content
stringlengths 0
14.9M
| filename
stringlengths 44
136
|
|---|---|
#' @export
ssea.oneway<-function(number.group,mc,sigma,n.weight,ci.width,type=c('s','b'), alpha=0.05){
type<-match.arg(type)
k<-dim(mc)[1]
r<-number.group
v<-sigma^2
o<-0
n=2 ############## !!!!!!!!!!!
while(o!=1){
#print(n)
mn<-n*n.weight
s<-c()
for (i in 1:k){
s<-c(s, sqrt( v*( sum( ((mc[i,])^2)/mn) ) ) )
}
if (match.arg(type)=='b'){
st<-qt(1 - alpha/(2*k), sum(mn)-r)
}else{
st<-sqrt((r - 1)*qf(1 - alpha, r - 1, sum(mn)-r))
}
if( sum((2*st*s)<=ci.width)==k ){
o=1
}else{
n=n+1
}
}
sample.size<-cbind(1:r,mn)
colnames(sample.size)<-c('treatment','n_i')
ci<-cbind(1:k,st*s*2,ci.width )
colnames(ci)<-c('Contrast','width of CI','expected CI')
out<-list(CI=ci, n=sample.size )
return(out)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/17_ss.towway.R |
#' @export
aligned.dot.plot2<-function(y,factor1,factor2=NULL){
factor1<-as.numeric(factor1)
#factor2<-match.arg(factor2)
if (!is.null(factor2)){
factor2<-as.numeric(factor2)
factor1<-as.numeric(factor1)
ufactor1<-unique(factor1)
ufactor2<-unique(factor2)
ci<-c()
cj<-c()
for(i in ufactor1){
for(j in ufactor2){
ci<-c(ci,ufactor1[i])
cj<-c(cj,ufactor2[j])
}
}
rn<-paste0(ci,' - ' ,cj)
yy=y[factor1==ufactor1[1] & factor2==ufactor2[1]]
plot(yy,rep(1,length(yy)) ,yaxt='n',col='white', xlab=c('') , ylim =c(0,length(table(factor1))*length(table(factor2))),xlim=range(y),ylab=c('treatment') )
axis(2,1:c(length(table(factor1))*length(table(factor2)) ) , labels = rn)
abline(h=1:c(length(table(factor1))*length(table(factor2))), lty = 2,col='gray75')
s=1
for(i in ufactor1){
for(j in ufactor2){
yy=y[factor1==ufactor1[i] & factor2==ufactor2[j]]
points(yy,rep(s,length(yy)))
s=s+1
}
}
}else{#######################################
factor1=as.numeric(factor1)
ufactor1<-unique(factor1)
ci<-c()
for(i in ufactor1){
ci<-c(ci,ufactor1[i])
}
rn<-paste0(ci)
yy=y[factor1==ufactor1[1] ]
plot(yy,rep(1,length(yy)) ,yaxt='n',col='white', xlab=c('') , ylim =c(0,length(table(factor1))+.5 ),xlim=range(y),ylab=c('treatment') )
axis(2,1:c(length(table(factor1)) ) , labels = rn)
abline(h=1:c(length(table(factor1))), lty = 2,col='gray75')
s=1
for(i in ufactor1){
yy=y[factor1==ufactor1[i] ]
points(yy,rep(s,length(yy)))
s=s+1
}
}
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/19_aligned.dot.plot.R |
#' @export
towway.ci<-function(y,x1,x2,mc=NULL,mp=NULL,mt=NULL,mse=NULL,alpha=0.05){
if (1==1 ){
a<-length(table(x1))
b<-length(table(x2))
ab<-c(a,b)
mean.a<-tapply(y,x1,mean)
mean.b<-tapply(y,x2,mean)
m<-list(mean.a,mean.b)
rv.a<-as.integer(names(mean.a))
rv.b<-as.integer(names(mean.b))
n<-aggregate(y,list(x1,x2),length)[,3]
m.n<-tapply(y,list(x1,x2),length)
m.mean<-tapply(y,list(x1,x2),mean)
ma<-apply(m.mean,1,mean)
mb<-apply(m.mean,2,mean)
out.t.c<-NULL
out.f.a<-NULL
out.f.b<-NULL
out.bt.a<-NULL
out.bt.b<-NULL
out.c.a<-NULL
out.s.a<-NULL
out.sb.a<-NULL
out.t<-NULL
out.t.s<-NULL
out.t.sb<-NULL
out.tpb<-NULL
out.tpt<-NULL
################## chekkkkkkkkk
if (sum(n==1)==length(y) ) {
fit <- lm(y ~ factor(x1)+factor(x2))######!!!!!
df.er=(a-1)*(b-1)
}else{
fit <- lm(y ~ factor(x1)*factor(x2))######!!!!!
df.er=length(y)-a*b
}
##################### !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# if ( is.null(mse) ){
mse<-(deviance(fit))/(fit$df.residual)
#}
ci.a<-c()
cj.a<-c()
for (i in 1:(a-1)){
ii<-i+1
for ( j in ii:a){
ci.a<-c(ci.a,rv.a[i])
cj.a<-c(cj.a,rv.a[j])
}
}
rn.a<-paste0(ci.a,'-' ,cj.a)
ci.b<-c()
cj.b<-c()
for (i in 1:(b-1)){
ii<-i+1
for ( j in ii:b){
ci.b<-c(ci.b,rv.b[i])
cj.b<-c(cj.b,rv.b[j])
}
}
rn.b<-paste0(ci.b,'-' ,cj.b)
###############
T.a<-(qtukey(1-alpha,a,df.er))/sqrt(2)
T.b<-(qtukey(1-alpha,b,df.er))/sqrt(2)
t.a<-qt(1-alpha/2,df.er)
t.b<-qt(1-alpha/2,df.er)
}
############################################ factor level
row.a<-as.integer(names(mean.a))
row.b<-as.integer(names(mean.b))
out.f.a<-matrix(1,1,3)
out.f.b<-matrix(1,1,3)
for ( i in 1:a){
s.a<-sqrt((mse*(apply(1/m.n,1,sum)[i]) )/(b^2))
out<-cbind(level=row.a[i],lower=ma[i]-t.a*s.a,upper=ma[i]+t.a*s.a)
out.f.a<-rbind(out.f.a,out)
}
out.f.a<- out.f.a[-1,]
for ( i in 1:b){
s.b<-sqrt((mse*(apply(1/m.n,2,sum)[i]) )/(a^2))
out<-cbind(level=row.b[i],lower=mb[i]-t.b*s.b,upper=mb[i]+t.b*s.b)
out.f.b<-rbind(out.f.b,out)
}
out.f.b<- out.f.b[-1,]
############################ tukey a
d.a<-ma[ci.a]-ma[cj.a]
s.a<-c()
for ( i in 1:length(ci.a)){
ss<-sum(apply(1/m.n,1,function(x) sum(x) )[c(ci.a[i],cj.a[i])])
s.a<-c(s.a,sqrt( (mse*ss)/(b^2) ) )
}
out.t.a<-cbind(d.a,lower=d.a-T.a*s.a,upper=d.a+T.a*s.a)
row.names(out.t.a)<-rn.a
d25=out.t.a
qwe<-dim(d25)[1]
plot(-10,-10,ylim=c(0,dim(d25)[1]+.5),yaxt='n',xlim=range(d25[,2:3]),xlab='',ylab='',main=paste("CI Tukey factor x1")) ####### -10 -10 !!!!!!
axis(2,1:qwe,labels=row.names(d25))
arrows(d25[,2],1:qwe,d25[,3],1:qwe,code=3,angle = 90,length = .1)
############################ tukey b
d.b<-mb[ci.b]-mb[cj.b]
s.b<-c()
for ( i in 1:length(ci.b)){
ss<-sum(apply(1/m.n,2,sum)[c(ci.b[i],cj.b[i])] )
s.b<-c(s.b,sqrt((mse*ss)/(a^2)) )
}
out.t.b<-cbind(d.b,lower=d.b-T.b*s.b,upper=d.b+T.b*s.b)
row.names(out.t.b)<-rn.b
d25=out.t.b
qwe<-dim(d25)[1]
plot(-10,-10,ylim=c(0,dim(d25)[1]+.5),yaxt='n',xlim=range(d25[,2:3]),xlab='',ylab='',main=paste("CI Tukey factor x2")) ####### -10 -10 !!!!!!
axis(2,1:qwe,labels=row.names(d25))
arrows(d25[,2],1:qwe,d25[,3],1:qwe,code=3,angle = 90,length = .1)
################################ BON tow a
g<-choose(a,2) ###!!!!!!!!!
t<-qt(1-alpha/(2*g),df.er)
out.bt.a<-cbind(d.a,lower=d.a-t*s.a,upper=d.a+t*s.a)
row.names(out.bt.a)<-rn.a
################################ BON tow b
g<-choose(b,2) ###!!!!!!!!!
t<-qt(1-alpha/(2*g),df.er)
out.bt.b<-cbind(d.b,lower=d.b-t*s.b,upper=d.b+t*s.b)
row.names(out.bt.b)<-rn.b
if (!is.null(mc)){#################### if is null mc
mm<-list(ma,mb)
z<-dim(mc)[1]
ss<-apply(mc!=0,1,any)
zz<-c(1:z)[-c(1:(z/3)*3)]
qq<-ss[zz]
e<-rep(1:2,length=length(qq))[qq]
mc2<-mc[ ss,]
z<-dim(mc2)[1]
if ( !sum(qq)==length(zz) ) {
############################## contrast
z<-dim(mc2)[1]
e<-rep(1:2,length=length(qq))[qq]
t<-qt(1-alpha/2,df.er)
g<-z/2
tb<-qt(1-alpha/(2*g),df.er)
out.c.a<-matrix(1,1,4)
out.s.a<-matrix(1,1,4)
out.sb.a<-matrix(1,1,4)
for( i in 1:c(z/2)){
l<-sum( (mm[[e[i]]][mc2[i*2-1,]])*(mc2[i*2,]) )
ss<-sum( (mc2[i*2,]^2)*(apply(1/m.n,e[i],sum)[mc2[i*2-1,] ] ) )
s.a<-sqrt( (mse/(rev(ab)[e[i]]^2))*ss )
s<-sqrt((ab[e[i]]-1)*qf(1-alpha,ab[e[i]]-1,df.er ) )
out<-cbind(num=i,L=l,lower=l-t*s.a,upper=l+t*s.a)
out.c.a<-rbind(out.c.a,out)
out<-cbind(num=i,L=l,lower=l-s*s.a,upper=l+s*s.a)
out.s.a<-rbind(out.s.a,out)
out<-cbind(num=i,L=l,lower=l-tb*s.a,upper=l+tb*s.a)
out.sb.a<-rbind(out.sb.a,out)
}
out.c.a<-out.c.a[-1,]
out.s.a<-out.s.a[-1,]
out.sb.a<-out.sb.a[-1,]
}else{
z<-dim(mc)[1]
out.t.s<-matrix(1,1,4)
out.t.c<-matrix(1,1,4)
out.t.sb<-matrix(1,1,4)
s<-sqrt( (a*b -1)*qf(1-alpha,a*b-1,df.er) )
g<-z/3
t<-qt(1-alpha/(2*g),df.er)
ttt<-qt(1-alpha/(2),df.er)
for ( i in 1:(z/3)){
l<-sum(apply(mc[c(i*3-2):c(i*3),],2,function(x) sum(m.mean[x[1],x[2]]*x[3]) ))
se2<-mse*sum(apply(mc[c(i*3-2):c(i*3),],2,function(x) sum((x[3]^2)/m.n[x[1],x[2]]) ))
se<-sqrt(se2)
out<-cbind(num=i,L=l,lower=l-s*se,upper=l+s*se)
out.t.s<-rbind(out.t.s,out)
out<-cbind(num=i,L=l,lower=l-ttt*se,upper=l+ttt*se)
out.t.c<-rbind(out.t.c,out)
out<-cbind(num=i,L=l,lower=l-t*se,upper=l+t*se)
out.t.sb<-rbind(out.t.sb,out)
}
out.t.s<-out.t.s[-1,]
out.t.sb<-out.t.sb[-1,]
out.t.c<-out.t.c[-1,]
} ### treat
} ### end mc
if( !is.null(mt)){#####################teratment BON
out.t<-matrix(1,ncol=5)
z<-dim(mt)[1]
for ( i in 1:z){
s<-sqrt(mse/(m.n[ mt[i,1], mt[i,2]]))
t<-qt(1-alpha/(2*z),df.er)
e<-m.mean[ mt[i,1], mt[i,2] ]
out<-cbind(i=mt[i,1],j=mt[i,2],estimate=e,lower= e-t*s , upper= e+t*s )
out.t<-rbind(out.t,out)
}
out.t<-out.t[-1,]
}
if( !is.null(mp)){#####################teratment
out.tpb<-matrix(1,ncol=3)
out.tpt<-matrix(1,ncol=3)
z<-dim(mp)[1]
Tu<-(qtukey(1-alpha,a*b,df.er))/sqrt(2)
tb<-qt(1-alpha/(2*z),df.er)
for ( i in 1:z){
s<-sqrt(mse*( 1/(m.n[ mp[i,1], mp[i,2] ])+1/(m.n[ mp[i,3], mp[i,4] ])))
e<- (m.mean[ mp[i,1], mp[i,2]]-m.mean[ mp[i,3], mp[i,4]] )
out<-cbind(estimate=e,lower= e-tb*s , upper= e+tb*s )
out.tpb<-rbind(out.tpb,out)
out<-cbind(estimate=e,lower= e-Tu*s , upper= e+Tu*s )
out.tpt<-rbind(out.tpt,out)
}
out.tpb<-out.tpb[-1,]
out.tpt<-out.tpt[-1,]
}
o<-list( factor.a=out.f.a,
factor.b=out.f.b,
bonferroni.simultaneous.pairwise.a=out.bt.a,
bonferroni.simultaneous.pairwise.b=out.bt.b,
tukey.simultaneous.pairwise.a=out.t.a,
tukey.simultaneous.pairwise.b=out.t.b,
NOT.simultaneous.Contrast=out.c.a,
Sheffe.simultaneous.Contrast=out.s.a,
bonferroni.simultaneous.Contrast=out.sb.a,
treatment.Bonferroni=out.t,
bonferroni.simultaneous.pairwise.treatment=out.tpb,
tukey.simultaneous.pairwise.treatment=out.tpt,
NOT.simultaneous.Contrast.treatment=out.t.c,
Sheffe.simultaneous.Contrast.treatment=out.t.s,
bonferroni.simultaneous.Contrast.treatment=out.t.sb)
return(o)
}########################################################### end function
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/19_towway.ci.R |
#' @export
MLS<-function(MSE1,df1,c1,MSE2,df2,c2,alpha=0.05){
f1=qf(1-alpha/2,df1,Inf)
f2=qf(1-alpha/2,df2,Inf)
f3=qf(1-alpha/2,Inf,df1)
f4=qf(1-alpha/2,Inf,df2)
f5=qf(1-alpha/2,df1,df2)
f6=qf(1-alpha/2,df2,df1)
g1<-1-1/f1
g2<-1-1/f2
g3<-( ((f5-1)^2)-((g1*f5)^2)-((f4-1)^2) )/f5
g4<-f6*( (((f6-1)/f6)^2) -1*(((f3-1)/f6)^2) -g2^2)
hl<-sqrt( ((g1*c1*MSE1)^2)+(((f4-1)*c2*MSE2)^2)-1*((g3*c1*c2*MSE1*MSE2)) )
hu<-sqrt( (((f3-1)*c1*MSE1 )^2)+((g2*c2*MSE2 )^2)-1*(( g4*c1*c2*MSE1*MSE2)) )
l=c1*MSE1+c2*MSE2
L=sum(l)
lower<-L-hl
upper<-L+hu
return(cbind(estimate=L,lower=lower,upper=upper))
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/25_MLS.R |
#' @export
#' @import car
#'
#'
onerandom<-function(y,treatment,alpha){
treatment<-factor(treatment)
aov=Anova(lm(y ~treatment ), type=2)
mse2<-aov[,1]/aov[,2]
mse<-mse2[2]
mstr<-mse2[1]
r<-aov[1,2]+1
n<-(aov[2,2]+r)/r
###########################################################
s<-sqrt(mstr/(r*n))
lower<-mean(y)-qt(1-alpha/2,r-1)*s
upper<-mean(y)+qt(1-alpha/2,r-1)*s
out.mu<-cbind(estimate=mean(y),lower=lower,upper=upper)
######################
l=((mstr/mse)*(1/qf(1-alpha/2,r-1,r*(n-1)))-1)/n
u=((mstr/mse)*(1/qf(alpha/2,r-1,r*(n-1)))-1)/n
lower<-l/(l+1)
upper<-u/(1+u)
out.prop.sigma2.mu<-cbind(lower=lower,upper=upper)
###############################################################
lower<-(r*(n-1)*mse)/(qchisq(1-alpha/2,r*(n-1)))
upper<-(r*(n-1)*mse)/(qchisq(alpha/2,r*(n-1)))
out.sigma2<-cbind(lower=lower,upper=upper)
##########################
out1<-satterthwaite(c=c(1/n,-1/n),MSE=c(mstr,mse),df=c(r-1,r*(n-1)),alpha=alpha)
out2<-MLS(MSE1=mstr,df1=r-1,c1=1/n,MSE2=mse,df2=r*(n-1),c2=-1/n,alpha=alpha)
o<-list(anova=aov,mu=out.mu,prop.sigma2.mu=out.prop.sigma2.mu, sigma2=out.sigma2,sigma2.mu.satterthwaite=out1, sigma2.mu.MLS= out2 )
return(o)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/25_onerandom.R |
#' @export
satterthwaite<-function(c,MSE,df,alpha=0.05){
l=c*MSE
L=sum(c*MSE)
dff<-((L^2)/sum((l^2)/df))
dff2<-round(dff)
if (dff2==0) dff2=1
lower<-(dff*L)/(qchisq(1-alpha/2,(dff2 )))
upper<-(dff*L)/(qchisq(alpha/2,(dff2)))
return(cbind(estimate=L,df=(dff),lower=lower,upper=upper))
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/25_satterthwaiteeeee.R |
#' @export
bftest<-function(fit,group,alpha=.05){
f<-fit$fitted
e<-fit$res
e1<-e[group==unique(group)[1]]
e2<-e[group==unique(group)[2]]
d1<-abs(e1-median(e1))
d2=abs(e2-median(e2))
n1<-length(e1)
n2<-length(e2)
n<-length(group)################# deghat, barasi, neveshtan stop
s=sqrt( ( (n1-1)*var(d1)+ (n2-1)*var(d2) )/(n-2))
t=(mean(d1)-mean(d2))/(s*sqrt((1/n1)+(1/n2)) )
out<-cbind(t.value=abs(t),P.Value=2*(1-pt(abs(t),n-2)),alpha=alpha,df=(n-2))###????
return(out)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/3_bftest.R |
#' @export
############ you can add mse log lik ---------
boxcox.sse<-function(x,y,l=seq(-2,2,.1)){
s<-c()
l<-l[l!=0]
k2=(prod(y))^(1/length(y))
for(j in 1:length(l)){
k1=1/( (l[j])*(k2^(l[j]-1)) )
w=k1*((y^l[j]) -1)
s<-c(s,deviance(lm(w~x)))
}
w=k2*log(y)
s0<-c(deviance(lm(w~x)))
s<-c(s,s0)
l<-c(l,0)
out<-data.frame(lambda=l,SSE=s)
out<-out[order(l),]
plot(out,ylab = 'SSE',xlab=expression(lambda),type='l')
return(out)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/3_boxcox.sse.R |
#' @export
#' @importFrom graphics lines par plot abline arrows axis barplot boxplot hist text title segments points
#' @importFrom stats anova deviance df.residual lm median predict pt qf qt var pf ptukey qnorm qqline qqnorm qtukey rstudent sd TukeyHSD aov aggregate cor qchisq
#' @importFrom leaps leaps
#' @importFrom car Anova
ci.reg<-function(model, newdata, type = c("b", "s","w","n","m","nm","gn"), alpha = 0.05,m=1){
type<-match.arg(type)
newdata<-as.data.frame(newdata)
if ( dim(newdata)[2] == length(names(model$coeff)) ){
colnames(newdata)<-names(model$coeff)
}else{
colnames(newdata)<-names(model$coeff)[-1]
}
CI<-predict(model, newdata, se.fit = T)
g<-nrow(newdata)
p<-ncol(newdata)+1
syh<-CI$se.fit
spred<-sqrt( CI$residual.scale^2 + (CI$se.fit)^2 ) # (2.38)
spredmean<-sqrt( (CI$residual.scale^2)/m + (CI$se.fit)^2 )
b<-qt(1 - alpha/(2*g), model$df) # B = (4.9a)
s<-sqrt(g * qf(1 - alpha, g, model$df)) # S = (4.8a)
w<-sqrt(p*qf(1 - alpha, p, model$df))
if (match.arg(type) == "b") {
s<-syh
z<-b
}else if(match.arg(type) == "s") {
s<-spred
z<-s
}else if(match.arg(type) == "w") {
s<-syh
z<-w
}else if(match.arg(type) == "n") {
s<-spred
z<-qt(1 - alpha/2, model$df)
}else if(match.arg(type) == "nm") {
s<-spredmean
z<-qt(1 - alpha/2, model$df)
}else if(match.arg(type) == "m") {
s<-syh
z<-qt(1 - alpha/2, model$df)
}else if(match.arg(type) == "gn") {
s<-spred
z<-b}
x<-data.frame(newdata,Fit=CI$fit,Lower.Band=CI$fit-z*s,Upper.Band=CI$fit+z*s)
return(x)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/6_ci.reg.R |
#' @export
AICp<- function(model){
n <- sum(anova(model)[,1])+1
p <- n-df.residual(model)
#print(n)
#print(p)
return(n*log(deviance(model))-n*log(n)+2*p)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_AICp.R |
#' @export
BestSub<-function(x,y,method=c('r2','r2adj','sse','cp','press','aic','sbc'),num=2){
method<-match.arg(method)
x<-as.matrix(x)
y<-as.matrix(y)
m<-model.s(x,y)
if (match.arg(method) == "r2") {
q<-dim(m)[2]-5
n<-cbind(1:dim(m)[1],m[,1],m[,q])[order(m[,q],decreasing = T),]
g<-unlist(tapply(n[,1],n[,2],function(x) x[1:num]))
p<-rep(2:max(m[,1]),each=num)[!is.na(g)]
g<-g[!is.na(g)]
return(cbind(p,m[g,][,-1]))
}else if(match.arg(method) == "r2adj") {
q<-dim(m)[2]-4
n<-cbind(1:dim(m)[1],m[,1],m[,q])[order(m[,q],decreasing = T),]
g<-unlist(tapply(n[,1],n[,2],function(x) x[1:num]))
p<-rep(2:max(m[,1]),each=num)[!is.na(g)]
g<-g[!is.na(g)]
return(cbind(p,m[g,][,-1]))
}else if(match.arg(method) == "sse") {
q<-dim(m)[2]-6
}else if(match.arg(method) == "cp") {
q<-dim(m)[2]-3
}else if(match.arg(method) == "aic") {
q<-dim(m)[2]-2
}else if(match.arg(method) == "sbc") {
q<-dim(m)[2]-1
}else if(match.arg(method) == "press") {
q<-dim(m)[2]-0
}
n<-cbind(1:dim(m)[1],m[,1],m[,q])[order(m[,q],decreasing = F),]
g<-unlist(tapply(n[,1],n[,2],function(x) x[1:num]))
p<-rep(2:max(m[,1]),each=num)[!is.na(g)]
g<-g[!is.na(g)]
return(cbind(p,m[g,][,-1]))
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_BestSub.R |
#' @export
SBCp<- function(model){
n <- sum(anova(model)[,1])+1
p <- n-df.residual(model)
#print(n)
#print(p)
return(n*log(deviance(model))-n*log(n)+(log(n))*p)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_SBCp.R |
#' @export
#'
cpc<- function(r,f){
n <- sum(anova(f)[,1])+1
p <- n-df.residual(r)
#print(n)
#print(p)
return((deviance(r)/(deviance(f)/df.residual(f)))-(n-2*p))
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_cpc.R |
#' @export
#' @import leaps
model.s<-function(x,y){
n<-ncol(x)
x<-as.matrix(x)
y<-as.vector(y)
xaic<-c()
xsbc<-c()
xpress<-c()
xsse<-c()
c<-leaps(x,y)$which
for (i in 1:nrow(c)){
model<-lm(y~ x[,(1:n)[c[i,]==T]] )
smodel<-summary(model)
xaic<-c(xaic,AICp(model))
xsbc<-c(xsbc,SBCp(model))
xpress<-c(xpress,pressc(model))
xsse<-c(xsse,deviance(model))
# xcp<-c(xcp, cpc(model) )
}
m1 <- leaps(x,y,method='Cp')
m2 <- leaps(x,y,method='r2')
m3 <- leaps(x,y,method='adjr2')
cbind(p=m1$size,m1$which,SSEp=xsse,r2=m2$r2,r2.adj=m3$adjr2,Cp=m1$Cp,AICp=xaic,SBCp=xsbc,PRESSp=xpress)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_model.s.R |
#' @export
######## DO NOT CALCULATE CP******************************************
modelval<-function(building.set,response.building, prediction.set, response.prediction){
building.set<-as.matrix(building.set)
response.building<-as.matrix(response.building)
prediction.set<-as.matrix(prediction.set)
response.prediction<-as.matrix(response.prediction)
model1<-lm(response.building~building.set)
model2<-lm(response.prediction~prediction.set)
smodel1<-summary(model1)$coeff[,1:2]
smodel2<-summary(model2)$coeff[,1:2]
#leaps(model.buildi11g.set,y,method = 'Cp')
#cp1<-cpc(model1)
#cp2<-cpc(model2)
p1<-pressc(model1)
p2<-pressc(model2)
a1<-AICp(model1)
a2<-AICp(model2)
s1<-SBCp(model1)
s2<-SBCp(model2)
r1<-summary(model1)$r.squared
r2<-summary(model2)$r.squared
r2a1<-summary(model1)$adj.r.squared
r2a2<-summary(model2)$adj.r.squared
Model.Training<-c(smodel1[,1],smodel1[,2],deviance(model1)/df.residual(model1),r1,r2a1,a1,s1,p1)
Model.Validation<-c(smodel2[,1],smodel2[,2],deviance(model2)/df.residual(model2),r2,r2a2,a2,s2,p2)
v<-cbind(Model.Training,Model.Validation)
namE<-paste('b',0:ncol(building.set),sep = "")
namSE<-paste('SE b',0:ncol(building.set),sep = "")
rownames(v)<-c(namE,namSE,'MSE','R2','R2.adj','AICp','SBCp','PRESSp')
return(v)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_modelval.R |
#' @export
plotmodel.s<-function(x,y){
n<-ncol(x)
x<-as.matrix(x)
y<-as.vector(y)
xaic<-c()
xsbc<-c()
xpress<-c()
c<-leaps(x,y)$which
for (i in 1:nrow(c)){
model<-lm(y~ x[,(1:n)[c[i,]==T]] )
#smodel<-summary(model)
xaic<-c(xaic,AICp(model))
xsbc<-c(xsbc,SBCp(model))
xpress<-c(xpress,pressc(model))
}
m1 <- leaps(x,y,method='Cp')
m2 <- leaps(x,y,method='r2')
m3 <- leaps(x,y,method='adjr2')
p<-m1$size
#cbind(p=m1$size,m1$which,r2=m2$r2,r2.adj=m3$adjr2,Cp=m1$Cp,AICp=xaic,SBCp=xsbc,PRESSp=xpress)
#par(mfrow=c(3,2))
par(mfrow=c(1,1))
e<-max(p)
plot(m2$r2 ~ p, xlab = "p", ylab = "R2",main='plot 1 of 6')
lines(2:e, tapply(m2$r2, p, max), lwd = 2,col=2)
plot(m3$adjr2 ~ p, xlab = "p", ylab = "R2adj",main='plot 2 of 6')
lines(2:e, tapply(m3$adjr2, p, max), lwd = 2,col=2)
plot(xaic ~ p, xlab = "p", ylab = "AIC",main='plot 3 of 6')
lines(2:e, tapply(xaic, p, min), lwd = 2,col=2)
plot(xsbc ~ p, xlab = "p", ylab = "SBC",main='plot 4 of 6')
lines(2:e, tapply(xsbc, p, min), lwd = 2,col=2)
plot(m1$Cp ~ p, xlab = "p", ylab = "Cp",main='plot 5 of 6')
lines(2:e, tapply(m1$Cp, p, min), lwd = 2,col=2)
plot(xpress ~ p, xlab = "p", ylab = "PRESS",main='plot 6 of 6')
lines(2:e, tapply(xpress, p, min), lwd = 2,col=2)
#par(mfrow=c(1,1))
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_plot.model.s.R |
#' @export
pressc<-function(fit){
x<-cbind( rep(1, nrow(as.matrix(fit$model[,-1])) ),fit$model[,-1] )
x<-as.matrix(x)
h<-x%*%solve(t(x)%*%x)%*%(t(x))
e<-fit$residuals
return(sum( (e/(1-diag(h)))^2 ) )
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/9_pressc.R |
#' @export
normal.cor.test<-function(residuals,MSE){
w<-1:length(residuals)
r<-cor(sort(residuals), sqrt(MSE)*(qnorm((w-0.375)/(length(residuals)+.25))) )
return(r)
}
| /scratch/gouwar.j/cran-all/cranData/ALSM/R/normal.cor.test.R |
#' Transform the array to the model matrix
#'
#' The internal function to make the model matrix corresponded to linear
#' predictor model from the array (vector) containg coordinates of stress factors.
#'
#' @keywords internal
#' @importFrom stats model.matrix
ExtendedForm <- function(array, formula, nf) {
terms <- attr(terms(formula), "term.labels")
mtx <- as.data.frame(matrix(array, ncol = nf))
colnames(mtx) <- terms[1:nf]
out <- model.matrix(formula, mtx)
}
#' Calculates the prediction variance at the particular use condition
#'
#' The internal function to calculate the prediction variance
#'
#' @keywords internal
PreVar <- function(location, formula, nf, infMtxInv) {
# Calculates the prediction variance in a particular use condition
use <- ExtendedForm(location, formula, nf)
as.numeric(use %*% infMtxInv %*% t(use))
}
#' Perform the k-means clustering and make design table
#'
#' The internal function to perform the k-means clustering and make design table.
#'
#' @keywords internal
#' @importFrom stats kmeans
kmeansCls <- function(Mtx, nCls) {
kmeansOut <- kmeans(Mtx, nCls)
Tbl <- cbind(kmeansOut$centers, kmeansOut$size)
colnames(Tbl)[ncol(Tbl)] <- paste("allocation")
Tbl
}
#' Objective function of D optimal design with right censoring
#'
#' The internal function to calculate the objective function value of
#' D optimal design with right censoring plan.
#'
#' @keywords internal
Dobj.rc <- function(x, formula, coef, nf, tc, alpha) {
X <- ExtendedForm(x, formula, nf)
b <- coef
eta <- X %*% b #linear predictor
phi <- 1 - exp(- exp(eta) * tc ^ alpha)
W <- diag(phi[, 1])
XWX <- t(X) %*% W %*% X
det(XWX)
}
#' Objective function of U optimal design with right censoring
#'
#' The internal function to calculate the objective function value of
#' U optimal design with right censoring plan.
#'
#' @keywords internal
#' @importFrom methods is
Uobj.rc <- function(x, formula, coef, nf, tc, alpha, useCond) {
X <- ExtendedForm(x, formula, nf)
b <- coef
eta <- X %*% b #linear predictor
phi <- 1 - exp(- exp(eta) * tc ^ alpha)
W <- diag(phi[, 1])
XWX <- t(X) %*% W %*% X
c <- try(qr.solve(XWX), silent = TRUE)
if (is(c, "try-error"))
return("cannot calculated ; information matrix is near singular")
else
PreVar(location = useCond, formula = formula, nf = nf, infMtxInv = c)
}
#' Objective function of I optimal design with right censoring
#'
#' The internal function to calculate the objective function value of
#' I optimal design with right censoring plan.
#'
#' @keywords internal
#' @importFrom methods is
Iobj.rc <- function(x, formula, coef, nf, tc, alpha, useLower, useUpper) {
X <- ExtendedForm(x, formula, nf)
b <- coef
eta <- X %*% b #linear predictor
phi <- 1 - exp(- exp(eta) * tc ^ alpha)
W <- diag(phi[, 1])
XWX <- t(X) %*% W %*% X
c <- try(qr.solve(XWX), silent = TRUE)
if (is(c, "try-error"))
return("cannot calculated ; information matrix is near singular")
else {
# numerical integration
intgratedPV <- cubature::adaptIntegrate(PreVar, lowerLimit = useLower,
upperLimit = useUpper, formula = formula,
nf = nf, infMtxInv = c)$integral
volume <- 1
for (i in 1:nf) volume <- volume * (useUpper[i] - useLower[i])
intgratedPV / volume
}
}
#' Design evaluation with right censoring.
#'
#' \code{\link{alteval.rc}} calculates the objective function value
#' (D, U or I) for a given design with right censoring plan.
#'
#' @param designTable a data frame containing the coordinates and the number of
#' allocation of each design point. The design created by either
#' \code{\link{altopt.rc}} or \code{\link{altopt.ic}} or any design matrix
#' with the same form as those can be provided for this argument.
#' @param optType the choice of \code{"D"}, \code{"U"} and \code{"I"} optimality.
#' @param tc the censoring time.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the numeric vector of use condition.
#' It should be provided when \code{optType} is \code{"U"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useLower the numeric vector of lower bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useUpper the numeric vector of upper bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @return The objective function value corresponded by \code{optType}
#' for a given design with right censoring plan.
#' @seealso \code{\link{altopt.rc}}
#' @examples
#' # Evaluation of factorial design for right censoring.
#' x1 <- c(0, 1, 0, 1)
#' x2 <- c(0, 0, 1, 1)
#' allocation <- c(25, 25, 25, 25)
#' facDes <- data.frame(x1, x2, allocation)
#'
#' alteval.rc(facDes, "D", 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' alteval.rc(facDes, "U", 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' alteval.rc(facDes, "I", 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' @export
alteval.rc <- function(designTable, optType, tc, nf, alpha, formula, coef,
useCond, useLower, useUpper) {
# Transform design to the single column array.
x <- NULL
for (col in (1:nf)) {
for (row in (1:nrow(designTable))) {
x <- c(x, rep(designTable[row, col], designTable[row, nf + 1]))
}
}
if (optType == "D")
value <- Dobj.rc(x, formula, coef, nf, tc, alpha)
else if (optType == "U")
value <- Uobj.rc(x, formula, coef, nf, tc, alpha, useCond)
else if (optType == "I")
value <- Iobj.rc(x, formula, coef, nf, tc, alpha, useLower, useUpper)
else stop('Wrong optimization criteria')
value
}
#' Optimal design with right censoring.
#'
#' \code{\link{altopt.rc}} creates D, U or I optimal design
#' of the accelerated life testing with right censoring plan.
#'
#' @param optType the choice of \code{"D"}, \code{"U"} and \code{"I"} optimality.
#' @param N the number of test units.
#' @param tc the censoring time.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the numeric vector of use condition.
#' It should be provided when \code{optType} is \code{"U"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useLower the numeric vector of lower bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useUpper the numeric vector of upper bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param nOpt the number of repetition of optimization process. Default is 1.
#' @param nKM the number of repetition of k-means clustering. Default is 20.
#' @param nCls the number of clusters used for k-means clustering. If not specified,
#' it is set as the number of parameters in the linear predictor model.
#' @return A list with components
#' \itemize{
#' \item{call:}{ the matched call.}
#' \item{opt.design.ori:}{ the original optimal design.}
#' \item{opt.value.ori:}{ the objective function value of \code{opt.design.ori}.}
#' \item{opt.design.rounded:}{ the optimal design clustered by rounding in third decimal points.}
#' \item{opt.value.rounded:}{ the objective function value of \code{opt.design.rounded}.}
#' \item{opt.design.kmeans:}{ the optimal design clustered by \code{\link[stats]{kmeans}}.}
#' \item{opt.value.kmeans:}{ the objective function value of \code{opt.design.kmeans}.}
#' }
#' @references
#' {
#' Monroe, E. M., Pan, R., Anderson-Cook, C. M., Montgomery, D. C. and
#' Borror C. M. (2011) A Generalized Linear Model Approach to Designing
#' Accelerated Life Test Experiments, \emph{Quality and Reliability Engineering
#' International} \bold{27(4)}, 595--607
#'
#' Yang, T., Pan, R. (2013) A Novel Approach to Optimal Accelerated Life Test
#' Planning With Interval Censoring, \emph{Reliability, IEEE Transactions on}
#' \bold{62(2)}, 527--536
#' }
#' @seealso \code{\link[stats]{kmeans}}, \code{\link{alteval.rc}}
#' @examples
#' \dontrun{
#' # Generating D optimal design for right censoring.
#' altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' # Generating U optimal design for right censoring.
#' altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' # Generating I optimal design for right censoring.
#' altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859),
#' useUpper = c(2.058, 3.459))
#' }
#' @importFrom stats aggregate
#' @importFrom stats optim
#' @importFrom stats runif
#' @importFrom methods is
#' @export
altopt.rc <- function(optType, N, tc, nf, alpha, formula, coef,
useCond, useLower, useUpper,
nOpt = 1, nKM = 30, nCls = NULL) {
# function for optimization
Opt.rc <- function() {
xInit <- runif(nf * N, min = 0, max = 1)
lb <- rep(0, nf * N)
ub <- rep(1, nf * N)
if (optType == "D")
solution <- optim(xInit, Dobj.rc, NULL, formula, coef, nf, tc, alpha,
method = "L-BFGS-B", lower = lb, upper = ub,
control = list(fnscale = -1) # Maximization
)
else if (optType == "U")
solution <- optim(xInit, Uobj.rc, NULL, formula, coef, nf, tc, alpha,
useCond,
method = "L-BFGS-B", lower = lb, upper = ub)
else if (optType == "I")
solution <- optim(xInit, Iobj.rc, NULL, formula, coef, nf, tc, alpha,
useLower, useUpper,
method = "L-BFGS-B", lower = lb, upper = ub)
else stop('Wrong optimization criteria')
solution
}
# Repeat optimization with different initial points
for (i in 1:nOpt) {
Curr_sol <- Opt.rc()
if (i == 1) Best_sol <- Curr_sol
else if (optType == "D" && Curr_sol$value > Best_sol$value)
Best_sol <- Curr_sol
else if ((optType %in% c("U", "I")) && Curr_sol$value < Best_sol$value)
Best_sol <- Curr_sol
}
terms <- attr(terms(formula), "term.labels")
optDesignMtx <- as.data.frame(matrix(Best_sol$par, ncol = nf))
colnames(optDesignMtx) <- terms[1:nf]
# Original result
columnlist <- apply(optDesignMtx, 2, list)
columnlist <- lapply(columnlist, unlist)
optDesignOri <- aggregate(optDesignMtx, columnlist, length)
while (length(optDesignOri) != (nf + 1))
optDesignOri <- optDesignOri[, -length(optDesignOri)]
names(optDesignOri)[ncol(optDesignOri)] <- paste("allocation")
optValueOri <- alteval.rc(optDesignOri, optType, tc, nf, alpha,
formula, coef, useCond, useLower, useUpper)
# Rounding result
optDesignMtxRound <- round(optDesignMtx, digits = 3)
columnlist <- apply(optDesignMtxRound, 2, list)
columnlist <- lapply(columnlist, unlist)
optDesignRound <- aggregate(optDesignMtxRound, columnlist, length)
while (length(optDesignRound) != (nf + 1))
optDesignRound <- optDesignRound[, -length(optDesignRound)]
names(optDesignRound)[ncol(optDesignRound)] <- paste("allocation")
optValueRound <- alteval.rc(optDesignRound, optType, tc, nf, alpha,
formula, coef, useCond, useLower, useUpper)
# k-means result
if (is.null(nCls)) nCls <- length(coef)
for (j in 1:nKM) {
curDesignKmeans <- kmeansCls(optDesignMtx, nCls)
curValueKmeans <- alteval.rc(curDesignKmeans, optType, tc, nf, alpha,
formula, coef, useCond, useLower, useUpper)
if (j == 1) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
} else if (optType == "D" && curValueKmeans > optValueKmeans) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
} else if ((optType %in% c("U", "I")) && curValueKmeans < optValueKmeans) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
}
}
# Creates output
out <- list(call = match.call(),
opt.design.ori = optDesignOri,
opt.value.ori = as.numeric(optValueOri),
opt.design.rounded = optDesignRound,
opt.value.rounded = as.numeric(optValueRound),
opt.design.kmeans = optDesignKmeans,
opt.value.kmeans = ifelse (is(optValueKmeans, "character"),
optValueKmeans, as.numeric(optValueKmeans))
)
out
}
#' Objective function of D optimal design with interval censoring
#'
#' The internal function to calculate the objective function value of
#' D optimal design with interval censoring plan.
#'
#' @keywords internal
Dobj.ic <- function(x, formula, coef, nf, t, k, alpha) {
X <- ExtendedForm(x, formula, nf)
b <- coef
dt <- t / k
eta <- X %*% b # linear predictor
temp1 <- exp(2 * eta)
temp2 <- diag(temp1[, 1])
sum <- 0
for(j in 1:k) {
c <- ((j - 1) ^ alpha - j ^ alpha) * dt ^ alpha
temp3 <- c ^ 2 * exp(- exp(eta) * (dt ^ alpha) * (j ^ alpha))
temp4 <- temp3 / (1 - exp(c * exp(eta)))
sum = sum + temp4
}
W <- sum[, 1] * temp2 # W plays a role of weight matrix
XWX <- t(X) %*% W %*% X
det(XWX)
}
#' Objective function of U optimal design with interval censoring
#'
#' The internal function to calculate the objective function value of
#' U optimal design with interval censoring plan.
#'
#' @keywords internal
#' @importFrom methods is
Uobj.ic <- function(x, formula, coef, nf, t, k, alpha, useCond) {
X <- ExtendedForm(x, formula, nf)
b <- coef
dt <- t / k
eta <- X %*% b # linear predictor
temp1 <- exp(2 * eta)
temp2 <- diag(temp1[, 1])
sum <- 0
for(j in 1:k) {
c <- ((j - 1) ^ alpha - j ^ alpha) * dt ^ alpha
temp3 <- c ^ 2 * exp(- exp(eta) * (dt ^ alpha) * (j ^ alpha))
temp4 <- temp3 / (1 - exp(c * exp(eta)))
sum = sum + temp4
}
W <- sum[, 1] * temp2 # W plays a role of weight matrix
XWX <- t(X) %*% W %*% X
c <- try(qr.solve(XWX), silent = TRUE)
if (is(c, "try-error"))
return("cannot calculated ; information matrix is near singular")
else
PreVar(location = useCond, formula = formula, nf = nf, infMtxInv = c)
}
#' Objective function of U optimal design with interval censoring
#'
#' The internal function to calculate the objective function value of
#' U optimal design with interval censoring plan.
#'
#' @keywords internal
#' @importFrom methods is
Iobj.ic <- function(x, formula, coef, nf, t, k, alpha, useLower, useUpper) {
X <- ExtendedForm(x, formula, nf)
b <- coef
dt <- t / k
eta <- X %*% b # linear predictor
temp1 <- exp(2 * eta)
temp2 <- diag(temp1[, 1])
sum <- 0
for(j in 1:k) {
c <- ((j - 1) ^ alpha - j ^ alpha) * dt ^ alpha
temp3 <- c ^ 2 * exp(- exp(eta) * (dt ^ alpha) * (j ^ alpha))
temp4 <- temp3 / (1 - exp(c * exp(eta)))
sum = sum + temp4
}
W <- sum[, 1] * temp2 # W plays a role of weight matrix
XWX <- t(X) %*% W %*% X
c <- try(qr.solve(XWX), silent = TRUE)
if (is(c, "try-error"))
return("cannot calculated ; information matrix is near singular")
else {
# numerical integration
intgratedPV <- cubature::adaptIntegrate(PreVar, lowerLimit = useLower,
upperLimit = useUpper, formula = formula,
nf = nf, infMtxInv = c)$integral
volume <- 1
for (i in 1:nf) volume <- volume * (useUpper[i] - useLower[i])
intgratedPV / volume
}
}
#' Design evaluation with interval censoring.
#'
#' \code{\link{alteval.ic}} calculates the objective function value
#' (D, U or I) for a given design with interval censoring plan.
#'
#' @param designTable a data frame containing the coordinates and the number of
#' allocation of each design point. The design created by either
#' \code{\link{altopt.rc}} or \code{\link{altopt.ic}} or any design matrix
#' with the same form as those can be provided for this argument.
#' @param optType the choice of \code{"D"}, \code{"U"} and \code{"I"} optimality.
#' @param t the total testing time.
#' @param k the number of time intervals.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the numeric vector of use condition.
#' It should be provided when \code{optType} is \code{"U"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useLower the numeric vector of lower bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useUpper the numeric vector of upper bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @return The objective function value corresponded by \code{optType}
#' for a given design with interval censoring plan.
#' @seealso \code{\link{altopt.ic}}
#' @examples
#' # Evaluation of factorial design for interval censoring.
#' x1 <- c(0, 1, 0, 1)
#' x2 <- c(0, 0, 1, 1)
#' allocation <- c(25, 25, 25, 25)
#' facDes <- data.frame(x1, x2, allocation)
#'
#' alteval.ic(facDes, "D", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' alteval.ic(facDes, "U", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' alteval.ic(facDes, "I", 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' @export
alteval.ic <- function(designTable, optType, t, k, nf, alpha, formula, coef,
useCond, useLower, useUpper) {
# Transform design to the single column array.
x <- NULL
for (col in (1:nf)) {
for (row in (1:nrow(designTable))) {
x <- c(x, rep(designTable[row, col], designTable[row, nf + 1]))
}
}
if (optType == "D")
value <- Dobj.ic(x, formula, coef, nf, t, k, alpha)
else if (optType == "U")
value <- Uobj.ic(x, formula, coef, nf, t, k, alpha, useCond)
else if (optType == "I")
value <- Iobj.ic(x, formula, coef, nf, t, k, alpha, useLower, useUpper)
else stop('Wrong optimization criteria')
value
}
#' Optimal design with interval censoring.
#'
#' \code{\link{altopt.ic}} creates D, U or I optimal design
#' of the accelerated life testing with interval censoring plan.
#'
#' @param optType the choice of \code{"D"}, \code{"U"} and \code{"I"} optimality.
#' @param N the number of test units.
#' @param t the total testing time.
#' @param k the number of time intervals.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the numeric vector of use condition.
#' It should be provided when \code{optType} is \code{"U"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useLower the numeric vector of lower bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param useUpper the numeric vector of upper bound of use region.
#' It should be provided when \code{optType} is \code{"I"}. The length of the vector
#' should be same as the number of stress factors.
#' @param nOpt the number of repetition of optimization process. Default is 1.
#' @param nKM the number of repetition of k-means clustering. Default is 20.
#' @param nCls the number of clusters used for k-means clustering. If not specified,
#' it is set as the number of parameters in the linear predictor model.
#' @return A list with components
#' \itemize{
#' \item{call:}{ the matched call.}
#' \item{opt.design.ori:}{ the original optimal design.}
#' \item{opt.value.ori:}{ the objective function value of \code{opt.design.ori}.}
#' \item{opt.design.rounded:}{ the optimal design clustered by rounding in third decimal points.}
#' \item{opt.value.rounded:}{ the objective function value of \code{opt.design.rounded}.}
#' \item{opt.design.kmeans:}{ the optimal design clustered by \code{\link[stats]{kmeans}}.}
#' \item{opt.value.kmeans:}{ the objective function value of \code{opt.design.kmeans}.}
#' }
#' @references
#' {
#' Monroe, E. M., Pan, R., Anderson-Cook, C. M., Montgomery, D. C. and
#' Borror C. M. (2011) A Generalized Linear Model Approach to Designing
#' Accelerated Life Test Experiments, \emph{Quality and Reliability Engineering
#' International} \bold{27(4)}, 595--607
#'
#' Yang, T., Pan, R. (2013) A Novel Approach to Optimal Accelerated Life Test
#' Planning With Interval Censoring, \emph{Reliability, IEEE Transactions on}
#' \bold{62(2)}, 527--536
#' }
#' @seealso \code{\link[stats]{kmeans}}, \code{\link{alteval.ic}}
#' @examples
#' \dontrun{
#' # Generating D optimal design for interval censoring.
#' altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' # Generating U optimal design for interval censoring.
#' altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' # Generating I optimal design for interval censoring.
#' altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859),
#' useUpper = c(2.058, 3.459))
#' }
#' @importFrom stats aggregate
#' @importFrom stats optim
#' @importFrom stats runif
#' @importFrom methods is
#' @export
altopt.ic <- function(optType, N, t, k, nf, alpha, formula, coef,
useCond, useLower, useUpper,
nOpt = 1, nKM = 30, nCls = NULL) {
# function for optimization
Opt.ic <- function() {
xInit <- runif(nf * N, min = 0, max = 1)
lb <- rep(0, nf * N)
ub <- rep(1, nf * N)
if (optType == "D")
solution <- optim(xInit, Dobj.ic, NULL, formula, coef, nf, t, k, alpha,
method = "L-BFGS-B", lower = lb, upper = ub,
control = list(fnscale = -1) # Maximization
)
else if (optType == "U")
solution <- optim(xInit, Uobj.ic, NULL, formula, coef, nf, t, k, alpha,
useCond,
method = "L-BFGS-B", lower = lb, upper = ub)
else if (optType == "I")
solution <- optim(xInit, Iobj.ic, NULL, formula, coef, nf, t, k, alpha,
useLower, useUpper,
method = "L-BFGS-B", lower = lb, upper = ub)
else stop('Wrong optimization criteria')
solution
}
# Repeat optimization with different initial points
for (i in 1:nOpt) {
Curr_sol <- Opt.ic()
if (i == 1) Best_sol <- Curr_sol
else if (optType == "D" && Curr_sol$value > Best_sol$value)
Best_sol <- Curr_sol
else if ((optType %in% c("U", "I")) && Curr_sol$value < Best_sol$value)
Best_sol <- Curr_sol
}
terms <- attr(terms(formula), "term.labels")
optDesignMtx <- as.data.frame(matrix(Best_sol$par, ncol = nf))
colnames(optDesignMtx) <- terms[1:nf]
# Original result
columnlist <- apply(optDesignMtx, 2, list)
columnlist <- lapply(columnlist, unlist)
optDesignOri <- aggregate(optDesignMtx, columnlist, length)
while (length(optDesignOri) != (nf + 1))
optDesignOri <- optDesignOri[, -length(optDesignOri)]
names(optDesignOri)[ncol(optDesignOri)] <- paste("allocation")
optValueOri <- alteval.ic(optDesignOri, optType, t, k, nf, alpha,
formula, coef, useCond, useLower, useUpper)
# Rounding result
optDesignMtxRound <- round(optDesignMtx, digits = 3)
columnlist <- apply(optDesignMtxRound, 2, list)
columnlist <- lapply(columnlist, unlist)
optDesignRound <- aggregate(optDesignMtxRound, columnlist, length)
while (length(optDesignRound) != (nf + 1))
optDesignRound <- optDesignRound[, -length(optDesignRound)]
names(optDesignRound)[ncol(optDesignRound)] <- paste("allocation")
optValueRound <- alteval.ic(optDesignRound, optType, t, k, nf, alpha,
formula, coef, useCond, useLower, useUpper)
# k-means result
if (is.null(nCls)) nCls <- length(coef)
for (j in 1:nKM) {
curDesignKmeans <- kmeansCls(optDesignMtx, nCls)
curValueKmeans <- alteval.ic(curDesignKmeans, optType, t, k, nf, alpha,
formula, coef, useCond, useLower, useUpper)
if (j == 1) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
} else if (optType == "D" && curValueKmeans > optValueKmeans) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
} else if ((optType %in% c("U", "I")) && curValueKmeans < optValueKmeans) {
optDesignKmeans <- curDesignKmeans
optValueKmeans <- curValueKmeans
}
}
# Creates output
out <- list(call = match.call(),
opt.design.ori = optDesignOri,
opt.value.ori = as.numeric(optValueOri),
opt.design.rounded = optDesignRound,
opt.value.rounded = as.numeric(optValueRound),
opt.design.kmeans = optDesignKmeans,
opt.value.kmeans = ifelse (is(optValueKmeans, "character"),
optValueKmeans, as.numeric(optValueKmeans))
)
out
}
#' Design plot.
#'
#' \code{\link{design.plot}} draws design plot as a form of a bubble plot
#' of any two stress factors which are specified by \code{xAxis} and \code{yAxis}.
#' The size of each bubble indicates the relative magnitude of allocation on
#' each design point.
#'
#' @param design the data frame containing the coordinates and the number of
#' allocation of each design point. The design created by either
#' \code{\link{altopt.rc}} or \code{\link{altopt.ic}} or any design matrix
#' with the same form as those can be provided for this argument.
#' @param xAxis the name of the factor to be displayed in x axis.
#' @param yAxis the name of the factor to be displayed in y axis.
#' @return The bubble plot of a design with two stress factors.
#' @examples
#' \dontrun{
#' # Design plot of D optimal design with right censoring.
#' Design1 <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' design.plot(Design1$opt.design.rounded, x1, x2)
#' }
#' @importFrom graphics plot
#' @importFrom graphics rect
#' @importFrom graphics symbols
#' @importFrom graphics text
#' @importFrom stats aggregate
#' @export
design.plot <- function (design, xAxis, yAxis) {
designName <- deparse(substitute(design))
xAxisName <- deparse(substitute(xAxis))
yAxisName <- deparse(substitute(yAxis))
plot(1, type = "n", main = designName, xlab = xAxisName, ylab = yAxisName,
xaxp = c(0, 1, 5), yaxp = c(0, 1, 5),
xlim = c(-0.2, 1.2), ylim = c(-0.2, 1.2), frame = FALSE)
rect(0, 0, 1, 1)
agg.des <- aggregate(design, by = list(design[, colnames(design) == xAxisName],
design[, colnames(design) == yAxisName]),
FUN = sum)
symbols(agg.des$Group.1, agg.des$Group.2, circles = agg.des$allocation / 300,
inches = FALSE, add = TRUE, fg = "blue", bg = "white", lwd = 1.5)
text(agg.des$Group.1, agg.des$Group.2, agg.des$allocation, cex = .75)
}
#' Contour plot of prediction variance for a design with right censoring.
#'
#' \code{\link{pv.contour.rc}} draws the contour plot of prediction variance
#' for a given design with right censoring plan. Either \code{useCond} or
#' use region (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param design the data frame containing the coordinates and the number of
#' allocation of each design point. The design created by either
#' \code{\link{altopt.rc}} or \code{\link{altopt.ic}} or any design matrix
#' with the same form as those can be provided for this argument.
#' @param xAxis the name of the factor to be displayed in x axis.
#' @param yAxis the name of the factor to be displayed in y axis.
#' @param tc the censoring time.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the vector of specified use condition. If it is provided,
#' the contour line will be generated up to this point.
#' @param useLower,useUpper the vector of the use region. If these are
#' provided, the contour line will be generated up to this region.
#' Note that either \code{useCond} or both of \code{useLower, useUpper}
#' should be provided.
#' @return The contour plot of prediction variance for right censoring.
#' @seealso \code{\link{altopt.rc}}
#' @examples
#' \dontrun{
#' # Contour plot of prediction variance of U optimal design with right censoring.
#' Design <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' pv.contour.rc(Design$opt.design.rounded, x1, x2, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#' }
#' @importFrom graphics contour
#' @importFrom graphics points
#' @importFrom graphics rect
#' @importFrom graphics segments
#' @importFrom graphics symbols
#' @importFrom graphics text
#' @importFrom graphics title
#' @importFrom stats aggregate
#' @export
pv.contour.rc <- function (design, xAxis, yAxis, tc, nf, alpha, formula, coef,
useCond = NULL, useLower = NULL, useUpper = NULL) {
designName <- deparse(substitute(design))
xAxisName <- deparse(substitute(xAxis))
yAxisName <- deparse(substitute(yAxis))
xColNum <- which(colnames(design) == xAxisName)
yColNum <- which(colnames(design) == yAxisName)
pv <- function (useCond, ux, uy) {
useCond[xColNum] <- ux # change use condition of two factors given by user
useCond[yColNum] <- uy # with maintaining values of the other factors
as.numeric(alteval.rc(design, optType = "U", tc, nf,
alpha, formula, coef, useCond))
}
if (!is.null(useCond)) {
range <- ceiling(max(useCond[xColNum], useCond[yColNum]))
pv.use <- round(pv(useCond, useCond[xColNum], useCond[yColNum]), digits = 2)
}
else if (!is.null(useLower) && !is.null(useUpper)) {
range <- ceiling(max(useUpper[xColNum], useUpper[yColNum]))
pv.use <- round(pv(useUpper, useUpper[xColNum], useUpper[yColNum]), digits = 2)
}
else stop('Use condition missing, either useCond or
both of useLower and useUpper should be provided.')
x <- seq(0, range, 0.1)
y <- seq(0, range, 0.1)
pv.grid <- matrix(nrow = length(x), ncol = length(y))
for (r in 1:length(x)) {
for (c in 1:length(y)) {
if (!is.null(useCond))
pv.grid[r, c] <- pv(useCond, x[r], y[c])
else if (!is.null(useLower) && !is.null(useUpper))
pv.grid[r, c] <- pv((useLower + useUpper) / 2, x[r], y[c])
}
}
mylevels <- seq(0, pv.use, 0.1)
contour(x, y, pv.grid, levels = mylevels, method = "edge", pty = "s")
title(main = paste("PV contour of ", designName, sep = ""),
xlab = xAxisName, ylab = yAxisName, cex.main = .75)
agg.des <- aggregate(design, by = list(design[, colnames(design) == xAxisName],
design[, colnames(design) == yAxisName]),
FUN = sum)
symbols(agg.des$Group.1, agg.des$Group.2, circles = agg.des$allocation / 300,
inches = FALSE, add = TRUE, bg = "gray")
text(agg.des$Group.1, agg.des$Group.2, agg.des$allocation, cex = .75,
adj = c(-.5, 1))
segments(0, 0, range, 0)
segments(0, 0, 0, range)
segments(range, 0, range, range)
segments(0, range, range, range)
segments(0, 1, 1, 1)
segments(1, 0, 1, 1)
if (!is.null(useCond)) {
points(useCond[xColNum], useCond[yColNum], pch=22, bg="white")
text(useCond[xColNum], useCond[yColNum], paste("PV =", pv.use),
cex = .75, adj = c(-.2, -.2))
segments(0, useCond[yColNum], useCond[xColNum], useCond[yColNum])
segments(useCond[xColNum], 0, useCond[xColNum], useCond[yColNum])
}
else if (!is.null(useLower) && !is.null(useUpper)) {
rect(useLower[xColNum], useLower[yColNum],
useUpper[xColNum], useUpper[yColNum], col="white")
text((useLower[xColNum] + useUpper[xColNum]) / 2,
(useLower[yColNum] + useUpper[yColNum]) / 2,
"Use Region", cex = .5, adj = c(0.5, 0.5))
}
}
#' Contour plot of prediction variance for a design with interval censoring.
#'
#' \code{\link{pv.contour.ic}} draws the contour plot of prediction variance
#' for a given design with interval censoring plan. Either \code{useCond} or
#' use region (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param design the data frame containing the coordinates and the number of
#' allocation of each design point. The design created by either
#' \code{\link{altopt.rc}} or \code{\link{altopt.ic}} or any design matrix
#' with the same form as those can be provided for this argument.
#' @param xAxis the name of the factor to be displayed in x axis.
#' @param yAxis the name of the factor to be displayed in y axis.
#' @param t the total testing time.
#' @param k the number of time intervals.
#' @param nf the number of stress factors.
#' @param alpha the value of the shape parameter of Weibull distribution.
#' @param formula the object of class formula which is the linear predictor model.
#' @param coef the numeric vector containing the coefficients of each term in \code{formula}.
#' @param useCond the vector of specified use condition. If it is provided,
#' the contour line will be generated up to this point.
#' @param useLower,useUpper the vector of the use region. If these are
#' provided, the contour line will be generated up to this region.
#' Note that either \code{useCond} or both of \code{useLower, useUpper}
#' should be provided.
#' @return The contour plot of prediction variance for interval censoring.
#' @seealso \code{\link{altopt.ic}}
#' @examples
#' \dontrun{
#' # Contour plot of prediction variance of U optimal design with interval censoring.
#' Design <- altopt.ic("D", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' pv.contour.ic(Design$opt.design.rounded, x1, x2, 30, 5, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#' }
#' @importFrom graphics contour
#' @importFrom graphics points
#' @importFrom graphics rect
#' @importFrom graphics segments
#' @importFrom graphics symbols
#' @importFrom graphics text
#' @importFrom graphics title
#' @importFrom stats aggregate
#' @export
pv.contour.ic <- function (design, xAxis, yAxis, t, k, nf, alpha, formula, coef,
useCond = NULL, useLower = NULL, useUpper = NULL) {
designName <- deparse(substitute(design))
xAxisName <- deparse(substitute(xAxis))
yAxisName <- deparse(substitute(yAxis))
xColNum <- which(colnames(design) == xAxisName)
yColNum <- which(colnames(design) == yAxisName)
pv <- function (useCond, ux, uy) {
useCond[xColNum] <- ux # change use condition of two factors given by user
useCond[yColNum] <- uy # with maintaining values of the other factors
as.numeric(alteval.ic(design, optType = "U", t, k, nf,
alpha, formula, coef, useCond))
}
if (!is.null(useCond)) {
range <- ceiling(max(useCond[xColNum], useCond[yColNum]))
pv.use <- round(pv(useCond, useCond[xColNum], useCond[yColNum]), digits = 2)
}
else if (!is.null(useLower) && !is.null(useUpper)) {
range <- ceiling(max(useUpper[xColNum], useUpper[yColNum]))
pv.use <- round(pv(useUpper, useUpper[xColNum], useUpper[yColNum]), digits = 2)
}
else stop('Use condition missing, either useCond or
both of useLower and useUpper should be provided.')
x <- seq(0, range, 0.1)
y <- seq(0, range, 0.1)
pv.grid <- matrix(nrow = length(x), ncol = length(y))
for (r in 1:length(x)) {
for (c in 1:length(y)) {
if (!is.null(useCond))
pv.grid[r, c] <- pv(useCond, x[r], y[c])
else if (!is.null(useLower) && !is.null(useUpper))
pv.grid[r, c] <- pv((useLower + useUpper) / 2, x[r], y[c])
}
}
mylevels <- seq(0, pv.use, 0.1)
contour(x, y, pv.grid, levels = mylevels, method = "edge", pty="s")
title(main = paste("PV contour of ", designName, sep = ""),
xlab = xAxisName, ylab = yAxisName, cex.main = .75)
agg.des <- aggregate(design, by = list(design[, colnames(design) == xAxisName],
design[, colnames(design) == yAxisName]),
FUN = sum)
symbols(agg.des$Group.1, agg.des$Group.2, circles = agg.des$allocation / 300,
inches = FALSE, add = TRUE, bg = "gray")
text(agg.des$Group.1, agg.des$Group.2, agg.des$allocation, cex = .75,
adj = c(-.5, 1))
segments(0, 0, range, 0)
segments(0, 0, 0, range)
segments(range, 0, range, range)
segments(0, range, range, range)
segments(0, 1, 1, 1)
segments(1, 0, 1, 1)
if (!is.null(useCond)) {
points(useCond[xColNum], useCond[yColNum], pch=22, bg="white")
text(useCond[xColNum], useCond[yColNum], paste("PV =", pv.use),
cex = .75, adj = c(-.2, -.2))
segments(0, useCond[yColNum], useCond[xColNum], useCond[yColNum])
segments(useCond[xColNum], 0, useCond[xColNum], useCond[yColNum])
}
else if (!is.null(useLower) && !is.null(useUpper)) {
rect(useLower[xColNum], useLower[yColNum],
useUpper[xColNum], useUpper[yColNum], col="white")
text((useLower[xColNum] + useUpper[xColNum]) / 2,
(useLower[yColNum] + useUpper[yColNum]) / 2,
"Use Region", cex = .5, adj = c(0.5, 0.5))
}
}
#' FUS (Fraction of Use Space) plot for right censoring.
#'
#' \code{\link{pv.fus.rc}} draws the FUS plot of prediction variance
#' for a given design with right censoring plan. The use region
#' (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param useLower,useUpper the vectors containing the lower bound and upper
#' bound for the use region. They should be provided for FUS plot.
#' @return The "trellis" object which includes the FUS plot
#' for right censoring.
#' @inheritParams design.plot
#' @inheritParams altopt.rc
#' @seealso \code{\link{altopt.rc}}
#' @examples
#' \dontrun{
#' # FUS plot of I optimal design with right censoring.
#' Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' pv.fus.rc(Design$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' }
#' @export
pv.fus.rc <- function (design, tc, nf, alpha, formula, coef,
useLower = NULL, useUpper = NULL) {
if (is.null(useLower) || is.null(useUpper)) stop('Use condition missing')
d <- round(5000 ^ (1 / nf))
pv.grid <- matrix(nrow = d ^ nf, ncol = nf + 1)
for (p in 1:nf) {
range <- seq(useLower[p], useUpper[p], length.out = d)
pv.grid[, p] <- rep(rep(range, each = d ^ (nf - p)), d ^ (p - 1))
}
for (r in 1:nrow(pv.grid)) {
pv.grid[r, ncol(pv.grid)] <- as.numeric(alteval.rc(design, optType = "U",
tc, nf, alpha, formula, coef, useCond = pv.grid[r, 1:nf]))
}
fus <- cbind(sort(pv.grid[, ncol(pv.grid)]), c(1:nrow(pv.grid)) / nrow(pv.grid))
plot <- lattice::xyplot(fus[, 1] ~ fus[, 2],
aspect = 1 / 2,
main = paste("FUS of ", deparse(substitute(design)), sep = ""),
xlab = paste("Fraction of Use Space"),
ylab = paste("Prediction Variance"),
type = "a",
grid = TRUE,
scales = list(x = list(tick.number = 11)))
plot
}
#' FUS (Fraction of Use Space) plot for interval censoring.
#'
#' \code{\link{pv.fus.ic}} draws the FUS plot of prediction variance
#' for a given design with interval censoring plan. The use region
#' (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param useLower,useUpper the vectors containing the lower bound and upper
#' bound for the use region. They should be provided for FUS plot.
#' @return The "trellis" object which includes the FUS plot
#' for interval censoring.
#' @inheritParams design.plot
#' @inheritParams altopt.ic
#' @seealso \code{\link{altopt.ic}}
#' @examples
#' \dontrun{
#' # FUS plot of I optimal design with interval censoring.
#' Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' pv.fus.ic(Design$opt.design.rounded, 30, 5, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' }
#' @export
pv.fus.ic <- function (design, t, k, nf, alpha, formula, coef,
useLower = NULL, useUpper = NULL) {
if (is.null(useLower) || is.null(useUpper)) stop('Use condition missing')
d <- round(5000 ^ (1 / nf))
pv.grid <- matrix(nrow = d ^ nf, ncol = nf + 1)
for (p in 1:nf) {
range <- seq(useLower[p], useUpper[p], length.out = d)
pv.grid[, p] <- rep(rep(range, each = d ^ (nf - p)), d ^ (p - 1))
}
for (r in 1:nrow(pv.grid)) {
pv.grid[r, ncol(pv.grid)] <- as.numeric(alteval.ic(design, optType="U",
t, k, nf, alpha, formula, coef, useCond = pv.grid[r, 1:nf]))
}
fus <- cbind(sort(pv.grid[, ncol(pv.grid)]), c(1:nrow(pv.grid)) / nrow(pv.grid))
plot <- lattice::xyplot(fus[, 1] ~ fus[, 2],
aspect = 1 / 2,
main = paste("FUS of ", deparse(substitute(design)), sep = ""),
xlab = paste("Fraction of Use Space"),
ylab = paste("Prediction Variance"),
type = "a",
grid = TRUE,
scales = list(x = list(tick.number = 11)))
plot
}
#' Comparing designs using FUS
#'
#' \code{\link{compare.fus}} draws the FUS plots of multiple designs on a
#' single frame.
#'
#' @param ... Objects created by \code{\link{pv.fus.rc}} or
#' \code{\link{pv.fus.ic}}.
#' @return FUS plots of multiple designs.
#' @seealso \code{\link{pv.fus.rc}}, \code{\link{pv.fus.ic}}
#' @examples
#' \dontrun{
#' # Generating D optimal design and FUS plot.
#' Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' FUS.D <- pv.fus.rc(Dopt$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' # Generating U optimal design and FUS plot.
#' Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' FUS.U <- pv.fus.rc(Uopt$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' # Comparing D and U optimal designs.
#' compare.fus(FUS.D, FUS.U)
#' }
#' @export
compare.fus <- function (...) {
# Keep the name of arguments as string
nm <- unlist(strsplit(deparse(substitute(list(...))), split = ","))
nm <- unlist(strsplit(nm, split = " "))
nm <- unlist(strsplit(nm, split = "list(", fixed = TRUE))
nm <- unlist(strsplit(nm, split = ")", fixed = TRUE))
fus.obj <- list(...)
data <- data.frame()
for (i in 1:length(fus.obj)) {
data <- rbind(data, data.frame(y = fus.obj[[i]]$panel.args[[1]]$y,
x = fus.obj[[i]]$panel.args[[1]]$x,
z = nm[i]))
}
plot <- lattice::xyplot(y ~ x, data, groups = data$z, auto.key = list(corner = c(0, 1)),
aspect = 1 / 2,
main = paste("FUS of ", deparse(substitute(list(...))), sep = ""),
xlab = paste("Fraction of Use Space"),
ylab = paste("Prediction Variance"),
type = "a",
grid = TRUE,
scales = list(x = list(tick.number = 11)))
plot
}
#' VDUS (Variance Dispersion of Use Space) plot for right censoring.
#'
#' \code{\link{pv.vdus.rc}} draws the VDUS plot of prediction variance
#' for a given design with right censoring plan. The use region
#' (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param useLower,useUpper the vectors containing the lower bound and upper
#' bound for the use region. They should be provided for VDUS plot.
#' @return The "trellis" object which includes the VDUS plot
#' for right censoring.
#' @inheritParams design.plot
#' @inheritParams altopt.rc
#' @seealso \code{\link{altopt.rc}}
#' @examples
#' \dontrun{
#' # VDUS plot of I optimal design with right censoring.
#' Design <- altopt.rc("I", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' pv.vdus.rc(Design$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' }
#' @importFrom stats aggregate
#' @export
pv.vdus.rc <- function (design, tc, nf, alpha, formula, coef,
useLower = NULL, useUpper = NULL) {
if (is.null(useLower) || is.null(useUpper)) stop('Use condition missing')
d <- round(5000 ^ (1 / nf))
if (d %% 2 == 0) d <- d + 1
pv.grid <- matrix(nrow = d ^ nf, ncol = nf)
for (p in 1:nf) {
range <- seq(useLower[p], useUpper[p], length.out = d)
pv.grid[, p] <- rep(rep(range, each = d ^ (nf - p)), d ^ (p - 1))
}
center <- (useLower + useUpper) / 2
dx <- (useUpper - center) / ((d - 1) / 2)
rank <- numeric(length = nrow(pv.grid))
for (r in 1:nrow(pv.grid)) {
if (all(pv.grid[r, ] == center)) rank[r] <- 0
else {
for (n in 1:(d - 1) / 2) {
for (p in 1:nf) {
if ((round(pv.grid[r, p], 3) == round(center[p] + n * dx[p], 3)
|| round(pv.grid[r, p], 3) == round(center[p] - n * dx[p], 3))
&& round(pv.grid[r, -p], 3) >= round(center[-p] - n * dx[-p], 3)
&& round(pv.grid[r, -p], 3) <= round(center[-p] + n * dx[-p], 3))
rank[r] <- n
}
}
}
}
pv.grid <- cbind(pv.grid, rank)
pv <- numeric(length = nrow(pv.grid))
for (r in 1:nrow(pv.grid)) {
pv[r] <- as.numeric(alteval.rc(design, optType = "U", tc, nf, alpha,
formula, coef, useCond = pv.grid[r, 1:nf]))
}
pv.grid <- cbind(pv.grid, pv)
min <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = min)
avg <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = mean)
max <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = max)
vdus <- rbind(cbind(min, gr = "min"), cbind(avg, gr = "avg"),
cbind(max, gr = "max"))
plot <- lattice::xyplot(vdus$pv ~ vdus$rank,
aspect = 1 / 2,
main = paste("VDUS of ", deparse(substitute(design)), sep = ""),
xlab = paste("Relative radius from origin"),
ylab = paste("Prediction Variance"),
type = "smooth",
grid = TRUE,
group = vdus$gr,
auto.key = list(corner = c(0, 1)))
plot
}
#' VDUS (Variance Dispersion of Use Space) plot for interval censoring.
#'
#' \code{\link{pv.vdus.ic}} draws the VDUS plot of prediction variance
#' for a given design with interval censoring plan. The use region
#' (\code{useLower} and \code{useUpper}) should be
#' provided.
#'
#' @param useLower,useUpper the vectors containing the lower bound and upper
#' bound for the use region. They should be provided for VDUS plot.
#' @return The "trellis" object which includes the VDUS plot
#' for interval censoring.
#' @inheritParams design.plot
#' @inheritParams altopt.ic
#' @seealso \code{\link{altopt.ic}}
#' @examples
#' \dontrun{
#' # VDUS plot of I optimal design with interval censoring.
#' Design <- altopt.ic("I", 100, 30, 5, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' pv.vdus.ic(Design$opt.design.rounded, 30, 5, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#' }
#' @importFrom stats aggregate
#' @export
pv.vdus.ic <- function (design, t, k, nf, alpha, formula, coef,
useLower = NULL, useUpper = NULL) {
if (is.null(useLower) || is.null(useUpper)) stop('Use condition missing')
d <- round(5000 ^ (1 / nf))
if (d %% 2 == 0) d <- d + 1
pv.grid <- matrix(nrow = d ^ nf, ncol = nf)
for (p in 1:nf) {
range <- seq(useLower[p], useUpper[p], length.out = d)
pv.grid[, p] <- rep(rep(range, each = d ^ (nf - p)), d ^ (p - 1))
}
center <- (useLower + useUpper) / 2
dx <- (useUpper - center)/((d - 1) / 2)
rank <- numeric(length = nrow(pv.grid))
for (r in 1:nrow(pv.grid)) {
if (all(pv.grid[r, ] == center)) rank[r] <- 0
else {
for (n in 1:(d - 1) / 2) {
for (p in 1:nf) {
if ((round(pv.grid[r, p], 3) == round(center[p] + n * dx[p], 3)
|| round(pv.grid[r, p], 3) == round(center[p] - n * dx[p], 3))
&& round(pv.grid[r, -p], 3) >= round(center[-p] - n * dx[-p], 3)
&& round(pv.grid[r, -p], 3) <= round(center[-p] + n * dx[-p], 3))
rank[r] <- n
}
}
}
}
pv.grid <- cbind(pv.grid, rank)
pv <- numeric(length = nrow(pv.grid))
for (r in 1:nrow(pv.grid)) {
pv[r] <- as.numeric(alteval.ic(design, optType = "U", t, k, nf, alpha,
formula, coef, useCond = pv.grid[r, 1:nf]))
}
pv.grid <- cbind(pv.grid, pv)
min <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = min)
avg <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = mean)
max <- aggregate(pv.grid, by = list(pv.grid[, "rank"]), FUN = max)
vdus <- rbind(cbind(min, gr = "min"), cbind(avg, gr = "avg"),
cbind(max, gr = "max"))
plot <- lattice::xyplot(vdus$pv ~ vdus$rank,
aspect = 1 / 2,
main = paste("VDUS of ", deparse(substitute(design)), sep = ""),
xlab = paste("Relative radius from origin"),
ylab = paste("Prediction Variance"),
type = "smooth",
grid = TRUE,
group = vdus$gr,
auto.key = list(corner = c(0, 1)))
plot
}
#' Comparing designs using VDUS
#'
#' \code{\link{compare.vdus}} draws the VDUS plots of multiple designs on a
#' single frame.
#'
#' @param ... Objects created by \code{\link{pv.vdus.rc}} or
#' \code{\link{pv.vdus.ic}}.
#' @return VDUS plots of multiple designs.
#' @seealso \code{\link{pv.vdus.rc}}, \code{\link{pv.vdus.ic}}
#' @examples
#' \dontrun{
#' # Generating D optimal design and VDUS plot.
#' Dopt <- altopt.rc("D", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01))
#'
#' VDUS.D <- pv.vdus.rc(Dopt$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' # Generating U optimal design and VDUS plot.
#' Uopt <- altopt.rc("U", 100, 100, 2, 1, formula = ~ x1 + x2 + x1:x2,
#' coef = c(0, -4.086, -1.476, 0.01), useCond = c(1.758, 3.159))
#'
#' VDUS.U <- pv.vdus.rc(Uopt$opt.design.rounded, 100, 2, 1,
#' formula = ~ x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01),
#' useLower = c(1.458, 2.859), useUpper = c(2.058, 3.459))
#'
#' # Comparing D and U optimal designs.
#' compare.vdus(VDUS.D, VDUS.U)
#' }
#' @export
compare.vdus <- function (...) {
nm <- unlist(strsplit(deparse(substitute(list(...))), split = ","))
nm <- unlist(strsplit(nm, split = " "))
nm <- unlist(strsplit(nm, split = "list(", fixed = TRUE))
nm <- unlist(strsplit(nm, split = ")", fixed = TRUE))
vdus.obj <- list(...)
data <- data.frame()
for (i in 1:length(vdus.obj)) {
data <- rbind(data, data.frame(y = vdus.obj[[i]]$panel.args[[1]]$y,
x = vdus.obj[[i]]$panel.args[[1]]$x,
z = paste(nm[i], vdus.obj[[i]]$panel.args.common$groups),
sep = "."))
}
plot <- lattice::xyplot(y ~ x, data, groups = data$z, auto.key = list(corner = c(0, 1)),
aspect = 1 / 2,
main = paste("VDUS of ", deparse(substitute(list(...))), sep = ""),
xlab = paste("Variance Dispersion of Use Space"),
ylab = paste("Prediction Variance"),
type = "a",
grid = TRUE,
scales = list(x = list(tick.number = 11)))
plot
}
#' Coding and decoding stress level
#'
#' Convert the stress levels from the actual levels to standardized levels,
#' and vice versa.
#'
#' @param lowStLv a numeric vector containing the actual lowest stress level
#' of each stress variable in design region.
#' @param highStLv a numeric vector containing the actual highest stress level
#' of each stress variable in design region.
#' @param actual a data frame or numeric vector containing the design points
#' in actual units.
#' @param stand a data frame or numeric vector containing the design points
#' in standardized units.
#' @return When \code{actual} is provided, the function converts it to the
#' standardized units and when \code{stand} is provided, the function converts
#' it to the actual units.
#' @examples
#' \dontrun{
#' # Generating D optimal design in coded unit.
#' Design <- altopt.rc(optType = "D", N = 100, tc = 100, nf = 2, alpha = 1,
#' formula = ~x1 + x2 + x1:x2, coef = c(0, -4.086, -1.476, 0.01))
#'
#' # Transform the coded unit to actual stress variable's level.
#' convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5),
#' stand = Design$opt.design.rounded)
#'
#' # Transform the actual stress level to coded units.
#' use <- c(38.281, 3.219)
#' convert.stress.level(lowStLv = c(34.834, 4.094), highStLv = c(30.288, 4.5),
#' actual = use)
#' }
#' @importFrom methods is
#' @export
convert.stress.level <- function(lowStLv, highStLv,
actual = NULL, stand = NULL) {
nf <- length(lowStLv)
if (is.null(actual) && is.null(stand))
stop ('Either actual or Stand should be provided.')
else if (!is.null(actual) && !is.null(stand))
stop ('Only one of actual or Stand should be provided')
else if (!is.null(stand)) {
# Convert from stand to actual
if (is(stand, "numeric"))
stand <- as.data.frame(matrix(stand, ncol = nf))
out <- stand
for (c in 1:nf) {
for (r in 1:nrow(stand))
out[r, c] <- stand[r, c] * (lowStLv[c] - highStLv[c]) + highStLv[c]
}
}
else if (!is.null(actual)) {
# Convert from actual to stand
if (is(actual, "numeric"))
actual <- as.data.frame(matrix(actual, ncol = nf))
out <- actual
for (c in 1:nf) {
for (r in 1:nrow(actual))
out[r, c] <- (actual[r, c] - highStLv[c]) / (lowStLv[c] - highStLv[c])
}
}
out
}
| /scratch/gouwar.j/cran-all/cranData/ALTopt/R/ALTopt.R |
#' Alfalfa soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Alfalfa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name ALFALFASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/ALFALFASoil.R |
#' Alfalfa temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Alfalfa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name ALFALFATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/ALFALFATemp.R |
#' Alfalfa terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Alfalfa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name ALFALFATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/ALFALFATerrain.R |
#' Alfalfa water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Alfalfa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CropLen - Length of growing period (days)
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WghAv - Relative humidity growing cycle (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name ALFALFAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/ALFALFAWater.R |
#' Agricultural Land Use Evaluation System
#'
#' Agricultural Land Use Evaluation System (ALUES) is
#' a package that evaluates land suitability for
#' different crop production. The package is based on
#' the Food and Agriculture Organization (FAO) and the
#' International Rice Research Institute (IRRI) methodology
#' for land evaluation. Development of ALUES is inspired by
#' similar tool for land evaluation, Land Use Suitability
#' Evaluation Tool (LUSET). The package uses fuzzy logic
#' approach to evaluate land suitability of a particular
#' area based on inputs such as rainfall,
#' temperature, topography, and soil properties. The
#' membership functions used for fuzzy modeling are the
#' following: Triangular, Trapezoidal, Gaussian, Sigmoidal
#' and custom models with functions that can be
#' defined by the user. The package also aims on complicated
#' methods like considering more than one fuzzy membership
#' function on different suitability class. The methods for
#' computing the overall suitability of a particular area are
#' also included, and these are the Minimum, Maximum, Product,
#' Sum, Average, Exponential and Gamma. Finally, ALUES utilizes
#' the power of Rcpp library for efficient computation.
#'
#' @author Al-Ahmadgaid B. Asaad <alahmadgaid@@gmail.com> (maintainer)
#' @author Arnold R. Salvacion <arsalvacion@@gmail.com>
#' @author Bui Tan Yen
#'
#' @import Rcpp
#' @docType package
#' @useDynLib ALUES
#' @name ALUES-package
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/ALUES.R |
#' Avocado soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Avocado.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 10 rows and 8 columns
#' @name AVOCADOSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/AVOCADOSoil.R |
#' Avocado temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Avocado.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TmMinXm - Avarage minimum temperature of coldest month ( C )
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name AVOCADOTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/AVOCADOTemp.R |
#' Avocado terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Avocado.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name AVOCADOTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/AVOCADOTerrain.R |
#' Avocado water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Avocado.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name AVOCADOWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/AVOCADOWater.R |
#' Bamboo soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Bamboo.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item SoilDpt - Soil depth (cm)
#' \item OC - Organic carbon (\%)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' \item pHH2O - pH H2O
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 4 rows and 8 columns
#' @name BAMBOOSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BAMBOOSoil.R |
#' Bamboo temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Bamboo.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyAv - Mean annual temperature (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name BAMBOOTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BAMBOOTemp.R |
#' Bamboo terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Bamboo.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name BAMBOOTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BAMBOOTerrain.R |
#' Bamboo water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Bamboo.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name BAMBOOWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BAMBOOWater.R |
#' Banana soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Banana.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name BANANASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BANANASoil.R |
#' Banana temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Banana.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TmMinXm - Avarage minimum temperature of coldest month ( C )
#' \item TmMinXmAb - Absolute min temp. coldest month (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name BANANATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BANANATemp.R |
#' Banana terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Banana.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name BANANATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BANANATerrain.R |
#' Banana water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Banana.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name BANANAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BANANAWater.R |
#' Barley soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Barley.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC6 - Organic carbon (\%) - Kaolinitic materials
#' \item OC7 - Organic carbon (\%) - Non Kaolinitic, Non calcareous materials
#' \item OC8 - Organic carbon (\%) - Calcareous materials
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 13 rows and 8 columns
#' @name BARLEYSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BARLEYSoil.R |
#' Barley temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Barley.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TmAv2 - Mean temp. crop development stage (2nd month) (°C)
#' \item TmAv3 - Mean temp. of the flowering stage (°C)
#' \item TmAv4 - Mean temp. of the ripening stage (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name BARLEYTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BARLEYTemp.R |
#' Barley terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Barley.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name BARLEYTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BARLEYTerrain.R |
#' Barley water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Barley.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmAv2 - Mean precipitation of second month (mm)
#' \item WmAv3 - Mean precipitation of third month (mm)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name BARLEYWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BARLEYWater.R |
#' Castor Beans soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Castor Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 9 rows and 8 columns
#' @name BEANCASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANCASoil.R |
#' Castor Beans temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Castor Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TmAv1 - Mean temp. of the initial stage( C )
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name BEANCATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANCATemp.R |
#' Castor Beans terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Castor Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name BEANCATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANCATerrain.R |
#' Castor Beans water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Castor Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmhAv3 - Relative humidity of maturation Stage (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name BEANCAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANCAWater.R |
#' Beans soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name BEANSSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANSSoil.R |
#' Beans temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TgMinAv - Mean min. temp. of growing cycle (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name BEANSTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANSTemp.R |
#' Beans terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 7 rows and 8 columns
#' @name BEANSTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANSTerrain.R |
#' Beans water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Beans.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmhAv2 - Relative humidity of devel. Stage (\%)
#' \item WmhAv3 - Relative humidity of maturation Stage (\%)
#' \item WmnN2 - n/N develop. Stage (2nd month)
#' \item WmnN4 - n/N maturation stage (4th month)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name BEANSWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/BEANSWater.R |
#' Cabbage soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cabbage.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name CABBAGESoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CABBAGESoil.R |
#' Cabbage temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cabbage.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TmAv0 - Mean temp. at germination (°C) (1st month)
#' \item TdDiff - Temp diffrence day/night ( C )
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name CABBAGETemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CABBAGETemp.R |
#' Cabbage terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cabbage.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 7 rows and 8 columns
#' @name CABBAGETerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CABBAGETerrain.R |
#' Cabbage water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cabbage.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WghAv - Relative humidity growing cycle (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CABBAGEWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CABBAGEWater.R |
#' Carrots soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Carrots.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 11 rows and 8 columns
#' @name CARROTSSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CARROTSSoil.R |
#' Carrots temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Carrots.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TmAv0 - Mean temp. at germination (°C) (1st month)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CARROTSTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CARROTSTemp.R |
#' Carrots terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Carrots.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name CARROTSTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CARROTSTerrain.R |
#' Carrots water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Carrots.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WghAv - Relative humidity growing cycle (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CARROTSWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CARROTSWater.R |
#' Cashew soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cashew.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 8 rows and 8 columns
#' @name CASHEWSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASHEWSoil.R |
#' Cashew temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cashew.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TmMinXm - Avarage minimum temperature of coldest month ( C )
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CASHEWTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASHEWTemp.R |
#' Cashew terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cashew.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item SlopeD - Slope (degree, 6 classes)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 8 rows and 8 columns
#' @name CASHEWTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASHEWTerrain.R |
#' Cashew water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cashew.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CASHEWWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASHEWWater.R |
#' Cassava soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cassava.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm1 - Coarse fragment in surface (Vol.\%)
#' \item CFragm2 - Coarse fragment in depth (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name CASSAVASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASSAVASoil.R |
#' Cassava temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cassava.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TmMinXmAb - Absolute min temp. coldest month (°C)
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name CASSAVATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASSAVATemp.R |
#' Cassava terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cassava.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name CASSAVATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASSAVATerrain.R |
#' Cassava water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cassava.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WmnN5 - n/N of the 5 dryest months
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name CASSAVAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CASSAVAWater.R |
#' Chickpea soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Chickpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 11 rows and 8 columns
#' @name CHICKPEASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CHICKPEASoil.R |
#' Chickpea temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Chickpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name CHICKPEATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CHICKPEATemp.R |
#' Chickpea terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Chickpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item SlopeD - Slope (degree, 6 classes)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 8 rows and 8 columns
#' @name CHICKPEATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CHICKPEATerrain.R |
#' Chickpea water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Chickpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmAv3 - Mean precipitation of third month (mm)
#' \item WmAv4 - Mean precipitation of fourth month (mm)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name CHICKPEAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CHICKPEAWater.R |
#' Cinnamon soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cinnamon.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 9 rows and 8 columns
#' @name CINNAMONSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CINNAMONSoil.R |
#' Cinnamon temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cinnamon.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name CINNAMONTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CINNAMONTemp.R |
#' Cinnamon terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cinnamon.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item SlopeD - Slope (degree, 6 classes)
#' \item Flood - Flooding
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CINNAMONTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CINNAMONTerrain.R |
#' Cinnamon water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cinnamon.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WghAv - Relative humidity growing cycle (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 2 rows and 8 columns
#' @name CINNAMONWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CINNAMONWater.R |
#' Citrus soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Citrus.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item ESP - ESP (\%)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 12 rows and 8 columns
#' @name CITRUSSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CITRUSSoil.R |
#' Citrus temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Citrus.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyAv - Mean annual temperature (°C)
#' \item TmMax38 - No of months with mean temp. > 38 °C
#' \item TmMin13 - No of months with mean temp. < 13 °C
#' \item TyMinAb - Absolute minimum temperature (°C)
#' \item TyMinAb - Absolute minimum temperature (°C)
#' \item TyMinAb - Absolute minimum temperature (°C)
#' \item TyMinAb - Absolute minimum temperature (°C)
#' \item TmAv3 - Mean temp. of the flowering stage (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 8 rows and 8 columns
#' @name CITRUSTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CITRUSTemp.R |
#' Citrus terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Citrus.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name CITRUSTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CITRUSTerrain.R |
#' Citrus water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Citrus.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WmhColdXm - Relative humidity of coldest month if frost (\%)
#' \item WmhAv4 - Relative humidity at harvest stage (\%)
#' \item WmhAv3 - Relative humidity of maturation Stage (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name CITRUSWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/CITRUSWater.R |
#' Cocoa soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cocoa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 10 rows and 8 columns
#' @name COCOASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCOASoil.R |
#' Cocoa temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cocoa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyAv - Mean annual temperature (°C)
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name COCOATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCOATemp.R |
#' Cocoa terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cocoa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name COCOATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCOATerrain.R |
#' Cocoa water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cocoa.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WmhDryXm - Mean rel. humidity dryest month (\%)
#' \item WmhDryXm - Mean rel. humidity dryest month (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 4 rows and 8 columns
#' @name COCOAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCOAWater.R |
#' Coconut soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Coconut.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item OC - Organic carbon (\%)
#' \item ECemh - ECe (mmhos/cm)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name COCONUTSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCONUTSoil.R |
#' Coconut temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Coconut.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyAv - Mean annual temperature (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 1 rows and 8 columns
#' @name COCONUTTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCONUTTemp.R |
#' Coconut terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Coconut.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name COCONUTTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCONUTTerrain.R |
#' Coconut water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Coconut.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WyhAv - Mean annual rel. humidity (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name COCONUTWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COCONUTWater.R |
#' Arabica Coffee soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Arabica Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 11 rows and 8 columns
#' @name COFFEEARSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEARSoil.R |
#' Arabica Coffee temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Arabica Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TdMinXm - Mean daily minimum temperature of coldest month (°C)
#' \item TyAv - Mean annual temperature (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name COFFEEARTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEARTemp.R |
#' Arabica Coffee terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Arabica Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name COFFEEARTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEARTerrain.R |
#' Arabica Coffee water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Arabica Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WmhDryXm - Mean rel. humidity dryest month (\%)
#' \item WmnN5 - n/N of the 5 dryest months
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 4 rows and 8 columns
#' @name COFFEEARWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEARWater.R |
#' Robusta Coffee soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Robusta Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 10 rows and 8 columns
#' @name COFFEEROSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEROSoil.R |
#' Robusta Coffee temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Robusta Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TyAv - Mean annual temperature (°C)
#' \item TyMaxAv - Mean annual maximum temperature (°C)
#' \item TdMinXm - Mean daily minimum temperature of coldest month (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name COFFEEROTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEROTemp.R |
#' Robusta Coffee terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Robusta Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage - Drainage
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 5 rows and 8 columns
#' @name COFFEEROTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEROTerrain.R |
#' Robusta Coffee water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Robusta Coffee.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WyAv - Annual precipitation (mm)
#' \item WmDryLen - Length dry season (months : P < 1/2 PET)
#' \item WmhDryXm - Mean rel. humidity dryest month (\%)
#' \item WmnN5 - n/N of the 5 dryest months
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 4 rows and 8 columns
#' @name COFFEEROWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COFFEEROWater.R |
#' Cotton soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cotton.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC6 - Organic carbon (\%) - Kaolinitic materials
#' \item OC7 - Organic carbon (\%) - Non Kaolinitic, Non calcareous materials
#' \item OC8 - Organic carbon (\%) - Calcareous materials
#' \item ECemh - ECe (mmhos/cm)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 13 rows and 8 columns
#' @name COTTONSoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COTTONSoil.R |
#' Cotton temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cotton.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TgMaxAv - Mean max temp. of growing cycle (°C)
#' \item TmMaxXm - Average max. temp. warmest month (°C)
#' \item TmAv2 - Mean temp. crop development stage (2nd month) (°C)
#' \item TdAvg3 - Mean DAY temp. of flowering stage (°C)
#' \item TdMinN3 - Mean Night temp. of flowering stage (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name COTTONTemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COTTONTemp.R |
#' Cotton terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cotton.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item Slope - nan
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 7 rows and 8 columns
#' @name COTTONTerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COTTONTerrain.R |
#' Cotton water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cotton.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmAv1 - Mean precipitation of first month (mm)
#' \item WmAv2 - Mean precipitation of second month (mm)
#' \item WmAv5 - Mean precipitation of fifth month (mm)
#' \item WmAv6 - Precipitation of ripening stage (mm)(6th month)
#' \item WmhAv3 - Relative humidity of maturation Stage (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name COTTONWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COTTONWater.R |
#' Cowpea soil requirement for land evaluation
#'
#' A dataset containing the soil characteristics of the crop requirements for farming Cowpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item CFragm - Coarse fragment (Vol.\%)
#' \item SoilDpt - Soil depth (cm)
#' \item CaCO3 - CaCO3 (\%)
#' \item Gyps - Gypsum (\%)
#' \item CECc - Apparent CEC Clay (cmol (+)/kg clay)
#' \item BS - Base Saturation (\%)
#' \item SumBCs - Sum of basic caions (cmol (+)/kg soil)
#' \item pHH2O - pH H2O
#' \item OC - Organic carbon (\%)
#' \item ECedS - ECe (dS/m)
#' \item SoilTe - 12 classes of soil texture (Soil Taxonomy)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 11 rows and 8 columns
#' @name COWPEASoil
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COWPEASoil.R |
#' Cowpea temp requirement for land evaluation
#'
#' A dataset containing the temp characteristics of the crop requirements for farming Cowpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item TgAv - Mean temperature of the growing cycle (°C)
#' \item TmAv0 - Mean temp. at germination (°C) (1st month)
#' \item TyMinAv - Mean annual minimum temperature (°C)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 3 rows and 8 columns
#' @name COWPEATemp
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COWPEATemp.R |
#' Cowpea terrain requirement for land evaluation
#'
#' A dataset containing the terrain characteristics of the crop requirements for farming Cowpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item Slope1 - Slope (\%) (1. Irrigrated agriculture, basin furrow irrigation)
#' \item Slope2 - Slope (\%) (2. High level of managemnet with full mechanization. )
#' \item Slope3 - Slope (\%) (3. Low level of managemnet animal traction or handwork.)
#' \item Flood - Flooding
#' \item Drainage4 - Drainage (Medium and fine textured soils)
#' \item Drainage5 - Drainage (Coarse textured soils - Sandy families)
#' \item SlopeD - Slope (degree, 6 classes)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 7 rows and 8 columns
#' @name COWPEATerrain
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COWPEATerrain.R |
#' Cowpea water requirement for land evaluation
#'
#' A dataset containing the water characteristics of the crop requirements for farming Cowpea.
#'
#' @details
#' The following are the factors for evaluation:
#'
#' \itemize{
#' \item WgAv - Precipitation of growing cycle (mm)
#' \item WmAv1 - Mean precipitation of first month (mm)
#' \item WmAv2 - Mean precipitation of second month (mm)
#' \item WmAv3 - Mean precipitation of third month (mm)
#' \item WmAv4 - Mean precipitation of fourth month (mm)
#' \item WmhAv4 - Relative humidity at harvest stage (\%)
#' }
#' @seealso
#' \itemize{
#' \item Yen, B. T., Pheng, K. S., and Hoanh, C. T. (2006). \emph{LUSET: Land Use Suitability Evaluation Tool User's Guide}. International Rice Research Institute.
#' }
#'
#' @docType data
#' @keywords dataset
#' @format A data frame with 6 rows and 8 columns
#' @name COWPEAWater
NULL | /scratch/gouwar.j/cran-all/cranData/ALUES/R/COWPEAWater.R |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.