title: How to get the full graph plotted by R?
body: <p>I've tried many times but still can't get the full image plotted by R, instead of a truncated one duo to "insufficient space"?</p>
  
  <p>Please someone help me how to solve this problem.</p>
  
  <p>below are my screenshot and R codes:</p>
  
  <p><a href="https://i.stack.imgur.com/5s7PU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5s7PU.png" alt="enter image description here"></a></p>
  
  <pre><code>library(foreign, pos=4)
  REmodeldata_continuous &lt;- 
  read.dta("F:/data for R.dta",
  convert.dates=TRUE, convert.factors=TRUE, missing.type=TRUE, 
  convert.underscore=TRUE, warn.missing.labels=TRUE)
  
  library(R2WinBUGS)
  
  REmodel_continuous&lt;-function(){
  
  for(i in 1:ns){                      #   LOOP THROUGH STUDIES  
  w[i,1] &lt;-  0    # adjustment for multi-arm trials is zero for control arm  
  delta[i,1] &lt; -  0             # treatment effect is zero for control arm  
  mu[i] ~ dnorm(0,.0001)           # vague priors for all trial baselines 
  for (k in 1:na[i]) {             #  LOOP THROUGH ARMS  
      var[i,k] &lt; -  pow(se[i,k],2)   # calculate variances  
      prec[i,k] &lt;-  1/var[i,k]      # set precisions 
      y[i,k] ~ dnorm(theta[i,k],prec[i,k]) # binomial likelihood 
      theta[i,k] &lt; - mu[i] + delta[i,k]  # model for linear predictor 
  #Deviance contribution 
      dev[i,k] &lt; -  (y[i,k] -theta[i,k])*(y[i,k] -theta[i,k])*prec[i,k]  
     } 
  #  summed residual deviance contribution for this trial  
  resdev[i] &lt; -  sum(dev[i,1:na[i]])         
  for (k in 2:na[i]) {             # LOOP THROUGH ARMS  
  # trial-specific LOR distributions 
      delta[i,k] ~ dnorm(md[i,k],taud[i,k])  
  # mean of LOR distributions, with m ulti-arm trial correction 
      md[i,k] &lt;-   d[t[i,k]] -  d[t[i,1]] + sw[i,k]  
  # precision of LOR distributions (with multi-arm trial correction) 
      taud[i,k] &lt; - tau *2*(k -1)/k  
  # adjustment, multi-arm RCTs 
      w[i,k] &lt;-  (delta[i,k] -  d[t[i,k]] + d[ t[i,1]])  
  # cumulative adjustment for multi-arm trials 
      sw[i,k] &lt; -  sum(w[i,1:k -1])/(k-1)  
     } 
  }    
  totresdev &lt; -  sum(resdev[])            #Total Residual Deviance  
  d[1]&lt;-0       # treatment effect is zero for control arm  
  # vague priors for  treatment effects  
  for (k in 2:nt){  d[k] ~ dnorm(0,.0001) }  
  sd ~ dunif(0,5)     # vague prior for between -trial SD 
  tau &lt;-  pow(sd,-2)   # between-trial precision = (1/between -trial variance)  
  
  # All pairwise comparisons  
  for (c in 1:(nt -1)) {  for (k in (c+1):nt)  { diff[c,k] &lt; -  (d[c] -  d[k] )}}  
  for (k in 1:nt) { 
  rk[k] &lt; -  nt+1 -rank(d[],k) # assumes events are “good”  
  #rk[k] &lt;-  rank(d[],k) # assumes events are “bad”  
  best[k] &lt; -  equals(rk[k],1) #calculate probability that treat k is best  
  }         
  }
  # End of model
  
  filename &lt;- file.path("D://","REmodel_continuous.bug")
  
  ## write model file:
  write.model(REmodel_continuous, filename)
  ## and let’s take a look:
  file.show(filename)
  
  #load data from dataset
  
  t1&lt;-REmodeldata_continuous$t1
  y1&lt;-REmodeldata_continuous$y1
  se1&lt;-REmodeldata_continuous$se1
  
  t2&lt;-REmodeldata_continuous$t2
  y2&lt;-REmodeldata_continuous$y2
  se2&lt;-REmodeldata_continuous$se2
  
  t3&lt;-REmodeldata_continuous$t3
  y3&lt;-REmodeldata_continuous$y3
  se3&lt;-REmodeldata_continuous$se3
  
  t4&lt;-REmodeldata_continuous$t4
  y4&lt;-REmodeldata_continuous$y4
  se4&lt;-REmodeldata_continuous$se4
  
  na&lt;-REmodeldata_continuous$na
  
  t&lt;-c(t1,t2,t3,t4)
  y&lt;-c(y1,y2,y3,y4)
  se&lt;-c(se1,se2,se3,se4)
  
  dim(t)&lt;-c(35,4)
  dim(y)&lt;-c(35,4)
  dim(se)&lt;-c(35,4)
  ns&lt;-35
  nt&lt;-23
  data&lt;-list("t","y","se","na","ns","nt")
  
  #Set Initial Values
  inits&lt;-function(){ 
  #Initial Values 
  #chain 1  
  list(d=c( NA, 0,0,0,0,0,0), sd=1, mu=c(0, 0, 0, 0, 0, 0, 0, 0 , 0, 0,  
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  
  0, 0, 0))
  
  #chain 2  
  list(d=c( NA,  -1, -3, -1,1,3,-1), sd=4, mu=c(-3, -3, -3, -3, -3, -3, -3,     -3, -3, -3,  
  -3, -3, -3, -3, -3, -3, -3, -3, -3, -3,  
  -3, -3, -3))
  
  #chain 3  
  list(d=c( NA, 2,2,2,2,2,2), sd=2, mu=c( -3, 5, -1, -3, 7,  -3, -4, -3, 5, -1,  
  -3, 5, -1, -3, 7, -3, -4, -3, 5, -1,  
  -3, 5, -1))
  }
  
  REmodel_continuous.sim&lt;-    bugs(data,inits,model.file="D:/REmodel_continuous.bug",
                    parameters=c("diff","totresdev"),
                    n.chains=3,n.iter=40000,n.burnin=10000,
                    bugs.directory="C:/Program Files (x86)/WinBUGS14")
  
  print(REmodel_continuous.sim, digits.summary=4)
  
  plot(REmodel_continuous.sim)
  </code></pre>