R/Brandi/Energy Tree/Regression_1.5/Simulation_REG.R

In tulliapadellini/energytree: Conditional Inference trees for mixed type data

##########################
#Load the libraries

library(fda.usc)
library(roahd)
library(energy)
library(entropy)
library(partykit)

##Load the classification tree function
source("functionsREG.R")


################
#Build the  functional object ----- define parameters

size <- c(50,50,50,50)

P <- 50
n.var <- 1


############
#Load the function that create the multivariate functional object

source("gen_data_REG_CATS.R")
#Generate data
data=list()

M=100



for(i in 1:M){
  alpha <- matrix( round(runif(4,0.1,1),2),
                   nrow=4, ncol=1)
  beta <- matrix( round(runif(4,0.1,1),2),
                  nrow=4, ncol=1)
  data[[i]]=gen_data_reg(size=size,P=P,n.var=n.var,alpha=alpha,beta=beta,
                 sigma=1.5)
  
    
}

#save(data, file = "Regression_Sim_Dataset.RData")
load("Regression_Sim_Dataset.RData")

MEPmy <- c()

MEP_b <- c()

MEP_pc <- c()
for(i in 1:M){
  print(i)

  #select train e test set
  #id.train <- sample(1:(3*size[1]), size=0.7*3*size[1])
  #list.train <- lapply(data[[i]], function(x) {x[id.train]})
  #list.test <- lapply(data[[i]], function(x) {x[-id.train]})
 
  nb <- 15
  
  #REGRESSION FUNCTIONAL TREE
  myREG <- mytree(Y="Y", data=data[[i]], weights = NULL, 
                  minbucket = 5, 
                  alpha = 0.05, R = 1000, 
                  rnd.sel = T, rnd.splt = TRUE, nb=nb)
  plot(myREG)
  
  #FUNCTIONAL LINEAR MODEL BASIS
  x <- data[[i]]$V1
  y <- data[[i]]$Y
  tt=x[["argvals"]]
  
  dataf=as.data.frame(y)
  
  nbasis.x=15
  
  basis1=create.bspline.basis(rangeval=range(tt),nbasis=nbasis.x)
  

  basis.x=list("x"=basis1)

  
  res=fregre.basis(x,y,basis.x=basis1)
  
  
  
  #########FUNCTIONAL LINEAR MODEL --- PC
  
  
  res2 <- fregre.pc(x, y, kmax = 7)
  
  
  
 
  ###################
  ##PREDICTION MYREG##
  
  
  foo <- min.basis(data[[i]]$V1, numbasis = nb)
  fd3 <- fdata2fd(foo$fdata.est, type.basis = "bspline", nbasis = foo$numbasis.opt)
  m.coef <- data.frame(t(fd3$coefs))
  for(j in 1: dim(m.coef)[2]){
    names(m.coef)[j] <- paste("V1",names(m.coef)[j],sep = ".")
  }
  y_pred=predict(myREG, newdata = m.coef)
  MEPmy[i] <- (sum((y-y_pred)^2)/length(y))/(var(y))
  
  #################
  ##PREDICTION FLM
  
  y_pred1 <- predict.fregre.fd(res, new.fdataobj=data[[i]]$V1)
  MEP_b[i] <- (sum((y-y_pred1)^2)/length(y))/(var(y))
  
  
  ###PREDICTION PC
  y_pred2 <- predict.fregre.fd(res2,data[[i]]$V1)
  MEP_pc[i] <- (sum((y-y_pred2)^2)/length(y))/(var(y))
  

  
}

save(MEPmy,MEP_b,MEP_pc, file="results.RData")