R/permTobit.R

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

permTobit = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL, hash = FALSE, stat_hash=NULL, pvalue_hash=NULL,
                  threshold = 0.05, R = 999){
  # Conditional independence test based on the Log Likelihood ratio test
  if (!survival::is.Surv(target) )   stop('The survival test can not be performed without a Surv object target');
  csIndex[which(is.na(csIndex))] = 0;
  thres <- threshold * R + 1
  if ( hash )  {
    csIndex2 = csIndex[which(csIndex!=0)]
    csIndex2 = sort(csIndex2)
    xcs = c(xIndex, csIndex2)
    key = paste(as.character(xcs) , collapse=" ");
    if (is.null(stat_hash[key]) == FALSE) {
      stat = stat_hash[key];
      pvalue = pvalue_hash[key];
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    }
  }
  #initialization: these values will be returned whether the test cannot be carried out
  pvalue = log(1)
  stat = 0;
  results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
  x <- dataset[ , xIndex]
  if ( length(csIndex) == 0 || sum(csIndex == 0, na.rm = TRUE) > 0 ) {
    tob <- survival::survreg(target ~ x, weights = wei, dist = "gaussian")
    stat = 2 * abs( diff(tob$loglik) )
    if (stat > 0) {
      step <- 0
      j <- 1	
      n <- length(x)
      while (j <= R & step < thres ) {
        xb <- sample(x, n)  
        bit2 =  survival::survreg(target ~ xb, weights = wei, dist = "gaussian")
        stat2 = 2 * abs( diff(bit2$loglik) )
        step <- step + ( stat2 > stat )
        j <- j + 1
      }
      pvalue <- log( (step + 1) / (R + 1) )        
    }  
    
  } else {
    tob <- survival::survreg(target ~ ., data = as.data.frame( dataset[ , csIndex] ), weights = wei, dist = "gaussian") 
    tob_full <- survival::survreg(target ~ ., data = as.data.frame(  dataset[ , c(csIndex, xIndex)] ), weights = wei, dist = "gaussian" )
    res = anova(tob, tob_full)
    stat = res[2, 6]
    if (stat > 0) {
      j <- 1	
      step <- 0
      n <- length(x)
      while (j <= R & step < thres ) {
        xb <- sample(x, n)  
        bit2 = survival::survreg(target ~., data = as.data.frame( cbind(dataset[ ,csIndex], xb ) ), weights= wei, dist = "gaussian" )
        step <- step + ( anova(tob, bit2)[2, 6] > stat )
        j <- j + 1
      }
      pvalue <- (step + 1) / (R + 1)
    }  
  }  
  if ( is.na(pvalue) || is.na(stat) ) {
    pvalue = log(1)
    stat = 0;
  } else {
    #update hash objects
    if( hash )  {
      stat_hash[key] <- stat;      #.set(stat_hash , key , stat)
      pvalue_hash[key] <- pvalue;     #.set(pvalue_hash , key , pvalue)
    }
  }
  results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
  return(results);
}