R/OTReg.R

In OTE: Optimal Trees Ensembles for Regression, Classification and Class Membership Probability Estimation

OTReg <-
function(XTraining,YTraining,p=0.2,t.initial= NULL, nf=NULL,ns=NULL,info=TRUE){
  
  
  t.initial <- ifelse(is.null(t.initial),1000,t.initial)

  ntr <- nrow(XTraining)   #Number of observations in the training data provided
  
  training2 <- sample(1:ntr,0.05*ntr) ## Sample of indices of training observations for second phase selection
  
  Xtraining1 <- XTraining[-training2,]
  
  Ytraining1 <- YTraining[-training2]
  
  
  rff   <-list() 
  #rf.use <-list()
  er    <- c()
  if(info==TRUE)
  cat("Assessing trees for individual performance..............\n")
  for (j in 1:t.initial){
    
    rff[[j]] <- randomForest(x=Xtraining1,y=Ytraining1, ntree=1, keep.forest=TRUE,norm.votes=FALSE,mtry =ifelse(is.null(nf),round(sqrt(length(Xtraining1))),nf),nodesize=ifelse(is.null(ns),5,ns))#sqrt(length(Xtraining1)))
    er[j]   <-  1-rff[[j]][5]$rsq
    
  }
  order1 <-order(er)   #order the error vector in increasing order
  rff    <-rff[order1]  #order trees in according to the order of errors
  rf.all <- rff[[1]]
  RF.ALL <- rff[[1]]    # initialize the forest best trees from the best single tree
  if(info==TRUE)
  cat("Assessing trees for collective performance..............\n")
  for (k in 1:(p*length(rff)-1))
  {
    p1 <- predict(rf.all, XTraining[training2,])
    sst1 <- sum((YTraining[training2]-mean(YTraining[training2]))^2)
    sse1 <- sum((YTraining[training2]-p1)^2)
    unexp1 <- sse1/sst1
    
    rf.all<-combine(rf.all,rff[[k+1]])
    p2 <- predict(rf.all, XTraining[training2,])
    sst <- sum((YTraining[training2]-mean(YTraining[training2]))^2)
    sse <- sum((YTraining[training2]-p2)^2)
    unexp <- sse/sst
    
    if(unexp1>unexp)
      RF.ALL<-combine(RF.ALL,rff[[k+1]])
    
  }
  if(info==TRUE)
  cat("Number of trees selected............................  =  ",RF.ALL$ntree,"\n")
  results <- list("t.object" = RF.ALL, "selected trees" = RF.ALL$ntree)
  return((results))
}