all series", placement = "top")
)
))))
),
tabPanel(title = "Parametric",
fluidRow(column(12,bsAlert("parametric_changepoint_alert"))),
fluidRow(column(12,
column(4,
h4("Available data"),
DT::dataTableOutput("parametric_changepoint_table_select")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("parametric_changepoint_table_selected")
),
column(4,br(),br(),div(align="center",
uiOutput("parametric_changepoint_model"),
sliderInput("parametric_modal_rangeFraction", label = "Training set (%)",min = 0, max = 100, value = c(20,80), step = 1, ticks = F),
br(),
column(6,div(actionButton("parametric_button_setRange", width = '95%', label = "Set Range"), align = "center")),
column(6,div(actionButton("parametric_button_settings", width = '95%', label = "Advanced Settings"), align = "center"))
)),
bsModal(id="parametric_plotsRange", trigger = "parametric_button_setRange", title = "Select range to use for change point estimation", size = "large",
div(id="parametric_plotsRangeErrorMessage",align = "center",h3("Select some series from table 'Available Data'", class = "hModal")),
div(id="parametric_plotsRangeAll",
fluidRow(
column(8,
plotOutput("parametric_selectRange", height = "350px", brush = brushOpts(id = "parametric_selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "parametric_selectRange_dbclick"),
br(),
plotOutput("parametric_selectRangeReturns", height = "350px", brush = brushOpts(id = "parametric_selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "parametric_selectRange_dbclick")
),
column(4,
div(selectInput("parametric_scale_selectRange", label = "Chart Scale", choices = c("Linear", "Logarithmic (Y)", "Logarithmic (X)", "Logarithmic (XY)")), align = "center"),
br(),br(),br(),
uiOutput("parametric_plotsRangeSeries", align = "center"),
uiOutput("parametric_chooseRange", align = "center"),
uiOutput("parametric_chooseRange_specify", align = "center"),
column(6,
tags$button(type="button", id="parametric_buttonApplyRange", class = "action-button", em("Apply")),
bsTooltip("parametric_buttonApplyRange", title = "Apply Range to selected symbol", placement = "top")
),
column(6,
tags$button(type="button", id="parametric_buttonApplyAllRange", class = "action-button", em("Apply All")),
bsTooltip("parametric_buttonApplyAllRange", title = "Apply Range to all symbols that are displayed", placement = "bottom")
)
)
)
)
),
bsModal(id="parametric_modal_id", title="Advanced Settings", trigger = "parametric_button_settings", size = "large",
div(id="parametric_modal_errorMessage", align = "center", h3("Select some series (from table 'Available Data')", class = "hModal")),
div(id="parametric_modal_body",
fluidRow(
column(6,
box(width = 12,div(align="center",
h3("Series Settings", class = "hModal"),
uiOutput("parametric_modal_series", align="center"),
fluidRow(
column(6,uiOutput("parametric_modal_delta", align="center")),
column(6,uiOutput("parametric_modal_toLog", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="parametric_modal_button_applyDelta", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="parametric_modal_button_applyAllDelta", class = "action-button", em("Apply to All series")))
)
)),
box(width = 12,div(align="center",
h3("General Settings", class = "hModal"),
uiOutput("parametric_modal_method", align="center"),
fluidRow(
column(6,uiOutput("parametric_modal_trials", align="center")),
column(6,uiOutput("parametric_modal_seed", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="parametric_modal_button_applyGeneral", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="parametric_modal_button_applyAllModelGeneral", class = "action-button", em("Apply to All series")))
)
))
),
column(6,
box(width = 12,div(align="center",
h3("Model Settings", class = "hModal"),
uiOutput("parametric_modal_model", align="center"),
uiOutput("parametric_modal_parameter", align="center"),
uiOutput("parametric_modal_start", align="center"),
fluidRow(
column(6,uiOutput("parametric_modal_startMin", align="center")),
column(6,uiOutput("parametric_modal_startMax", align="center"))
),
fluidRow(
column(6,uiOutput("parametric_modal_lower", align="center")),
column(6,uiOutput("parametric_modal_upper", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="parametric_modal_button_applyModel", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="parametric_modal_button_applyAllModel", class = "action-button", em("Apply to All series")))
)
))
)
)
)
)
)),
br(),
fluidRow(column(12,
column(2,actionButton("parametric_changepoint_button_select",label = "Select", align = "center")),
bsTooltip("changepoint_button_select", title = "Select data", placement = "top"),
column(2,actionButton("parametric_changepoint_button_selectAll",label = "Select All", align = "center")),
bsTooltip("changepoint_button_selectAll", title = "Select all data that are displayed", placement = "top"),
column(2,actionButton("parametric_changepoint_button_delete",label = "Delete", align = "center")),
bsTooltip("changepoint_button_delete", title = "Delete selected data", placement = "top"),
column(2,actionButton("parametric_changepoint_button_deleteAll",label = "Delete All", align = "center")),
bsTooltip("changepoint_button_deleteAll", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("parametric_changepoint_button_startEstimation", label = "Start Estimation", align = "center"))
)),
br(),br(),
fluidRow(column(12,shinyjs::hidden(div(id="parametric_changepoint_charts",
hr(class = "hrHeader"),
uiOutput("parametric_changepoint_symb", align="center"),
div(fluidRow(
column(12, selectInput("parametric_changepoint_scale", label = "Scale", choices=c("Linear","Logarithmic (Y)","Logarithmic (X)", "Logarithmic (XY)"), width = "150px"))
)),
fluidRow(
column(6,plotOutput("parametric_changepoint_plot_series", brush = brushOpts(id = "parametric_changePoint_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "parametric_changePoint_dbclick")),
column(6,
div(uiOutput("parametric_changepoint_info"),
a(id = "parametric_linkChangePointInfo", "Change Point Info", style = "font-size: 140%;", href = "#"),
align="center"),br(),br(),br(),br(),br(),br(),br(),br(),br(),
fluidRow(
column(2),
column(4,actionButton("parametric_changepoint_button_delete_estimated",label = "Delete", align = "center")),
bsTooltip("parametric_changepoint_button_delete_estimated", title = "Delete selected series", placement = "top"),
column(4,actionButton("parametric_changepoint_button_deleteAll_estimated",label = "Delete All", align = "center")),
bsTooltip("parametric_changepoint_button_deleteAll_estimated", title = "Delete all series", placement = "top")
)
)
),
bsModal(id = "parametric_changepoint_modal_info", title = "Change Point Info", trigger = "parametric_linkChangePointInfo",
fluidRow(column(12, uiOutput("parametric_changepoint_modal_info_text"))), br(),
fluidRow(
column(6, div(h5("Estimates before the Change Point", class = "hModal"), tableOutput("parametric_changepoint_modal_info_tableL"), align="center")),
column(6, div(h5("Estimates after the Change Point", class = "hModal"), tableOutput("parametric_changepoint_modal_info_tableR"), align="center"))
)
)
))))
)
)))
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/eda/changepoint.R |
tabItem(tabName = "cluster",
fluidRow(
column(12,
h3("Clustering",class = "hTitle"),
h4("Select data you want to cluster.", br(),
"Choose the distance you want to use and the kind of linkage for the hierarchical cluster analysis.", br(),
"The results will be shown below by plotting a dendrogram and multidimensional scaling output."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,bsAlert("cluster_alert"))),
fluidRow(column(12,
column(4,
h4("Available data"),
DT::dataTableOutput("cluster_table_select")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("cluster_table_selected")
),
column(4,br(),br(),
div(align="center",
selectInput("cluster_linkage", "Linkage", choices = c("Complete"="complete", "Single"="single", "Average"="average", "Ward"="ward.D", "Ward squared"="ward.D2", "McQuitty"="mcquitty", "Median"="median", "Centroid"="centroid")),
selectInput("cluster_distance", "Metric", choices = c("Percentage Increments Distribution"="MYdist_perc", "Increments Distribution"="MYdist_ass", "Markov Operator"="MOdist", "Euclidean"="euclidean", "Maximum"="maximum", "Manhattan"="manhattan", "Canberra"="canberra", "Minkowski"="minkowski")),
shinyjs::hidden(numericInput("cluster_distance_minkowskiPower", label = "Power", value = 2)))
)
)),
br(),
fluidRow(column(12,
column(2,actionButton("cluster_button_select",label = "Select", align = "center")),
bsTooltip("cluster_button_select", title = "Select data to cluster", placement = "top"),
column(2,actionButton("cluster_button_selectAll",label = "Select All", align = "center")),
bsTooltip("cluster_button_selectAll", title = "Select all data that are displayed", placement = "top"),
column(2,actionButton("cluster_button_delete",label = "Delete", align = "center")),
bsTooltip("cluster_button_delete", title = "Delete selected data", placement = "top"),
column(2,actionButton("cluster_button_deleteAll",label = "Delete All", align = "center")),
bsTooltip("cluster_button_deleteAll", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("cluster_button_startCluster", label = "Start Clustering", align = "center"))
)),
div(id="cluster_charts", align = "center",
br(),br(),
hr(class = "hrHeader"),
fluidRow(
column(4),
column(4, uiOutput("cluster_analysis_id"))
),
fluidRow(column(11, div(align="left", uiOutput("cluster_moreInfo")))),
fluidRow(column(12,
column(8, plotOutput("cluster_dendogram", click = "cluster_dendrogram_click")),
column(4, plotOutput("cluster_scaling2D"))
)),
br(),
fluidRow(column(12,
column(2, div(actionButton("cluster_button_delete_analysis", label = "Delete"))),
column(2, div(actionButton("cluster_button_deleteAll_analysis", label = "Delete All"))),
column(2),
column(2, div(downloadButton("cluster_button_saveDendogram", label = "Dendrogram"))),
column(2),
column(2, div(downloadButton("cluster_button_saveScaling2D", label = "Scaling")))
))
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/eda/cluster.R |
tabItem(tabName = "llag",
fluidRow(
column(12,
h3("Lead-Lag and Correlation Analysis",class = "hTitle"),
h4("Select the series you wish to analyze and the kind of analysis you want to perform (Lead-Lag or Correlation).", br(),
"Choose the correlation measure (if you selected Correlation) or the maximum lag to use (if you selected Lead-Lag).",br(),
"You can specify which interval to use over the whole series for your analysis."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,bsAlert("llag_alert"))),
fluidRow(column(12,
column(4,
h4("Available data"),
DT::dataTableOutput("llag_table_select")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("llag_table_selected")
),
column(4,br(),br(),
div(align="center",
selectInput("llag_type", label = "Type of analysis", choices = c("Lead-Lag"="llag", "Correlation"="corr"), selected = "llag"),
numericInput("llag_maxLag", label = "Max Lag", value = 20, min = 1, step = 1),
bsTooltip("llag_maxLag", title = "Max Lag is expressed in days if you are using series indexed by date. It is expressed in the same unit of measure of the index if you are using numerical indexes.", placement = "top"),
shinyjs::hidden(selectInput("llag_corr_method", label = "Method", choices = c("Pearson"="pearson", "Kendall"="kendall", "Spearman"="spearman", "Hayashi-Yoshida"="HY", "Pre-averaged Hayashi-Yoshida"="PHY", "Modulated Realized Covariance"="MRC", "Two Scales realized CoVariance"="TSCV", "Generalized Multiscale Estimator"="GME", "Realized Kernel"="RK", "Quasi Maximum Likelihood Estimator"="QMLE", "Separating Information Maximum Likelihood"="SIML", "Truncated Hayashi-Yoshida"="THY", "Pre-averaged Truncated Hayashi-Yoshida"="PTHY", "Subsampled Realized Covariance"="SRC", "Subsampled realized BiPower Covariation"="SBPC"), selected = "HY")),
dateRangeInput("llag_range_date", label = "Range", start = Sys.Date()-365, end = Sys.Date()),
shinyjs::hidden(div(id="llag_range_numeric",
column(6,numericInput("llag_range_numeric1", label = "From", value = 0)),
column(6,numericInput("llag_range_numeric2", label = "To", value = 1))
))
)
)
)),
br(),
fluidRow(column(12,
column(2,actionButton("llag_button_select",label = "Select", align = "center")),
bsTooltip("llag_button_select", title = "Select data", placement = "top"),
column(2,actionButton("llag_button_selectAll",label = "Select All", align = "center")),
bsTooltip("llag_button_selectAll", title = "Select all data that are displayed", placement = "top"),
column(2,actionButton("llag_button_delete",label = "Delete", align = "center")),
bsTooltip("llag_button_delete", title = "Delete selected data", placement = "top"),
column(2,actionButton("llag_button_deleteAll",label = "Delete All", align = "center")),
bsTooltip("llag_button_deleteAll", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("llag_button_startEstimation", label = "Start Analysis", align = "center"))
)),
br(),
fluidRow(column(12,
shinyjs::hidden(div(id = "llag_plot_body", align = "center",
hr(class = "hrHeader"),
fluidRow(
column(4),
column(4,uiOutput("llag_analysis_id"))
),
fluidRow(
column(12,
div(align="center", numericInput("llag_plot_confidence", label = "Confidence Level",width = "20%", value = 0.001, min = 0, max = 1, step = 0.0001)),
div(align="center", uiOutput("llag_plot_corr_method"))
),
bsTooltip(id = "llag_plot_confidence", title = "The evaluated p-values should carefully be interpreted because they are calculated based on pointwise confidence intervals rather than simultaneous confidence intervals (so there would be a multiple testing problem). Evaluation of p-values based on the latter will be implemented in the future extension of this function: Indeed, so far no theory has been developed for this. However, it is conjectured that the error distributions of the estimated cross-correlation functions are asymptotically independent if the grid is not dense too much, so p-values evaluated by this function will still be meaningful as long as sufficiently low significance levels are used.")
),
fluidRow(
column(1),
column(10,plotOutput("llag_plot", height = "600px"))
),
fluidRow(
column(1),
column(2,actionButton("llag_delete_analysis", label = "Delete")),
column(6),
column(2,actionButton("llag_deleteAll_analysis", label = "Delete All"))
),
HTML("<div id = 'llag_plot_howToRead'><h4><b>How to read the plot:</b><br/>If the lead-lag is positive: 'row.name' anticipates 'col.name of 'X' periods<br/>If the lead-lag is negative: 'row.name' follows 'col.name' with 'X' delay periods<br/><br/><b>'X'</b> are the numbers in the plot above.<br/>They are expressed in days if you are using time series, or in the same unit of measure of time if you are using numerical time index.<br/>The numbers in round brackets are correlations between the series shifted by the corresponding estimated lead-lag parameter.</h4></div>")
)))
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/eda/llag.R |
tabItem(tabName = "hedging",
fluidRow(
column(12,
h3("Here you can manage the risk of a portfolio composed of options and the underlying asset.",class = "hTitle"),
h4("The evolution of the underlying asset is simulated by the models you estimated in the Modeling section.", br(),
"After performing the simulation, click on the Show P&L button in the Profit&Loss tab and customize your portfolio.",br(),
"The Profit&Loss distribution of your portfolio will be displayed (it includes transaction costs that you can customize)."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,bsAlert("hedging_alert"))),
tabsetPanel(id = "panel_hedging", type = "tabs",
tabPanel(title = "Start simulations",
fluidRow(column(12, br(),
h4("Click on the model in order to simulate the evolution of the underlying asset"),
DT::dataTableOutput("hedging_databaseModels")
)
),
br(),
fluidRow(column(12,div(align="center",
br(),
fluidRow(
column(3,selectInput("hedging_type", label="Option Type:", c(Call="call", Put="put"))),
column(3,uiOutput("hedging_strike")),
column(3,dateInput("hedging_maturity", label="Maturity:", value = Sys.Date()+30)),
column(3,numericInput("hedging_optMarketPrice", label="Option Market Price:", value=NA, min = 0))
),
fluidRow(
column(3),
column(3,uiOutput("hedging_assMarketPrice")),
column(3,numericInput("hedging_lotMult", label="Number of Options per Lot:", value=1, min = 1)),
column(3)
),
fluidRow(
column(4),
column(4,numericInput("hedging_nSim", label="Number of Simulations", value=1000, min = 1)),
column(4)
),
fluidRow(
column(4),
column(4, actionButton("hedging_button_startComputation", label = "Start Computation", width = "50%"))
)
)))
),
tabPanel(title = "Profit&Loss",
bsModal(id="hedging_commissionPlan", trigger = "hedging_button_show", title = "Profit & Loss", size = "large",
div(id="hedging_body",align="center",
fluidRow(
column(3),
column(6, uiOutput("hedging_modal_id")),
column(3, uiOutput("hedging_modal_id_hidden"))
),
fluidRow(
column(3),
column(3,uiOutput("hedging_nOptLot_hedge")),
column(3,uiOutput("hedging_nAss_hedge"))
),
fluidRow(
column(9, plotOutput("hedging_plot_distribution")),
column(3,
sliderInput("hedging_slider_nBin", label = "Adjust bin width", min=1, max=100, value = 30, step = 1, ticks = FALSE),
br(),
sliderInput("hedging_slider_rangeHist", label = "Quantiles (%)", min = 0, max = 100, value = c(5,95), ticks = FALSE, step = 0.01),
uiOutput("hedging_quantiles_text"),
br(),br(),
uiOutput("hedging_capital_text"),
br(),br(),
actionButton("hedging_button_saveHedging", "Save Changes", width = "80%")
)
),
br(),
box(title = p("Modify Option", style="color:black; font-weight: bold;"),collapsible = TRUE, collapsed = FALSE, width = 12,
fluidRow(
column(4,uiOutput("hedging_type2")),
column(4,uiOutput("hedging_strike2")),
column(4,uiOutput("hedging_optMarketPrice2"))
)
),
box(title = p("Trading Costs", style="color:black; font-weight: bold;"),collapsible = TRUE, collapsed = FALSE, width = 12,
fluidRow(
column(3,br(),numericInput("hedging_percCostAss", label="Asset: Trading cost (%):", value=0.19, min = 0)),
column(3,br(),numericInput("hedging_minCostAss", label="Asset: Min trading cost:", value=2.95, min = 0)),
column(3,numericInput("hedging_rateShort", label="Asset: Yearly interest rate short position (%):", value=4.95, min = 0)),
column(3,numericInput("hedging_lotCostOpt", label="Option: Trading cost per lot:", value=5.95, min = 0))
)
)
)
),
fluidRow(column(12,br(),
DT::dataTableOutput("hedging_table_results")
)),
br(),
fluidRow(
column(2,actionButton(inputId = "hedging_button_show", label = "Show P&L")),
column(6),
column(2,actionButton(inputId = "hedging_button_delete", label = "Delete")),
column(2,actionButton(inputId = "hedging_button_deleteAll", label = "Delete All"))
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/finance/hedging.R |
tabItem(tabName = "home",
fluidRow(
column(12,
h1("Welcome to yuimaGUI", align = "center", class = "hTitle"),
h4("An amazingly powerful tool for your analyses", align = "center"),
hr(class = "hrHeader"),
h4("Get acquainted with yuimaGUI and learn how to get the most out of it in a few simple steps", class = "hTitle", align = "center"),
br()
)),
fluidRow(
column(8,
h4("Step 1", class = "hTitle"),
h4("Load the data you wish to analyze in the section Data I/O.", br(),
"The interface provides an easy way to load economic data (e.g. GDP) or a financial series (stocks and shares) from the Internet. If you prefer, you can load data from your own files.",br(),
"Once the data is loaded, you can then use the Explorative Data Analysis and Modeling sections."),
h4("Step 2", class = "hTitle"),
h4("Model data in section Modeling.", br(),
"Here you can fit models choosing between a number of default options or defining your own model.", br(),
"Now you are ready to go to the Simulate section."),
h4("Step 3", class = "hTitle"),
h4("Read the short explanation at the beginning of each section for further information. Enjoy!")
),
column(4,
br(), br(),
uiOutput("video_intro", align = "center")
)
),
fluidRow(
column(8,h4(),br(),br(),br(),
h4("Tips", class = "hTitle"),
h4("Press F11 to go to full screen.", br(),
"Press CTRL+ or CTRL- to zoom in and out.")
),
column(4,
h3(em("Developed by"), class = "hTitle", align = "center"),
h4("Emanuele Guidotti", align = "center"),
h3(em("in collaboration with"), class = "hTitle", align = "center"),
h4("Stefano M. Iacus & Lorenzo Mercuri", align = "center")
)
)
)
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/home/home.R |
tabItem(tabName="finData",
fluidRow(
column(12,
h3("Load Financial and Economic data",class = "hTitle"),
h4("For Stocks and Shares, select Yahoo as the source using the symbols you can find ",
a("here", href="http://finance.yahoo.com/lookup", target = "_blank"), ".",
br(),
"For currencies and metals, select Oanda as the source and type the two symbols divided by '/' (e.g. EUR/USD or XAU/USD ).",
"The symbols are available ", a("here",href="http://www.oanda.com/help/currency-iso-code", target = "_blank"), ".",
br(),
"Economic series are available from ",a("Federal Reserve Bank of St. Louis", href="https://research.stlouisfed.org/fred2/", target = "_blank"), ".",
"Follow this ", a("example",href="example.jpg", target = "_blank"), " to find the symbols.",
br(),
"Multiple symbols are allowed if divided by empty spaces and/or commas (e.g. AAPL FB CSCO or AAPL,FB,CSCO)."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,bsAlert("finDataAlert"))),
fluidRow(
column(6,
textInput(inputId="symb", value = NULL,label = "Insert Symbol"),
dateRangeInput(inputId="dR", label = "Download data from", start = "1900-01-01" ,end = Sys.Date()),
selectInput(inputId="sources", label = "Source", choices = c("Yahoo (OHLC data)" = "yahoo", "Oanda (Currencies & Metals)" = "oanda", "Federal Reserve Bank of St. Louis" = "FRED")),
tags$button(type="button", id="finDataGo", class = "action-button", em("Load data")),
br(),br(),br(),
column(9),
column(3,shinyjs::hidden(selectInput("scale_finDataPlot", label = "Chart Scale", choices = c("Linear", "Logarithmic"))))
),
column(6,
plotOutput("finDataPlot", height = "350px", brush = brushOpts(id = "finDataPlot_brush", delayType = "debounce", clip = TRUE, delay = 10000, resetOnNew = TRUE), dblclick = "finDataPlot_dbclick")
)
),
br(),
fluidRow(
column(12, DT::dataTableOutput("database1"))
),
shinyjs::hidden(div(id="buttons_DataIO_fin", br(),
fluidRow(
column(6),
column(2,downloadButton(outputId = "finDataSave", label = "Save")),
bsTooltip("finDataSave", title = "Save data to file", placement = "top"),
column(2,actionButton(inputId = "finDataDelete", label = "Delete")),
bsTooltip("finDataDelete", title = "Delete selected data", placement = "top"),
column(2,actionButton(inputId = "finDataDeleteAll", label = "Delete All")),
bsTooltip("finDataDeleteAll", title = "Delete all data that are displayed", placement = "top")
)
))
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/load_data/finData.R |
tabItem("yourData",
fluidRow(
column(12,
h3("Load data from Your Own Files",class = "hTitle"),
h4("Upload your file and specify its structure. A preview will appear below.",
br(),
"Declare if the file contains raw and/or column headers and specify what kind of field separator has to be used to read the data.",
br(),
"Each column will be uploaded as a different series. So you may wish to switch columns with rows if your file is organized differently.",
br(),
"Specify the format and the column to use as an index."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,bsAlert("yourDataAlert"))),
fluidRow(
column(5,
fileInput(inputId = "yourFile", width = "60%", label = "Choose file to upload", multiple = FALSE),
selectInput('yourFileHeader', 'Headers',width = "60%", choices = c("Auto"="Default","Only columns", "Only rows", "Both", "None"), selected = "Default"),
selectInput(inputId = 'yourFileSep',width = "60%", label = 'Field Separator', choices = c("Space"="default", "Comma"=',', "Semicolon"=';', "Tab"='\t'), selected = "default"),
uiOutput("yourFileIndex"),
uiOutput("yourFileFUN"),
br(),
div(align = "center", style="width:55%", box(background = switch(getOption("yuimaGUItheme"), "black"="black", "white"=NULL), width = 12, title = "More Settings", collapsible = TRUE, id = "yourFileMoreSettings", collapsed = TRUE,
textInput('yourFileDec', 'Decimal Separator', value = "."),
textInput('yourFileThnd', 'Thousands Separator', value = ""),
textInput("yourFileNA", "Missing Value string", value = "NA"),
numericInput("yourFileLine", "Begin from line", value = 1, min = 1, step = 1),
selectInput('yourFileSwitch', 'Switch rows/columns', choices = c("No"=FALSE, "Yes"=TRUE))
)
)),
column(7,
textOutput("yourFilePreviewText"),
DT::dataTableOutput("yourFilePreview"),
br(),
uiOutput("yourFileButton", align = "center")
)
),
br(),
br(),
fluidRow(
column(12,
DT::dataTableOutput("database2")
)
),
shinyjs::hidden(div(id="buttons_DataIO_file", br(),
fluidRow(
column(6),
column(2, downloadButton(outputId = "yourFileSave", label = "Save")),
bsTooltip("yourFileSave", title = "Save data to file", placement = "top"),
column(2,actionButton(inputId = "yourFileDelete", label = "Delete")),
bsTooltip("yourFileDelete", title = "Delete selected data", placement = "top"),
column(2,actionButton(inputId = "yourFileDeleteAll", label = "Delete All")),
bsTooltip("yourFileDeleteAll", title = "Delete all data that are displayed", placement = "top")
)
))
)
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/load_data/yourData.R |
tabItem(tabName="models",
fluidRow(column(12,
fluidRow(
column(12,
h3("Univariate Model Estimation",class = "hTitle"),
h4("Select the data and the model you wish to estimate. Each model will be fitted to each selected series.",
br(),
"Click on the Set Range and Advanced Settings buttons to customize the estimation process.",
br(),
"A number of default models are available but you can set your own model (tab Set model) and use it for estimation and/or simulation purposes."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,tabsetPanel(id = "panel_estimates", type = "tabs",
tabPanel(title = "Run estimation",
fluidRow(column(12,bsAlert("panel_run_estimation_alert"))),
br(),
fluidRow(
column(4,div(align="center",
selectInput("modelClass",label = "Model Class", choices = c("Diffusion process", "Fractional process", "Compound Poisson","Point Process", #"Levy process",
"CARMA", "COGARCH"), selected = "Diffusion process"),
uiOutput("model"),
uiOutput("jumps"),
uiOutput("pq_C")
)),
column(5,
fluidRow(shinyjs::hidden(h4(id="titlePrintModelLatex","Models to estimate:", style="font-size: 2em;"))),
fluidRow(uiOutput("PrintModelLatex"))
)
),
br(),
fluidRow(
column(4,
h4("Available data"),
DT::dataTableOutput("database3")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("database4")
),
column(4,
br(),br(),br(),br(),br(),br(),
div(actionButton("DisplayPlotsRange", label = "Set Range"), align = "center"),
br(),
div(actionButton("advancedSettingsButton", label = "Advanced Settings", align = "center"), align = "center")
)
),
br(),
fluidRow(
column(2,actionButton("buttonSelect_models_Univariate",label = "Select", align = "center")),
bsTooltip("buttonSelect_models_Univariate", title = "Select data to model", placement = "top"),
column(2,actionButton("buttonSelectAll_models_Univariate",label = "Select All", align = "center")),
bsTooltip("buttonSelectAll_models_Univariate", title = "Select all data that are displayed", placement = "top") ,
column(2,actionButton("buttonDelete_models_Univariate",label = "Delete", align = "center")),
bsTooltip("buttonDelete_models_Univariate", title = "Delete selected data", placement = "top"),
column(2,actionButton("buttonDeleteAll_models_Univariate",label = "Delete All", align = "center")),
bsTooltip("buttonDeleteAll_models_Univariate", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("EstimateModels", label = "Start Models Estimation", align = "center"))
)
),
tabPanel(title = "Set model",
fluidRow(column(12,bsAlert("panel_set_model_alert"))),
br(),
fluidRow(div(align="center",
column(6,
fluidRow(selectInput("usr_modelClass",label = "Model Class", width = "50%", choices = c("Diffusion process", "Fractional process", "Compound Poisson"), selected = "Diffusion process")),
fluidRow(textInput("usr_model_name", label = "Model Name", width = "50%")),
fluidRow(uiOutput("usr_modelClass_latex")),
fluidRow(uiOutput("usr_model_coeff")),
br(),br(),
fluidRow(
column(4),
column(4,actionButton("usr_model_button_save", label = "Save Model"))
)
),
column(6,div(id="usr_model_saved_div",align="center",
uiOutput("usr_model_saved"),
uiOutput("usr_model_saved_latex"),
br(),
actionButton("usr_model_button_delete", label = "Delete Model(s)")
))
))
),
tabPanel(title = "Estimates",
fluidRow(column(12,bsAlert("panel_estimates_alert"))),
shinyjs::hidden(div(id="estimates_info", fluidRow(
column(12,
textOutput("SymbolName"),
a(id = "linkMoreInfo", tags$u("More Info"), href = "#"),
bsModal(id = "MoreInfo", trigger = "linkMoreInfo", title = "Info", size = "large",
column(12,
fluidRow(uiOutput("text_MoreInfo")),
br(),
fluidRow(div(tableOutput("table_MoreInfo"), align="center")),
bsTooltip(id = "table_MoreInfo" ,"Estimates and Std. Errors are coherent with delta that has been used. No conversion to other units of measure has been applied.")
)
),
uiOutput("estimatedModelsLatex")
),
column(12,
div(align = "center",
tableOutput("estimatedModelsTable"),
shinyjs::hidden(selectInput(inputId = "baseModels", label = "Base", width = "150px", choices = c("Yearly","Semestral","Quarterly","Trimestral","Bimestral","Monthly","Weekly","Daily"), selected = "Yearly"))
)
)
))),
fluidRow(
column(12, br(), DT::dataTableOutput("databaseModels"))
),
br(),
fluidRow(
column(2,actionButton(inputId = "databaseModels_button_showResults", label = "Show Fitting")),
bsTooltip("databaseModels_button_showResults", title = "Available for: Diffusive Processes, Compound Poisson and COGARCH", placement = "top"),
column(6),
column(2,actionButton(inputId = "databaseModelsDelete", label = "Delete")),
bsTooltip("databaseModelsDelete", title = "Delete selected model", placement = "top"),
column(2,actionButton(inputId = "databaseModelsDeleteAll", label = "Delete All")),
bsTooltip("databaseModelsDeleteAll", title = "Delete all models that are displayed", placement = "top")
),
bsModal(id = "model_modal_fitting", title = "Fitting", trigger = "databaseModels_button_showResults", size = "Large",
div(id = "model_modal_fitting_body",
fluidRow(
column(2),
column(8, uiOutput("model_modal_model_id", align = "center"))
),
fluidRow(
column(12,
plotOutput("model_modal_plot_variance"),
plotOutput("model_modal_plot_intensity"),
plotOutput("model_modal_plot_distr"),
uiOutput("model_modal_plot_test", align = "center")
)
)
)
)
)
))),
bsModal(id="plotsRange", trigger = "DisplayPlotsRange", title = "Select range to use for models estimation", size = "large",
div(id="plotsRangeErrorMessage",align = "center",h3("Select some series from table 'Available Data'", class = "hModal")),
div(id="plotsRangeAll",
fluidRow(
column(8,
plotOutput("selectRange", height = "350px", brush = brushOpts(id = "selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "selectRange_dbclick"),
br(),
plotOutput("selectRangeReturns", height = "350px", brush = brushOpts(id = "selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "selectRange_dbclick")
),
column(4,
div(selectInput("scale_selectRange", label = "Chart Scale", choices = c("Linear", "Logarithmic (Y)", "Logarithmic (X)", "Logarithmic (XY)")), align = "center"),
br(),br(),br(),
uiOutput("plotsRangeSeries", align = "center"),
uiOutput("chooseRange", align = "center"),
uiOutput("chooseRange_specify", align = "center"),
column(6,
tags$button(type="button", id="buttonApplyRange", class = "action-button", em("Apply")),
bsTooltip("buttonApplyRange", title = "Apply Range to selected symbol", placement = "top")
),
column(6,
tags$button(type="button", id="buttonApplyAllRange", class = "action-button", em("Apply All")),
bsTooltip("buttonApplyAllRange", title = "Apply Range to all symbols that are displayed", placement = "bottom")
)
)
)
)
),
bsModal(id="advancedSettings", title="Advanced Settings", trigger = "advancedSettingsButton", size = "large",
div(id="advancedSettingsErrorMessage",align = "center",h3("Select some models and series (from table 'Available Data')", class = "hModal")),
div(id="advancedSettingsAll",
fluidRow(
column(6,
box(width = 12,div(align="center",
h3("Series Settings", class = "hModal"),
uiOutput("advancedSettingsSeries", align="center"),
fluidRow(
column(6,uiOutput("advancedSettingsDelta", align="center")),
column(6,uiOutput("advancedSettingsToLog", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="advancedSettingsButtonApplyDelta", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="advancedSettingsButtonApplyAllDelta", class = "action-button", em("Apply to All series")))
)
)),
box(width = 12,div(align="center",
h3("General Settings", class = "hModal"),
uiOutput("advancedSettingsMethod", align="center"),
uiOutput("advancedSettingsThreshold", align="center"),
fluidRow(
column(6,uiOutput("advancedSettingsTrials", align="center")),
column(6,uiOutput("advancedSettingsSeed", align="center"))
),
uiOutput("advancedSettingsJoint", align="center"),
uiOutput("advancedSettingsAggregation", align="center"),
uiOutput("advancedSettingsTimeout", align="center"),
fluidRow(
column(6, tags$button(type="button", id="advancedSettingsButtonApplyGeneral", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="advancedSettingsButtonApplyAllModelGeneral", class = "action-button", em("Apply to All series")))
),
fluidRow(column(12, tags$button(type="button", id="advancedSettingsButtonApplyAllGeneral", class = "action-button", em("Apply to All series & models"))))
))
),
column(6,
box(width = 12,div(align="center",
h3("Model Settings", class = "hModal"),
uiOutput("advancedSettingsModel", align="center"),
uiOutput("advancedSettingsParameter", align="center"),
uiOutput("advancedSettingsFixed", align="center"),
uiOutput("advancedSettingsStart", align="center"),
fluidRow(
column(6,uiOutput("advancedSettingsStartMin", align="center")),
column(6,uiOutput("advancedSettingsStartMax", align="center"))
),
fluidRow(
column(6,uiOutput("advancedSettingsLower", align="center")),
column(6,uiOutput("advancedSettingsUpper", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="advancedSettingsButtonApplyModel", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="advancedSettingsButtonApplyAllModel", class = "action-button", em("Apply to All series")))
)
))
)
)
)
)
))
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/modeling/models.R |
tabItem(tabName="multi_models",
fluidRow(column(12,
fluidRow(
column(12,
h3("Multivariate Model Estimation",class = "hTitle"),
h4("Select the data and the model you wish to estimate. The model will be fitted to the selected series.",
br(),
"Click on buttons Set Range and Advanced Settings to customize the estimation process.",
br(),
'Building your own multivariate model is not possible at the moment.'
#"Some default models are available but you can set your own model (tab 'Set model') and use it for estimation and/or simulation purposes."
),
hr(class = "hrHeader")
)
),
fluidRow(column(12,tabsetPanel(id = "multi_panel_estimates", type = "tabs",
tabPanel(title = "Run estimation",
fluidRow(column(12,bsAlert("multi_panel_run_estimation_alert"))),
br(),
fluidRow(
column(4,div(align="center",
selectInput("multi_modelClass",label = "Model Class", choices = c("Diffusion process"), selected = "Diffusion process"),
uiOutput("multi_model"),
uiOutput("multi_jumps"),
uiOutput("multi_pq_C")
)),
column(5,
fluidRow(shinyjs::hidden(h4(id="multi_titlePrintModelLatex","Models to estimate:", style="font-size: 2em;"))),
fluidRow(uiOutput("multi_PrintModelLatex"))
)
),
br(),
fluidRow(
column(4,
h4("Available data"),
DT::dataTableOutput("multi_database3")
),
column(4,
h4("Selected data"),
DT::dataTableOutput("multi_database4")
),
column(4,
br(),br(),br(),br(),br(),br(),
div(actionButton("multi_DisplayPlotsRange", label = "Set Range"), align = "center"),
br(),
div(actionButton("multi_advancedSettingsButton", label = "Advanced Settings", align = "center"), align = "center")
)
),
br(),
fluidRow(
column(2,actionButton("multi_buttonSelect_models_Univariate",label = "Select", align = "center")),
bsTooltip("multi_buttonSelect_models_Univariate", title = "Select data to model", placement = "top"),
column(2,actionButton("multi_buttonSelectAll_models_Univariate",label = "Select All", align = "center")),
bsTooltip("multi_buttonSelectAll_models_Univariate", title = "Select all data that are displayed", placement = "top") ,
column(2,actionButton("multi_buttonDelete_models_Univariate",label = "Delete", align = "center")),
bsTooltip("multi_buttonDelete_models_Univariate", title = "Delete selected data", placement = "top"),
column(2,actionButton("multi_buttonDeleteAll_models_Univariate",label = "Delete All", align = "center")),
bsTooltip("multi_buttonDeleteAll_models_Univariate", title = "Delete all data that are displayed", placement = "top"),
column(4,actionButton("multi_EstimateModels", label = "Start Models Estimation", align = "center"))
)
),
# tabPanel(title = "Set model",
# fluidRow(column(12,bsAlert("multi_panel_set_model_alert"))),
# br(),
# fluidRow(div(align="center",
# column(6,
# fluidRow(selectInput("multi_usr_modelClass",label = "Model Class", width = "50%", choices = c("Diffusion process", "Fractional process", "Compound Poisson"), selected = "Diffusion process")),
# fluidRow(textInput("multi_usr_model_name", label = "Model Name", width = "50%")),
# fluidRow(uiOutput("multi_usr_modelClass_latex")),
# fluidRow(uiOutput("multi_usr_model_coeff")),
# br(),br(),
# fluidRow(
# column(4),
# column(4,actionButton("multi_usr_model_button_save", label = "Save Model"))
# )
# ),
# column(6,div(id="multi_usr_model_saved_div",align="center",
# uiOutput("multi_usr_model_saved"),
# uiOutput("multi_usr_model_saved_latex"),
# br(),
# actionButton("multi_usr_model_button_delete", label = "Delete Model(s)")
# ))
# ))
# ),
tabPanel(title = "Estimates",
fluidRow(column(12,bsAlert("multi_panel_estimates_alert"))),
shinyjs::hidden(div(id="multi_estimates_info", fluidRow(
column(12,
textOutput("multi_SymbolName"),
a(id = "multi_linkMoreInfo", tags$u("More Info"), href = "#"),
bsModal(id = "multi_MoreInfo", trigger = "multi_linkMoreInfo", title = "Info", size = "large",
column(12,
fluidRow(uiOutput("multi_text_MoreInfo")),
br(),
fluidRow(div(tableOutput("multi_table_MoreInfo"), align="center")),
bsTooltip(id = "multi_table_MoreInfo" ,"Estimates and Std. Errors are coherent with delta that has been used. No conversion to other units of measure has been applied.")
)
),
uiOutput("multi_estimatedModelsLatex")
),
column(12,
div(align = "center",
tableOutput("multi_estimatedModelsTable"),
shinyjs::hidden(selectInput(inputId = "multi_baseModels", label = "Base", width = "150px", choices = c("Yearly","Semestral","Quarterly","Trimestral","Bimestral","Monthly","Weekly","Daily"), selected = "Yearly"))
)
)
))),
fluidRow(
column(12, br(), DT::dataTableOutput("multi_databaseModels"))
),
br(),
fluidRow(
column(2,actionButton(inputId = "multi_databaseModels_button_showResults", label = "Show Fitting")),
bsTooltip("multi_databaseModels_button_showResults", title = "Available for: Diffusive Processes", placement = "top"),
column(6),
column(2,actionButton(inputId = "multi_databaseModelsDelete", label = "Delete")),
bsTooltip("multi_databaseModelsDelete", title = "Delete selected model", placement = "top"),
column(2,actionButton(inputId = "multi_databaseModelsDeleteAll", label = "Delete All")),
bsTooltip("multi_databaseModelsDeleteAll", title = "Delete all models that are displayed", placement = "top")
),
bsModal(id = "multi_model_modal_fitting", title = "Fitting", trigger = "multi_databaseModels_button_showResults", size = "Large",
div(id = "multi_model_modal_fitting_body",
fluidRow(
column(2),
column(8,
uiOutput("multi_model_modal_model_id", align = "center"),
uiOutput("multi_model_modal_series_id", align = "center")
)
),
fluidRow(
column(12,
plotOutput("multi_model_modal_plot_variance"),
plotOutput("multi_model_modal_plot_intensity"),
plotOutput("multi_model_modal_plot_distr"),
uiOutput("multi_model_modal_plot_test", align = "center")
)
)
)
)
)
))),
bsModal(id="multi_plotsRange", trigger = "multi_DisplayPlotsRange", title = "Select range to use for models estimation", size = "large",
div(id="multi_plotsRangeErrorMessage",align = "center",h3("Select some series from table 'Available Data'", class = "hModal")),
div(id="multi_plotsRangeAll",
fluidRow(
column(8,
plotOutput("multi_selectRange", height = "350px", brush = brushOpts(id = "multi_selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "multi_selectRange_dbclick"),
br(),
plotOutput("multi_selectRangeReturns", height = "350px", brush = brushOpts(id = "multi_selectRange_brush", delayType = "debounce", delay = 10000, resetOnNew = TRUE), dblclick = "multi_selectRange_dbclick")
),
column(4,
div(selectInput("multi_scale_selectRange", label = "Chart Scale", choices = c("Linear", "Logarithmic (Y)", "Logarithmic (X)", "Logarithmic (XY)")), align = "center"),
br(),br(),br(),
uiOutput("multi_plotsRangeSeries", align = "center"),
uiOutput("multi_chooseRange", align = "center"),
uiOutput("multi_chooseRange_specify", align = "center"),
column(6,
tags$button(type="button", id="multi_buttonApplyRange", class = "action-button", em("Apply")),
bsTooltip("multi_buttonApplyRange", title = "Apply Range to selected symbol", placement = "top")
),
column(6,
tags$button(type="button", id="multi_buttonApplyAllRange", class = "action-button", em("Apply All")),
bsTooltip("multi_buttonApplyAllRange", title = "Apply Range to all symbols that are displayed", placement = "bottom")
)
)
)
)
),
bsModal(id="multi_advancedSettings", title="Advanced Settings", trigger = "multi_advancedSettingsButton", size = "large",
div(id="multi_advancedSettingsErrorMessage",align = "center",h3("Select some models and series (from table 'Available Data')", class = "hModal")),
div(id="multi_advancedSettingsAll",
fluidRow(
column(6,
box(width = 12,div(align="center",
h3("Series Settings", class = "hModal"),
uiOutput("multi_advancedSettingsSeries", align="center"),
fluidRow(
column(6,uiOutput("multi_advancedSettingsDelta", align="center")),
column(6,uiOutput("multi_advancedSettingsToLog", align="center"))
),
fluidRow(
column(6, tags$button(type="button", id="multi_advancedSettingsButtonApplyDelta", class = "action-button", em("Apply"))),
column(6, tags$button(type="button", id="multi_advancedSettingsButtonApplyAllDelta", class = "action-button", em("Apply to All series")))
)
)),
box(width = 12,div(align="center",
h3("General Settings", class = "hModal"),
uiOutput("multi_advancedSettingsMethod", align="center"),
uiOutput("multi_advancedSettingsThreshold", align="center"),
fluidRow(
column(6,uiOutput("multi_advancedSettingsTrials", align="center")),
column(6,uiOutput("multi_advancedSettingsSeed", align="center"))
),
uiOutput("multi_advancedSettingsJoint", align="center"),
uiOutput("multi_advancedSettingsAggregation", align="center"),
fluidRow(
column(3),
column(6, tags$button(type="button", id="multi_advancedSettingsButtonApplyGeneral", class = "action-button", em("Apply")))
)
))
),
column(6,
box(width = 12,div(align="center",
h3("Model Settings", class = "hModal"),
uiOutput("multi_advancedSettingsModel", align="center"),
uiOutput("multi_advancedSettingsParameter", align="center"),
uiOutput("multi_advancedSettingsFixed", align="center"),
uiOutput("multi_advancedSettingsStart", align="center"),
fluidRow(
column(6,uiOutput("multi_advancedSettingsStartMin", align="center")),
column(6,uiOutput("multi_advancedSettingsStartMax", align="center"))
),
fluidRow(
column(6,uiOutput("multi_advancedSettingsLower", align="center")),
column(6,uiOutput("multi_advancedSettingsUpper", align="center"))
),
fluidRow(
column(3),
column(6, tags$button(type="button", id="multi_advancedSettingsButtonApplyModel", class = "action-button", em("Apply")))
)
))
)
)
)
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/modeling/multi_models.R |
tabItem(tabName = "multi_simulate",
fluidRow(
column(12,
h3("Multivariate Simulation",class = "hTitle"),
h4("Select the estimated models you wish to simulate.",
#br(),
#"If you want to simulate a model that has not been estimated, you can use the 'Non-estimated models' tab.",
br(),
"Click on the Set Simulation and Advanced Settings buttons to customize the simulation process."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,tabsetPanel(id = "panel_multi_simulations", type = "tabs",
tabPanel(title = "Estimated models",
fluidRow(column(12,bsAlert("panel_multi_simulate_model_alert"))),
fluidRow(column(12, br(),
h4("Available models"),
DT::dataTableOutput("multi_simulate_databaseModels"),
br(),
fluidRow(
column(8),
column(2,actionButton(inputId = "multi_simulate_button_selectModels", label = "Select")),
bsTooltip("multi_simulate_button_selectModels", title = "Select model", placement = "top"),
column(2,actionButton(inputId = "multi_simulate_button_selectAllModels", label = "Select All")),
bsTooltip("multi_simulate_button_selectAllModels", title = "Select all models that are displayed", placement = "top")
)
)
)),
tabPanel(title = "Non-estimated models",
fluidRow(column(12,bsAlert("panel_multi_simulate_equation_alert"))),
fluidRow(
uiOutput("multi_simulate_PrintModelLatex")
),
fluidRow(
column(6, br(), div(align="center",
fluidRow(
column(1),
column(5,selectInput("multi_simulate_model_usr_selectClass", label = "Class", choices = c("Diffusion process"))),
column(5,uiOutput("multi_simulate_model_usr_selectModel"))
),
uiOutput("multi_simulate_model_usr_ID"),
uiOutput("multi_simulate_model_usr_selectJumps"),
uiOutput("multi_simulate_model_usr_selectDimension"),
fluidRow(
column(1),
column(5,uiOutput("multi_simulate_model_usr_selectParam")),
column(5,uiOutput("multi_simulate_model_usr_param"))
),
fluidRow(
column(4),
column(4,actionButton("multi_simulate_model_usr_button_save", label = "Save", align = "center"))
)
)),
column(6,
br(),
DT::dataTableOutput("multi_simulate_model_usr_table"),
br(),
fluidRow(
column(3,actionButton("multi_simulate_model_usr_button_select", label = "Select")),
column(3,actionButton("multi_simulate_model_usr_button_selectAll", label = "Select All")),
column(3,actionButton("multi_simulate_model_usr_button_delete", label = "Delete")),
column(3,actionButton("multi_simulate_model_usr_button_deleteAll", label = "Delete All"))
)
)
)
),
tabPanel(title = "Simulations",
fluidRow(column(12,bsAlert("panel_multi_simulations_alert"))),
br(),
fluidRow(column(12, DT::dataTableOutput("multi_simulate_monitor_table"))),
br(),
fluidRow(
column(2,actionButton(inputId = "multi_simulate_monitor_button_showSimulation", label = "Show Simulations")),
bsTooltip("multi_simulate_monitor_button_showSimulation", title = "Show selected simulation", placement = "top"),
column(6),
column(2,actionButton(inputId = "multi_simulate_monitor_button_delete", label = "Delete")),
bsTooltip("multi_simulate_monitor_button_delete", title = "Delete selected simulation", placement = "top"),
column(2,actionButton(inputId = "multi_simulate_monitor_button_deleteAll", label = "Delete All")),
bsTooltip("multi_simulate_monitor_button_deleteAll", title = "Delete all simulations that are displayed", placement = "top")
)
)
))),
div(id="multi_div_simulations",
fluidRow(
column(12,br(),br(),br()),
column(8,
h4("Selected Models"),
DT::dataTableOutput("multi_simulate_selectedModels")
),
column(4,
br(),br(),br(),br(),br(),br(),
div(actionButton("multi_simulate_button_setSimulation", label = "Set Simulation"), align = "center"),
br(),
div(actionButton("multi_simulate_button_advancedSettings", label = "Advanced Settings", align = "center"), align = "center")
)
),
br(),
fluidRow(
column(4,actionButton("multi_simulation_button_deleteModels",label = "Delete", align = "center")),
bsTooltip("multi_simulation_button_deleteModels", title = "Delete selected models", placement = "top"),
column(4,actionButton("multi_simulation_button_deleteAllModels",label = "Delete All", align = "center")),
bsTooltip("multi_simulation_button_deleteAllModels", title = "Delete all models that are displayed", placement = "top"),
column(4,actionButton("multi_simulate_simulateModels", label = "Start Simulation", align = "center"))
)
),
bsModal(id="multi_simulate_showSimulation", trigger = "multi_simulate_monitor_button_showSimulation", title = "Simulation", size = "large",
fluidRow(column(12,
fluidRow(column(12,
div(align="center",
uiOutput("multi_simulate_showSimulation_simID")
)
)
),
fluidRow(column(3),
column(3, uiOutput('multi_simulate_showSimulation_plot_series1')),
column(3, uiOutput('multi_simulate_showSimulation_plot_series2')),
column(3)
),
fluidRow(div(id="multi_simulate_showSimulation_plot_div", align = "center",
column(12,
plotlyOutput("multi_simulate_showSimulation_plot"),
downloadButton(outputId = "multi_simulate_showSimulation_button_saveTrajectory", label = "Save Trajectories"),
hr()
)
)),
br(),
fluidRow(shinyjs::hidden(div(id="multi_simulate_showSimulation_hist_div",
column(8,
plotlyOutput("multi_simulate_showSimulation_hist")
),
column(4,
div(align="center",br(),br(),br(),
sliderInput("multi_simulate_showSimulation_hist_probability_slider", width = "75%", min = 0, max = 100, value = c(5, 95), label = "Quantiles Marginal Distribution (%)", step = 0.01, ticks=FALSE),
uiOutput("multi_simulate_showSimulation_hist_text"),
br(),
downloadButton(outputId = "multi_simulate_showSimulation_button_saveHist", label = "Save Histogram")
)
)
)))
))
),
bsModal(id="multi_simulate_setSimulation", trigger = "multi_simulate_button_setSimulation", title = "Set Simulation", size = "small",
tags$style(type = "text/css", ".datepicker{z-index: 1100 !important;}"),
div(id="multi_simulate_setSimulation_errorMessage",align = "center", h3("Select some models first", class = "hModal")),
div(id="multi_simulate_setSimulation_body", align = "center",
uiOutput("multi_simulate_modelID"),
br(),
box(width = 12,
uiOutput("multi_simulate_range"),
column(6,tags$button(type="button", id="multi_simulate_button_apply_range", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="multi_simulate_button_applyAll_range", class = "action-button", em("Apply All")))
),
box(width =12,
uiOutput("multi_simulate_xinit_symb"),
uiOutput("multi_simulate_xinit"),
column(6,tags$button(type="button", id="multi_simulate_button_apply_xinit", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="multi_simulate_button_applyAll_xinit", class = "action-button", em("Apply All")))
),
box(width = 12,
uiOutput("multi_simulate_nsim"),
uiOutput("multi_simulate_nstep"),
column(6,tags$button(type="button", id="multi_simulate_button_apply_nsim", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="multi_simulate_button_applyAll_nsim", class = "action-button", em("Apply All")))
)
)
),
bsModal(id="multi_simulate_advancedSettings", trigger = "multi_simulate_button_advancedSettings", title = "Advanced Settings", size = "small",
div(id="multi_simulate_advancedSettings_errorMessage", align = "center", h3("Select some models first", class = "hModal")),
div(id="multi_simulate_advancedSettings_body", align = "center",
uiOutput("multi_simulate_advancedSettings_modelID"),
uiOutput("multi_simulate_seed"),
uiOutput("multi_simulate_traj"),
column(6,tags$button(type="button", id="multi_simulate_button_apply_advancedSettings", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="multi_simulate_button_applyAll_advancedSettings", class = "action-button", em("Apply All")))
)
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/simulation/multivariate.R |
tabItem(tabName = "simulate",
fluidRow(
column(12,
h3("Univariate Simulation",class = "hTitle"),
h4("Select the estimated models you wish to simulate.",
br(),
"If you want to simulate a model that has not been estimated, you can use the Non-estimated models tab.",
br(),
"Click on the Set Simulation and Advanced Settings buttons to customize the simulation process."),
hr(class = "hrHeader")
)
),
fluidRow(column(12,tabsetPanel(id = "panel_simulations", type = "tabs",
tabPanel(title = "Estimated models",
fluidRow(column(12,bsAlert("panel_simulate_model_alert"))),
fluidRow(column(12, br(),
h4("Available models"),
DT::dataTableOutput("simulate_databaseModels"),
br(),
fluidRow(
column(8),
column(2,actionButton(inputId = "simulate_button_selectModels", label = "Select")),
bsTooltip("simulate_button_selectModels", title = "Select model", placement = "top"),
column(2,actionButton(inputId = "simulate_button_selectAllModels", label = "Select All")),
bsTooltip("simulate_button_selectAllModels", title = "Select all models that are displayed", placement = "top")
)
)
)),
tabPanel(title = "Non-estimated models",
fluidRow(column(12,bsAlert("panel_simulate_equation_alert"))),
fluidRow(
uiOutput("simulate_PrintModelLatex")
),
fluidRow(
column(6, br(), div(align="center",
fluidRow(
column(1),
column(5,selectInput("simulate_model_usr_selectClass", label = "Class", choices = c("Diffusion process", "Fractional process", "Compound Poisson", "Point Process"))),
column(5,uiOutput("simulate_model_usr_selectModel"))
),
uiOutput("simulate_model_usr_ID"),
uiOutput("simulate_model_usr_selectJumps"),
fluidRow(
column(1),
column(5,uiOutput("simulate_model_usr_selectParam")),
column(5,uiOutput("simulate_model_usr_param"))
),
fluidRow(
column(4),
column(4,actionButton("simulate_model_usr_button_save", label = "Save", align = "center"))
)
)),
column(6,
br(),
DT::dataTableOutput("simulate_model_usr_table"),
br(),
fluidRow(
column(3,actionButton("simulate_model_usr_button_select", label = "Select")),
column(3,actionButton("simulate_model_usr_button_selectAll", label = "Select All")),
column(3,actionButton("simulate_model_usr_button_delete", label = "Delete")),
column(3,actionButton("simulate_model_usr_button_deleteAll", label = "Delete All"))
)
)
)
),
tabPanel(title = "Simulations",
fluidRow(column(12,bsAlert("panel_simulations_alert"))),
br(),
fluidRow(column(12, DT::dataTableOutput("simulate_monitor_table"))),
br(),
fluidRow(
column(2,actionButton(inputId = "simulate_monitor_button_showSimulation", label = "Show Simulations")),
bsTooltip("simulate_monitor_button_showSimulation", title = "Show selected simulation", placement = "top"),
column(6),
column(2,actionButton(inputId = "simulate_monitor_button_delete", label = "Delete")),
bsTooltip("simulate_monitor_button_delete", title = "Delete selected simulation", placement = "top"),
column(2,actionButton(inputId = "simulate_monitor_button_deleteAll", label = "Delete All")),
bsTooltip("simulate_monitor_button_deleteAll", title = "Delete all simulations that are displayed", placement = "top")
)
)
))),
div(id="div_simulations",
fluidRow(
column(12,br(),br(),br()),
column(8,
h4("Selected Models"),
DT::dataTableOutput("simulate_selectedModels")
),
column(4,
br(),br(),br(),br(),br(),br(),
div(actionButton("simulate_button_setSimulation", label = "Set Simulation"), align = "center"),
br(),
div(actionButton("simulate_button_advancedSettings", label = "Advanced Settings", align = "center"), align = "center")
)
),
br(),
fluidRow(
column(4,actionButton("simulation_button_deleteModels",label = "Delete", align = "center")),
bsTooltip("simulation_button_deleteModels", title = "Delete selected models", placement = "top"),
column(4,actionButton("simulation_button_deleteAllModels",label = "Delete All", align = "center")),
bsTooltip("simulation_button_deleteAllModels", title = "Delete all models that are displayed", placement = "top"),
column(4,actionButton("simulate_simulateModels", label = "Start Simulation", align = "center"))
)
),
bsModal(id="simulate_showSimulation", trigger = "simulate_monitor_button_showSimulation", title = "Simulation", size = "large",
fluidRow(column(12,
fluidRow(column(8,div(align="center",uiOutput("simulate_showSimulation_simID")))),
fluidRow(div(id="simulate_showSimulation_plot_div", align = "center",
column(8,
plotOutput("simulate_showSimulation_plot", height = "350px", click = "simulate_showSimulation_plot_click")
),
column(4,br(),br(),br(),
div(selectInput("simulate_showSimulation_plot_scale", label = "Chart Scale", choices = c("Linear", "Logarithmic (Y)", "Logarithmic (X)", "Logarithmic (XY)")), align = "center"),
br(),br(),br(),br(),br(),br(),br(),
downloadButton(outputId = "simulate_showSimulation_button_saveTrajectory", label = "Save Trajectories")
)
)),
br(),
fluidRow(div(id="simulate_showSimulation_hist_div",
column(8,
plotOutput("simulate_showSimulation_hist", height = "350px")
),
column(4,
div(align="center",br(),br(),br(),
sliderInput("simulate_showSimulation_hist_nBins", width = "75%",min = 1, max = 100, step = 1,value = 30, ticks = FALSE, label = "Adjust bin width"),
sliderInput("simulate_showSimulation_hist_probability_slider", width = "75%", min = 0, max = 100, value = c(5, 95), label = "Quantiles (%)", step = 0.01, ticks=FALSE),
uiOutput("simulate_showSimulation_hist_text"),
br(),
downloadButton(outputId = "simulate_showSimulation_button_saveHist", label = "Save Histogram")
)
)
))
))
),
bsModal(id="simulate_setSimulation", trigger = "simulate_button_setSimulation", title = "Set Simulation", size = "small",
tags$style(type = "text/css", ".datepicker{z-index: 1100 !important;}"),
div(id="simulate_setSimulation_errorMessage",align = "center", h3("Select some models first", class = "hModal")),
div(id="simulate_setSimulation_body", align = "center",
uiOutput("simulate_modelID"),
br(),
box(width = 12,
uiOutput("simulate_range"),
column(6,tags$button(type="button", id="simulate_button_apply_range", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="simulate_button_applyAll_range", class = "action-button", em("Apply All")))
),
box(width =12,
uiOutput("simulate_xinit"),
column(6,tags$button(type="button", id="simulate_button_apply_xinit", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="simulate_button_applyAll_xinit", class = "action-button", em("Apply All")))
),
box(width = 12,
uiOutput("simulate_nsim"),
uiOutput("simulate_nstep"),
column(6,tags$button(type="button", id="simulate_button_apply_nsim", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="simulate_button_applyAll_nsim", class = "action-button", em("Apply All")))
)
)
),
bsModal(id="simulate_advancedSettings", trigger = "simulate_button_advancedSettings", title = "Advanced Settings", size = "small",
div(id="simulate_advancedSettings_errorMessage", align = "center", h3("Select some models first", class = "hModal")),
div(id="simulate_advancedSettings_body", align = "center",
uiOutput("simulate_advancedSettings_modelID"),
uiOutput("simulate_seed"),
uiOutput("simulate_traj"),
column(6,tags$button(type="button", id="simulate_button_apply_advancedSettings", class = "action-button", em("Apply"))),
column(6,tags$button(type="button", id="simulate_button_applyAll_advancedSettings", class = "action-button", em("Apply All")))
)
)
) | /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui/simulation/univariate.R |
header <- dashboardHeader(
title = "yuimaGUI"
)
sidebar<-dashboardSidebar(
sidebarMenu(
menuItem("Home", tabName = "home", icon = icon("home")),
menuItem("Data I/O", tabName = "data_section", icon = icon("upload"),
menuSubItem("Financial & Economic Data", tabName = "finData"),
menuSubItem("Your Data", tabName = "yourData")
),
menuItem("Explorative Data Analysis", tabName = "eda_section", icon = icon("map"),
menuSubItem("Change Point Estimation", tabName = "changepoint"),
menuSubItem("Clustering", tabName = "cluster"),
menuSubItem("Lead-Lag & Correlation", tabName = "llag")
),
menuItem("Modeling", tabName = "models_section", icon = icon("sliders"),
menuSubItem("Univariate", tabName = "models"),
menuSubItem("Multivariate", tabName = "multi_models")
),
menuItem("Simulation", tabName = "simulate_section", icon = icon("area-chart"),
menuSubItem("Univariate", tabName = "simulate"),
menuSubItem("Multivariate", tabName = "multi_simulate")
),
hr(),
menuItem("Finance", tabName = "finance",
menuSubItem("P&L distribution", tabName = "hedging")
),
hr(),br(),
div(id="sessionButtons",
fluidRow( downloadButton("saveSession", label = "Save Session")),
br(),
fluidRow(column(1),column(9,fileInput("loadSession", label = "Load Session", multiple=FALSE)))
),
hr(),br(),
div(id="theyuimaprojct",
a("User Guide", href="https://yuimaproject.com/wp-content/uploads/2018/08/manual_yuimagui_v1-02.pdf", target="_blank"),br(),
br()
)
)
)
body<-dashboardBody(
tags$head(tags$link(rel = "stylesheet", type = "text/css", href = paste(getOption("yuimaGUItheme"), ".css", sep = ""))),
tags$head(tags$link(rel="shortcut icon", href="yuimaLogo.ico")),
shinyjs::useShinyjs(),
withMathJax(),
tabItems(
source("ui/home/home.R", local = TRUE)$value,
source("ui/load_data/finData.R", local = TRUE)$value,
source("ui/load_data/yourData.R", local = TRUE)$value,
source("ui/eda/cluster.R", local = TRUE)$value,
source("ui/eda/changepoint.R", local = TRUE)$value,
source("ui/eda/llag.R", local = TRUE)$value,
source("ui/modeling/models.R", local = TRUE)$value,
source("ui/modeling/multi_models.R", local = TRUE)$value,
source("ui/simulation/univariate.R", local = TRUE)$value,
source("ui/simulation/multivariate.R", local = TRUE)$value,
source("ui/finance/hedging.R", local = TRUE)$value
)
)
ui <- dashboardPage(header,sidebar,body)
| /scratch/gouwar.j/cran-all/cranData/yuimaGUI/inst/yuimaGUI/ui.R |
## biorxiv_get_publication <- function(url) {
## # url <- "https://www.biorxiv.org/search/visualization%20numresults%3A75%20sort%3Arelevance-rank"
## x <- readLines(url)
## pub <- x[grep("/content/10.1101", x)]
## pub_url <- gsub(".*(/content/[[:digit:]\\.v/]+).*", "\\1", pub)
## pub_url <- paste0("https://www.biorxiv.org", pub_url)
## pub_title <- gsub("<[^>]+>", "", pub) %>%
## sub("^\\s+", "", .) %>%
## sub("\\s+$", "", .)
## data.frame(url = pub_url,
## title = pub_title)
## }
## biorxiv_get_correspondance <- function(url) {
## # url <- "https://www.biorxiv.org/content/10.1101/701680v3"
## x <- readLines(url)
## i <- grep("citation_author\"", x)
## j <- grep("citation_author_email", x)
## idx <- vapply(j, function(ii) {
## jj <- ii - i
## i[which(jj == min(jj[jj >0]))]
## }, numeric(1))
## author <- x[idx] %>% unique %>%
## sub(".*content=\"([^\"]+).*", "\\1", .)
## email <- x[j] %>% unique %>%
## sub(".*content=\"([^\"]+).*", "\\1", .)
## data.frame(author = author, email = email) %>% unique
## }
## url <- "https://www.biorxiv.org/search/visualization%20numresults%3A75%20sort%3Arelevance-rank"
## y <- biorxiv_get_publication(url)
## xx <- lapply(y$url, function(x) {
## cat("parsing", x, "\n")
## biorxiv_get_correspondance(x)
## })
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/biorxiv.R |
##' @rdname yulab-cache
##' @export
initial_cache <- function() {
pos <- 1
envir <- as.environment(pos)
assign(".yulabCache", new.env(), envir = envir)
}
##' @rdname yulab-cache
##' @export
get_cache <- function() {
if (!exists(".yulabCache", envir = .GlobalEnv)) {
initial_cache()
}
get(".yulabCache", envir = .GlobalEnv)
}
##' @rdname yulab-cache
##' @export
rm_cache <- function() {
if (exists(".yulabCache", envir = .GlobalEnv)) {
rm(".yulabCache", envir = .GlobalEnv)
}
}
##' @rdname yulab-cache
##' @export
initial_cache_item <- function(item) {
env <- get_cache()
assign(item, list(), envir = env)
}
##' @rdname yulab-cache
##' @export
get_cache_item <- function(item) {
env <- get_cache()
if (!exists(item, envir = env)) {
initial_cache_item(item)
}
get(item, envir = env, inherits = FALSE)
}
##' @rdname yulab-cache
##' @export
rm_cache_item <- function(item) {
env <- get_cache()
if (exists(item, envir = env)) {
rm(list = item, envir = env)
}
}
##' cache intermediate data
##'
##' Yulab provides a set of utilities to cache intermediate data,
##' including initialize the cached item, update cached item and rmove the cached item, etc.
##'
##' @rdname yulab-cache
##' @param item the name of the cached item
##' @param elements elements to be cached in the item
##' @return return the cache environment, item or selected elements, depends on the functions.
##' @importFrom utils modifyList
##' @export
##' @examples
##' \dontrun{
##' slow_fib <- function(x) {
##' if (x < 2) return(1)
##' slow_fib(x-2) + slow_fib(x-1)
##' }
##'
##' fast_fib <- function(x) {
##' if (x < 2) return(1)
##' res <- get_cache_element('fibonacci', as.character(x))
##' if (!is.null(res)) {
##' return(res)
##' }
##' res <- fast_fib(x-2) + fast_fib(x-1)
##' e <- list()
##' e[[as.character(x)]] <- res
##' update_cache_item('fibonacci', e)
##' return(res)
##' }
##'
##' system.time(slow_fib(30))
##' system.time(fast_fib(30))
##'
##' }
update_cache_item <- function(item, elements) {
msg <- "new elements should be stored as a named list"
if (!inherits(elements, 'list')) {
stop(msg)
}
if (is.null(names(elements))) {
stop(msg)
}
if(any(names(elements) == "")) {
stop(msg)
}
env <- get_cache()
res <- get_cache_item(item)
res <- modifyList(res, elements)
assign(item, res, envir = env)
}
##' @rdname yulab-cache
##' @export
get_cache_element <- function(item, elements) {
x <- get_cache_item(item)
n <- length(elements)
if (n == 1) return(x[[elements]])
return(x[elements])
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/cache.R |
#' all possible combinations of n sets
#'
#' @title combinations
#' @param n number of sets
#' @return a list of all combinations
#' @importFrom utils combn
#' @export
combinations <- function(n){
l <- lapply(seq_len(n), function(x){
m <- combn(n,x)
mat2list(m)
})
unlist(l, recursive = F)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/combinations.R |
##' run system command
##'
##'
##' @title exec
##' @param command system command to run
##' @return An `exec` instance that stores system command outputs
##' @export
##' @author Guangchuang Yu
exec <- function(command) {
res <- system(command, intern=TRUE)
structure(res, class = "exec")
}
##' @method print exec
##' @export
print.exec <- function(x, ...) {
cat(x, sep='\n')
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/exec.R |
#' @rdname yread
#' @export
yread_tsv <- function(file, reader = utils::read.delim,
params = list(),
cache_dir = tempdir()
) {
# e.g. params = list(sep = "\t", header = FALSE)
yread(file,
reader = reader,
params = params,
cache_dir = cache_dir
)
}
#' read file with caching
#'
#' This function read a file (local or url) and cache the content.
#' @title yread
#' @rdname yread
#' @param file a file or url
#' @param reader a function to read the 'file_url'
#' @param params a list of parameters that passed to the 'reader'
#' @param cache_dir a folder to store cache files
#' @return the output of using the 'reader' to read the 'file_url' with parameters specified by the 'params'
#' @author Yonghe Xia and Guangchuang Yu
#' @importFrom fs path_join
#' @importFrom digest digest
#' @export
yread <- function(file, reader = readLines, params = list(),
cache_dir = tempdir()) {
# Generate a unique cache filename based on the file URL
cache_filename <- fs::path_join(c(cache_dir, paste0(digest::digest(file), ".rds")))
# Check if the cached file exists
if (file.exists(cache_filename)) {
# If cached file exists, load and return the cached data
cached_data <- readRDS(cache_filename)
return(cached_data)
} else {
# If cached file does not exist, read and cache the data
data <- do.call(reader, args = c(file, params))
saveRDS(data, cache_filename)
return(data)
}
}
##' read clipboard
##'
##'
##' @title read.cb
##' @param reader function to read the clipboard
##' @param ... parameters for the reader
##' @return clipboard content, output type depends on the output of the reader
##' @author Guangchuang Yu
##' @importFrom utils read.table
##' @export
read.cb <- function(reader = read.table, ...) {
os <- Sys.info()[1]
if (os == "Darwin") {
clip <- pipe("pbpaste")
} else {
clip <- "clipboard"
}
reader(clip, ...)
}
##' open selected directory or file
##'
##'
##' @title o
##' @param file to be open; open working directory by default
##' @return No return value, called for opening specific directory or file
##' @examples
##' \dontrun{
##' ## to open current working directory
##' o()
##' }
##' @export
##' @author Guangchuang Yu
o <- function(file=".") {
file <- normalizePath(file)
os <- Sys.info()[1]
if (is.rserver()) {
if (dir.exists(file)) {
stop("open directory in RStudio Server is not supported.")
}
rserver_ip <- getOption("rserver_ip")
if (!is.null(rserver_ip)) {
rserver_port <- getOption("rserver_port") %||% '8787'
if (!startsWith(rserver_ip, "http")) {
rserver_ip = paste0("http://", rserver_ip)
}
utils::browseURL(
paste0(
paste(rserver_ip, rserver_port, sep=":"),
"/file_show?path=",
file
))
} else {
file.edit <- get("file.edit")
file.edit(file)
}
} else if (os == "Darwin") {
cmd <- paste("open", file)
system(cmd)
} else if (os == "Linux") {
cmd <- paste("xdg-open", file, "&")
system(cmd)
} else if (os == "Windows") {
## wd <- sub("/", "\\", getwd())
## cmd <- paste("explorer", wd)
## suppressWarnings(shell(cmd))
cmd <- paste("start", file)
shell(cmd)
}
}
is.rserver <- function(){
RStudio.Version = tryCatch(get("RStudio.Version"), error = function(e) NULL)
if(is.null(RStudio.Version)) return(FALSE)
if(!is.function(RStudio.Version)) return(FALSE)
RStudio.Version()$mode == 'server'
}
##' Open data frame in Excel. It can be used in pipe.
##'
##'
##' @title show_in_excel
##' @param .data a data frame to be open
##' @return original .data
##' @export
##' @author Guangchuang Yu
show_in_excel <- function(.data) {
f <- tempfile(fileext = '.csv')
utils::write.csv(.data, file=f)
o(f)
invisible(.data)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/file.R |
##' install github package
##'
##' it download the zip file first and use `install_zip` to install it
##' @title install_zip_gh
##' @param repo github repo
##' @param ref github branch, default is HEAD, which means the default branch of the GitHub repo
##' @param args argument to build package
##' @return No return value, called for installing github package
##' @importFrom utils download.file
##' @export
##' @author Guangchuang Yu
install_zip_gh <- function(repo, ref = "HEAD", args = "--no-build-vignettes") {
## repo <- 'GuangchuangYu/nCov2019'
url <- paste0('https://codeload.github.com/', repo, '/zip/', ref)
f <- tempfile(fileext=".zip")
method <- "auto"
if (.Platform$OS.type == "windows") method <- "curl"
utils::download.file(url, destfile=f, method = method)
if (!is_valid_zip(f)) {
stop("Invalid zip file downloaded, please check the 'ref' parameter to set a correct github branch.")
}
install_zip(f, args=args)
}
##' install R package from zip file of source codes
##'
##'
##' @title install_zip
##' @param file zip file
##' @param args argument to build package
##' @return No return value, called for install R package from zip file of source codes
##' @export
##' @author Guangchuang Yu
install_zip <- function(file, args = "--no-build-vignettes") {
dir <- tempfile()
utils::unzip(file, exdir=dir)
fs <- list.files(path=dir, full.names=T)
#if (length(fs) == 1 && dir.exists(fs)) {
# dir <- fs
#}
## dir <- paste0(dir, '/', basename(repo), '-master')
dir <- fs[which.max(file.info(fs)$atime)]
if ("INDEX" %in% list.files(dir)) {
# file is binary package
pkg <- file
} else {
# file is zip of package source
## remotes::install_local(path=dir, ..., force=TRUE)
## pkg <- pkgbuild::build(dir, args=args)
build <- get_fun_from_pkg('pkgbuild', 'build')
pkg <- build(dir, args=args)
}
utils::install.packages(pkg, repos=NULL)
}
is_valid_zip <- function(zipfile) {
fs <- tryCatch(utils::unzip(zipfile, list=TRUE), error = function(e) NULL)
if (is.null(fs)) return(FALSE)
return(TRUE)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/install_zip.R |
##' rbind a list
##'
##'
##' @title rbindlist
##' @param x a list that have similar elements that can be rbind to a data.frame
##' @return data.frame
##' @author Guangchuang Yu
##' @export
rbindlist <- function(x) {
do.call('rbind', x)
}
##' Convert a list of vector to a data.frame object.
##'
##'
##' @title Convert a list of vector (e.g, gene IDs) to a data.frame object
##' @param inputList A list of vector
##' @return a data.frame object.
##' @export
ls2df <- function(inputList) {
# ldf <- lapply(1:length(inputList), function(i) {
ldf <- lapply(seq_len(length(inputList)), function(i) {
data.frame(category=rep(names(inputList[i]),
length(inputList[[i]])),
value=inputList[[i]])
})
do.call('rbind', ldf)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/list.R |
##' convert a matrix to a tidy data frame
##' (from wide to long format as described in the tidyverse concept)
##'
##'
##' @title mat2df
##' @param x the input matrix
##' @return a data.frame in long format with the 'value' column stores the original values
##' and 'row' and 'col' columns stored in row and column index as in x
##' @examples
##' x <- matrix(1:15, nrow = 3)
##' mat2df(x)
##' @export
##' @author Guangchuang Yu
mat2df <- function(x) {
nr <- nrow(x)
nc <- ncol(x)
d <- data.frame(
value = as.vector(x),
row = rep(1:nr, times = nc),
col = rep(1:nc, each = nr)
)
return(d)
}
##' convert a matrix to a list
##'
##'
##' @title mat2list
##' @param x the input matrix
##' @return a list that contains matrix columns as its elements
##' @examples
##' x <- matrix(1:15, nrow = 3)
##' mat2list(x)
##' @export
mat2list <- function(x){
lapply(seq_len(ncol(x)), function(i) x[,i])
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/matrix-utils.R |
##' loading a package
##'
##' The function use 'library()' to load the package.
##' If the package is not installed, the function will try to install it before loading it.
##' @title pload
##' @param package package name
##' @param action function used to install package.
##' If 'action = "auto"', it will try to use 'BiocManager::install()' if it is available.
##' @return the selected package loaded to the R session
##' @importFrom rlang as_name
##' @importFrom rlang enquo
##' @importFrom rlang check_installed
##' @importFrom cli cli_h2
##' @importFrom utils getFromNamespace
##' @export
##' @author Guangchuang Yu
pload <- function(package, action = "auto") {
pkg <- as_name(enquo(package))
if (action == "auto") {
if (is.installed("BiocManager")) {
install <- getFromNamespace("install", "BiocManager")
action <- function(package, ask=FALSE, update=FALSE, ...){
install(package, ask=ask, update = update, ...)
}
} else {
action <- NULL
}
}
check_installed(pkg, action = action)
cli::cli_h2(sprintf("loading the package: %s", pkg))
library(pkg, character.only = TRUE)
}
##' get reverse dependencies
##'
##'
##' @title get_dependencies
##' @param pkg package name
##' @param repo 'CRAN' and/or 'BioC'
##' @return reverse dependencies
## @importFrom BiocInstaller biocinstallRepos
##' @importFrom tools package_dependencies
##' @export
##' @author Guangchuang Yu
get_dependencies <- function(pkg, repo=c("CRAN", "BioC")) {
rp <- get_repo(repo)
db <- utils::available.packages(repo=rp)
tools::package_dependencies(pkg, db=db, reverse=TRUE)
}
get_repo <- function(repo = c("CRAN", "BioC")) {
rp <- c()
if ('CRAN' %in% repo) {
cran <- getOption("repos")["CRAN"]
if (is.null(cran)) {
cran <- "http://cloud.r-project.org/"
}
rp <- c(rp, cran)
}
if ('BioC' %in% repo) {
bioc <- getOption("BioC_mirror")
if (is.null(bioc)) {
bioc <- "https://mirrors.tuna.tsinghua.edu.cn/bioconductor/"
}
rp <- c(rp, bioc)
}
## options(repos = biocinstallRepos())
sub("/$", "", rp)
}
##' Extract package title
##'
##'
##' @title packageTitle
##' @param pkg package name
##' @param repo 'CRAN' and/or 'BioC'
##' @return reverse dependencies
##' @importFrom utils packageDescription
##' @export
##' @author Guangchuang Yu
packageTitle <- function(pkg, repo='CRAN') {
title <- tryCatch(packageDescription(pkg)$Title, error=function(e) NULL)
if (is.null(title)) {
repo_url <- get_repo(repo)
if (repo == "CRAN") {
url <- sprintf("%s/package=%s", repo_url, pkg)
} else {
bioc_type <- c("bioc", "workflows", "data/annotation", "data/experiment")
url <- sprintf("%s/packages/release/%s/html/%s.html", repo_url, bioc_type, pkg)
}
## x <- tryCatch(readLines(url), error = function(e) NULL)
## if (is.null(x)) return("")
for (u in url) {
x <- tryCatch(yread(u), error = function(e) NULL)
if (!is.null(x)) {
break()
}
}
if (is.null(x)) {
return(NA)
}
i <- grep('^\\s*<h2>', x)
if (grepl("</h2>$", x[i])) {
xx <- x[i]
} else {
j <- grep('</h2>$', x)
xx <- paste(x[i:j], collapse=" ")
}
title <- gsub('</h2>$', '', gsub('\\s*<h2>', '', xx))
}
sub("^\\w+\\s*:\\s*", "", gsub("\n", " ", title))
}
##' Check whether the input packages are installed
##'
##' This function check whether the input packages are installed
##' @title is.installed
##' @param packages package names
##' @return logical vector
##' @export
##' @examples
##' is.installed(c("dplyr", "ggplot2"))
##' @author Guangchuang Yu
is.installed <- function(packages) {
vapply(packages, function(package) {
system.file(package=package) != ""
}, logical(1))
}
##' Check whether the input packages are installed
##'
##' This function check whether the input packages are installed. If not, it asks the user whether to install the missing packages.
##' @title check_pkg
##' @param pkg package names
##' @param reason the reason to check the pkg. If NULL, it will set the reason to the parent call.
##' @param ... additional parameters that passed to `rlang::check_installed()`
##' @return see also [check_installed][rlang::check_installed]
##' @export
##' @importFrom rlang check_installed
##' @author Guangchuang Yu
check_pkg <- function(pkg, reason=NULL, ...) {
# v1
#
# if (!is.installed(pkg)) {
# msg <- sprintf("%s is required, please install it first", pkg)
# stop(msg)
# }
if (is.null(reason)) {
call <- sys.call(1L)
reason <- sprintf("for %s()", as.character(call)[1])
}
rlang::check_installed(pkg, reason, ...)
}
##' load function from package
##'
##'
##' @title get_fun_from_pkg
##' @param pkg package
##' @param fun function
##' @return function
##' @export
##' @examples
##' get_fun_from_pkg('utils', 'zip')
##' @author Guangchuang Yu
get_fun_from_pkg <- function(pkg, fun) {
## v1
##
## requireNamespace(pkg)
## eval(parse(text=paste0(pkg, "::", fun)))
## v2
##
## require(pkg, character.only = TRUE)
## eval(parse(text = fun))
# check_pkg(pkg)
utils::getFromNamespace(fun, pkg)
}
##' print md text of package with link to homepage (CRAN or Bioconductor)
##'
##'
##' @rdname cran-bioc-pkg
##' @param pkg package name
##' @return md text string
##' @export
##' @author Guangchuang Yu
CRANpkg <- function(pkg) {
cran <- "https://CRAN.R-project.org/package"
fmt <- "[%s](%s=%s)"
sprintf(fmt, pkgfmt(pkg), cran, pkg)
}
##' @rdname cran-bioc-pkg
##' @export
Biocpkg <- function(pkg) {
sprintf("[%s](http://bioconductor.org/packages/%s)", pkgfmt(pkg), pkg)
}
##' print md text of package with link to github repo
##'
##'
##' @rdname github-pkg
##' @param user github user
##' @param pkg package name
##' @return md text string
##' @export
##' @author Guangchuang Yu
Githubpkg <- function(user, pkg) {
gh <- "https://github.com"
fmt <- "[%s](%s/%s/%s)"
sprintf(fmt, pkgfmt(pkg), gh, user, pkg)
}
##' print md text of link to a pakcage
##'
##'
##' @title mypkg
##' @param pkg package name
##' @param url package url
##' @return md text string
##' @export
##' @author Guangchuang Yu
mypkg <- function(pkg, url) {
fmt <- "[%s](%s)"
sprintf(fmt, pkgfmt(pkg), url)
}
pkgfmt <- function(pkg) {
fmt <- getOption('yulab.utils_pkgfmt', default="%s")
sprintf(fmt, pkg)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/pkg-utils.R |
##' @rdname regexpr-style
##' @export
set_PCRE <- function() {
options(regexpr_use_perl = TRUE)
}
##' @rdname regexpr-style
##' @export
set_TRE <- function() {
options(regexpr_use_perl = FALSE)
}
##' @rdname regexpr-style
##' @export
use_perl <- function() {
res <- getOption("regexpr_use_perl", default = auto_set_regexpr_style())
return(res)
}
##' switch regular expression style (PCRE vs TRE)
##'
##' The `set_regexpr_style()` allows user to specify which style to be used,
##' while the `auto_set_regexpr_style()` automatically set the style depdending on
##' the operating system (TRE for Windows and PCRE for other OSs (Linux and Mac)).
##'
##' `set_PCRE()` force to use PCRE style while `set_TRE()` force to use TRE.
##'
##' Note that all these functions are not change the behavior of `gsub()` and `regexpr()`.
##' The functions are just set a global option to store the user's choice of whether using `perl = TRUE`.
##'
##' Users can access the option via `use_perl()` and pass the return value to `gusb()` or `regexpr()` to specify the style in use.
##'
##' @rdname regexpr-style
##' @param style one of 'PCRE' or 'TRE'
##' @return logical value of whether use perl
##' @references <https://stackoverflow.com/questions/47240375/regular-expressions-in-base-r-perl-true-vs-the-default-pcre-vs-tre>
##' @export
##' @author Guangchuang Yu
set_regexpr_style <- function(style) {
if (missing(style)) {
message("style is not specific, set automatically.")
auto_set_regexpr_style()
} else {
style <- match.arg(style, c("PCRE", "TRE"))
if (style == "PCRE") {
set_PCRE()
} else {
set_TRE()
}
}
res <- getOption("regexpr_use_perl")
invisible(res)
}
##' @rdname regexpr-style
##' @export
auto_set_regexpr_style <- function() {
os <- Sys.info()[1]
if (os == "Windows") {
set_TRE()
res <- FALSE
} else {
set_PCRE()
res <- TRUE
}
invisible(res)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/regexpr.R |
##' normalized data by range
##'
##'
##' @title scale-range
##' @param data the input data.
##' @return normalized data
##' @export
##' @author Guangchuang Yu
scale_range <- function(data) {
normalized_data <- apply(data, 2, function(x) {
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
})
as.data.frame(normalized_data)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/scale.R |
##' download publication via scihub
##'
##' using scihub to download publication using doi
##' @rdname scihub-dl
##' @name scihub_dl
##' @param doi doi
##' @param scihub scihub website
##' @param download whether download the pdf file
##' @return pdf url
##' @author Guangchuang Yu
##' @export
scihub_dl <- function(doi, scihub = 'sci-hub.tw', download=TRUE) {
url <- paste0('https://', scihub, '/', doi)
x <- readLines(url)
i <- grep('id = "pdf"', x)
pdf_url <-sub(".*(//.*\\.pdf).*", "https:\\1", x[i])
if (download) {
outfile <- sub(".*/", "", pdf_url)
utils::download.file(pdf_url, destfile = outfile)
}
invisible(pdf_url)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/scihub-dl.R |
##' wraping long string to multiple lines
##'
##'
##' @title str_wrap
##' @param string input string
##' @param width the maximum number of characters before wrapping to a new line
##' @return update strings with new line character inserted
##' @export
##' @author Guangchuang Yu and Erqiang Hu
str_wrap <- function(string, width = getOption("width")) {
##
## actually, there is a base::strwrap() function available
##
# x <- gregexpr(' ', string)
# vapply(seq_along(x),
# FUN = function(i) {
# y <- x[[i]]
# n <- nchar(string[i])
# len <- (c(y,n) - c(0, y)) ## length + 1
# idx <- len > width
# j <- which(!idx)
# if (length(j) && max(j) == length(len)) {
# j <- j[-length(j)]
# }
# if (length(j)) {
# idx[j] <- len[j] + len[j+1] > width
# }
# idx <- idx[-length(idx)] ## length - 1
# start <- c(1, y[idx] + 1)
# end <- c(y[idx] - 1, n)
# words <- substring(string[i], start, end)
# paste0(words, collapse="\n")
# },
# FUN.VALUE = character(1)
# )
result <- vapply(string,
FUN = function(st) {
words <- list()
i <- 1
while(nchar(st) > width) {
if (length(grep(" ", st)) == 0) break
y <- gregexpr(' ', st)[[1]]
n <- nchar(st)
y <- c(y,n)
idx <- which(y < width)
# When the length of first word > width
if (length(idx) == 0) idx <- 1
# Split the string into two pieces
# The length of first piece is small than width
words[[i]] <- substring(st, 1, y[idx[length(idx)]] - 1)
st <- substring(st, y[idx[length(idx)]] + 1, n)
i <- i + 1
}
words[[i]] <- st
paste0(unlist(words), collapse="\n")
},
FUN.VALUE = character(1)
)
names(result) <- NULL
result
}
##' Detect the presence or absence of a pattern at the beginning or end of a string or string vector.
##'
##'
##' @title str_starts
##' @rdname str-starts-ends
##' @param string input string
##' @param pattern pattern with which the string starts or ends
##' @param negate if TRUE, return non-matching elements
##' @return a logical vector
##' @export
##' @author Guangchuang Yu
str_starts <- function(string, pattern, negate=FALSE) {
pattern <- paste0('^', pattern)
str_detect(string, pattern, negate)
}
##' @rdname str-starts-ends
##' @export
str_ends <- function(string, pattern, negate=FALSE) {
pattern <- paste0(pattern, '$')
str_detect(string, pattern, negate)
}
##' @importFrom stats setNames
str_detect <- function(string, pattern, negate) {
res <- setNames(
vapply(string, grepl, pattern=pattern,
FUN.VALUE=logical(1)),
NULL)
if (negate) res <- !res
return(res)
}
##' Extract a substring using a pattern
##'
##'
##' @title str_extract
##' @rdname str-extract
##' @param string input string
##' @param pattern a regular expression to describe the pattern to extracted from the 'string'
##' @return substring
##' @export
##' @author Guangchuang Yu
str_extract <- function(string, pattern) {
i <- regexpr(pattern, string)
j <- attr(i, 'match.length')
res <- substring(string, i, i+j-1)
res[res == ""] <- NA
return(res)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/str-utils.R |
sudo_install <- function(pkgs) {
## pkgs_str <- paste0('"', pkgs, '"') %>%
## paste(collapse=',') %>%
## paste("c(", ., ")")
## rcmd0 <- 'options(repos = c(CRAN = "https://mirrors.e-ducation.cn/CRAN/"));'
os <- Sys.info()[1]
if (os == "Windows") {
sudo <- ""
} else {
sudo <- "sudo"
}
for (pkg in pkgs) {
pkg <- paste0('"', pkg, '"')
rcmd <- paste0('install.packages(', pkg, ')')
## rcmd <- paste0(rcmd0, rcmd)
cmd <- paste0(sudo, " Rscript -e '", rcmd, "'")
system(cmd)
}
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/sudo-install.R |
`%||%` <- function(a, b) ifelse(is.null(a), b, a)
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/utilities.R |
##' @importFrom memoise memoise
.onLoad <- function(libname, pkgname) {
yread <<- memoise::memoise(yread)
yread_tsv <<- memoise::memoise(yread_tsv)
}
| /scratch/gouwar.j/cran-all/cranData/yulab.utils/R/zzz.R |
#' Convert the objects loaded from YAML fragments into a tree
#'
#' If the [data.tree::data.tree] package is installed, this function
#' can be used to convert a list of objects, as loaded from extracted
#' YAML fragments, into a [data.tree::Node()].
#'
#' @param x Either a list of YAML fragments loaded from a file with
#' [load_yaml_fragments()], or a list of such lists loaded from all files
#' in a directory with [load_yaml_dir()].
#' @param idName The name of the field containing each elements' identifier,
#' used to build the data tree when there are references to a parent from a child
#' element.
#' @param parentIdName The name of the field containing references to an element's
#' parent element (i.e. the field containing the identifier of the corresponding
#' parent element).
#' @param childrenName The name of the field containing an element's children, either
#' as a list of elements, or using the 'shorthand' notation, in which case a vector
#' is supplied with the identifiers of the children.
#' @param autofill A named vector where the names represent fields to fill with
#' the values of the fields specified in the vector values. Note that autofill
#' replacements are only applied if the fields to be autofilled (i.e. the names of
#' the vector specified in `autofill`) do not already have a value.
#' @param rankdir How to plot the plot when it's plotted: the default `"LR"` plots from
#' left to right. Specify e.g. `"TB"` to plot from top to bottom.
#' @param directed Whether the edges should have arrows (`"forward"` or `"backward"`)
#' or not (`"false"`).
#' @param silent Whether to provide (`FALSE`) or suppress (`TRUE`) more detailed progress updates.
#'
#' @return a [data.tree::Node()] object.
#'
#' @examples
#' loadedYum <- yum::load_yaml_fragments(text=c(
#' "---",
#' "-",
#' " id: firstFragment",
#' "---",
#' "Outside of YAML",
#' "---",
#' "-",
#' " id: secondFragment",
#' " parentId: firstFragment",
#' "---",
#' "Also outside of YAML"));
#' yum::build_tree(loadedYum);
#' @export
build_tree <- function(x,
idName = 'id',
parentIdName = 'parentId',
childrenName = 'children',
autofill = c(label = 'id'),
rankdir="LR",
directed="false",
silent=TRUE) {
if (!requireNamespace("data.tree", quietly = TRUE)) {
stop("To build a tree, the \"data.tree\" package is required. ",
"Please install it using `install.packages('data.tree');`.",
call. = FALSE);
}
if ("simplifiedYum" %in% class(x)) {
### Nothing more to do - can be processed directly.
} else if ("yumFromDir" %in% class(x)) {
x <-
unlist(x,
recursive=FALSE);
} else if (!("yumFromFile" %in% class(x)) &&
!("yumRecursion" %in% class(x)) &&
!("yumFromList" %in% class(x))) {
stop("I can only process the objects resulting from ",
"a call to 'load_yaml_fragments', 'load_yaml_dir, ",
" or 'load_yaml_list', which have class ",
"'yumFromFile', 'yumFromDir', and 'yumFromList'. The ",
"object you provided has ",
ifelse(length(class(x)) == 1,
"class ",
"classes "),
vecTxtQ(class(x)),
".");
}
if (is.null(x)) {
return(x);
}
if (!is.null(x[[idName]])) {
### We have an identifier, so this is a node in itself. First check
### and if necessary, clean up this node.
if (!silent) {
cat("Passed object has identifier '",
x[[idName]],
"'.",
sep="");
}
for (currentAutofill in names(autofill)) {
if (is.null(x[[currentAutofill]])) {
x[[currentAutofill]] <- x[[autofill[currentAutofill]]];
}
}
### Then, check whether it has children.
if (is.null(x[[childrenName]])) {
### If not, convert this node into a Node and return it.
res <- data.tree::Node$new(x[[idName]]);
for (currentSub in (setdiff(names(x),
idName))) {
res[[currentSub]] <-
x[[currentSub]];
}
return(res);
} else {
### Check whether the children are 'shorthand children', and if
### they are, construct the proper sub-object first.
if (is.atomic(x[[childrenName]])) {
x[[childrenName]] <-
lapply(x[[childrenName]],
function(childId) {
currentChild <- list();
currentChild[[idName]] <-
childId;
for (currentAutofill in names(autofill)) {
currentChild[[currentAutofill]] <-
childId;
}
return(currentChild);
});
}
if (!silent) {
cat("Converting it to a data.tree Node and returning it.");
}
### Then convert this node into a Node
res <-
data.tree::FromListExplicit(x,
nameName=idName,
childrenName=childrenName);
if (!silent) {
cat("Tree root object has name '",
res$name,
"'.", sep="");
}
### Check for missing labels and/or codes and fill them with the identifiers
res$Do(function(node) {
for (currentAutofill in names(autofill)) {
if (is.null(node[[currentAutofill]])) {
if (autofill[currentAutofill] == idName) {
node[[currentAutofill]] <-
node$name;
} else {
node[[currentAutofill]] <-
node[[autofill[currentAutofill]]];
}
}
}
});
### Set plotting styles
data.tree::SetGraphStyle(res,
directed = directed);
data.tree::SetGraphStyle(res,
rankdir = rankdir);
### Return the result
return(res);
}
} else {
### This is a list of nodes, so pass each on to this function and
### collect the results; then start building the tree.
if (!silent) {
cat("Passed object does not have an identifier; processing it as a list of objects.");
}
nodeList <-
lapply(lapply(x,
function(xToStructure) {
if (is.null(xToStructure)) {
return(structure(list(),
class = 'yumRecursion'));
} else {
return(structure(xToStructure,
class = 'yumRecursion'));
}
}),
build_tree,
idName = 'id',
parentIdName = parentIdName,
childrenName = childrenName,
autofill = autofill,
silent = silent);
nodeIds <-
data.tree::Get(nodeList,
'name');
if (!silent) {
cat("Processed ",
length(nodeList),
" nodes (",
vecTxtQ(nodeIds),
").", sep="");
}
### If it's a single node, just return it immediately.
if (length(nodeList) == 1) {
if (!silent) {
cat("Single node, so returning it.");
}
return(nodeList[[1]]);
}
### Create the data tree object
resTree <- data.tree::Node$new();
### Add all children of nodes without an id as children of the resTree root
if (any(nchar(nodeIds)==0)) {
for (nodeWithoutId in nodeList[nchar(nodeIds)==0]) {
if (!silent) {
cat("Found a set of nodes without identifiers; adding the children to the root of the code tree.");
}
for (subNodeWithoutId in nodeWithoutId$children) {
resTree$AddChildNode(subNodeWithoutId);
if (!silent) {
cat("Added '",
subNodeWithoutId$name,
"' to the root of the code tree.\n", sep="");
}
}
}
### Then remove them from the nodeList
nodeList <-
nodeList[nchar(nodeIds)>0];
}
### Check which nodes have a parent
parentIds <-
data.tree::Get(nodeList,
parentIdName);
nodesWithoutParents <-
nodeList[unlist(is.na(parentIds))];
nodesWithParents <-
nodeList[unlist(!is.na(parentIds))];
### Attach those that don't to the root.
for (i in nodesWithoutParents) {
resTree$AddChildNode(i);
if (!silent) {
cat("Attached parentless node '",
i$name,
"' to the root of the code tree.");
}
}
### For those that do, insert them at the appropriate place.
for (i in seq_along(nodesWithParents)) {
if (!silent) {
cat("Starting to process node '",
nodesWithParents[[i]]$name,
"' to find its parent '",
nodesWithParents[[i]][[parentIdName]],
"'.\n",
sep="");
}
parentNode <-
data.tree::FindNode(resTree,
nodesWithParents[[i]][[parentIdName]]);
if (!is.null(parentNode)) {
### Parent is already in the coding tree; attach this child node.
parentNode$AddChildNode(nodesWithParents[[i]]);
if (!silent) {
cat("Attached node '",
nodesWithParents[[i]]$name,
"' to its parent node '",
parentNode$name,
"' in the code tree.\n",
sep="");
}
} else {
### Parent is not in the coding tree; look in the other nodes with
### parents that we still have to process
if (i == length(nodesWithParents)) {
stop(paste0("Node with identifier '", nodesWithParents[[i]]$name,
"' has specified parent '", nodesWithParents[[i]][[parentIdName]],
"' but no node with that identifier exists."));
} else {
foundParent <- FALSE;
for (j in (i+1):length(nodesWithParents)) {
parentNode <-
data.tree::FindNode(nodesWithParents[[j]],
nodesWithParents[[i]][[parentIdName]]);
if (!is.null(parentNode)) {
### Parent is not yet in the coding tree; attach this child node.
parentNode$AddChildNode(nodesWithParents[[i]]);
foundParent <- TRUE;
if (!silent) {
cat("Attached node '",
nodesWithParents[[i]]$name,
"' to its parent node '",
parentNode$name,
"', for now outside the code tree.",
sep="");
}
}
}
if (!foundParent) {
print(parentNode);
stop(paste0("Node with identifier '", nodesWithParents[[i]]$name,
"' has specified parent '", nodesWithParents[[i]][[parentIdName]],
"' but no node with that identifier exists (all node identifiers are ",
vecTxtQ(nodeIds), ")."));
}
}
}
}
### Set plotting styles
data.tree::SetGraphStyle(resTree,
directed = directed);
data.tree::SetGraphStyle(resTree,
rankdir = rankdir);
### Return the result
return(resTree);
}
}
| /scratch/gouwar.j/cran-all/cranData/yum/R/build_tree.R |
#' Delete all YAML fragments from a file
#'
#' These function deletes all YAML fragments from a file, returning
#' a character vector without the lines that specified the YAML
#' fragments.
#'
#' @param file The path to a file to scan; if provided, takes precedence
#' over `text`.
#' @param text A character vector to scan, where every element should
#' represent one line in the file; can be specified instead of `file`.
#' @param delimiterRegEx The regular expression used to locate YAML
#' fragments.
#' @param ignoreOddDelimiters Whether to throw an error (FALSE) or
#' delete the last delimiter (TRUE) if an odd number of delimiters is
#' encountered.
#' @param silent Whether to be silent (TRUE) or informative (FALSE).
#'
#' @return A list of character vectors.
#' @examples
#' yum::delete_yaml_fragments(text=c("---", "First YAML fragment", "---",
#' "Outside of YAML",
#' "---", "Second fragment", "---",
#' "Also outside of YAML"));
#'
#' @export
delete_yaml_fragments <- function(file,
text,
delimiterRegEx = "^---$",
ignoreOddDelimiters = FALSE,
silent=TRUE) {
if (missing(file)) {
if (missing(text)) {
stop("Provide either a `file` or a `text` to scan!");
} else {
allLines <- text;
}
} else {
allLines <- readLines(file);
}
yamlFragments <- grep(delimiterRegEx,
allLines);
if (length(yamlFragments) == 0) {
return(allLines);
}
if (!is.even(length(yamlFragments))) {
if (ignoreOddDelimiters) {
yamlFragments <-
yamlFragments[-length(yamlFragments)];
} else {
stop("Extracted an uneven number of lines with specifications ",
"(the regular expression for the specification ",
"delimiter that was specified was '", delimiterRegEx,
"'). To ignore the last delimiter, specify ",
"'ignoreOddDelimiters=TRUE'.");
}
}
yamlFragmentIndices <- seq_along(yamlFragments);
### Rewritten using base R to remove `purrr` dependency
# indexSets <- purrr::map2(.x=yamlFragments[is.odd(yamlFragmentIndices)],
# .y=yamlFragments[is.even(yamlFragmentIndices)],
# .f=`:`);
indexPairIndices <-
list(yamlFragments[is.odd(yamlFragmentIndices)],
yamlFragments[is.even(yamlFragmentIndices)]);
indexSets <-
lapply(seq_along(indexPairIndices[[1]]),
function(pair) {
return(seq(indexPairIndices[[1]][pair],
indexPairIndices[[2]][pair]));
});
return(allLines[-do.call(c,
indexSets)]);
}
| /scratch/gouwar.j/cran-all/cranData/yum/R/delete_yaml_fragments.R |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.