R/permER.R

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

permER = 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);
  expo_results = NULL;
  expo_results_full = NULL;
  event = target[,2]
  #retrieving the data
  x = dataset[ , xIndex];
  #if the censored indicator is empty, a dummy variable is created
  numCases = dim(dataset)[1];
  if ( length(event) == 0 )  event = vector('numeric',numCases) + 1;
      if ( length(csIndex) == 0 || sum(csIndex == 0, na.rm = TRUE) > 0 ) {
        expo_results <- survival::survreg(target ~ x, dist = "exponential", weights = wei)
        dof <- length( coef(expo_results) ) - 1
        stat = 2 * abs( diff(expo_results$loglik) )
		if (stat > 0) {
          step <- 0
          j <- 1		
          n <- length(x)
          while (j <= R & step < thres ) {
            xb <- sample(x, n)  
            bit2 =  survival::survreg(target ~ xb, dist = "exponential", weights = wei)
            stat2 = 2 * abs( diff(bit2$loglik) )
            step <- step + ( stat2 > stat )
            j <- j + 1
          }
          pvalue <- log( (step + 1) / (R + 1) )
		}  
      } else {
        #fitting the full model
        expo_results <- survival::survreg(target ~ ., data = as.data.frame(dataset[, csIndex]), dist = "exponential", weights = wei) 
        expo_results_full <- survival::survreg(target ~ ., data = as.data.frame( dataset[ , c(csIndex, xIndex)] ), dist = "exponential", weights = wei )
        res = anova(expo_results, expo_results_full)
        stat = abs( res[2, 6] );
		if (stat > 0) {
          xcs  = dataset[, csIndex]
          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) ), dist = "exponential", weights= wei )
            step <- step + ( anova(expo_results, bit2)[2, 6] > stat )
            j <- j + 1
          }
          pvalue <- log( (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)
        }
      }
      #testerrorcaseintrycatch(4);
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
}