R/permIGreg.R

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

permIGreg = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL, hash = FALSE, stat_hash = NULL, 
                       pvalue_hash = NULL, threshold = 0.05, R = 999) {
  # TESTINDPOIS Conditional Independence Test for discrete class variables 
  # PVALUE = TESTINDPOIS(Y, DATA, XINDEX, CSINDEX)
  # This test provides a p-value PVALUE for the NULL hypothesis H0 which is
  # X is independent by TARGET given CS. The pvalue is calculated following
  # nested models
  # This method requires the following inputs
  #   TARGET: a numeric vector containing the values of the target (discrete) variable. 
  #   Its support can be R or any number betweeen 0 and 1, i.e. it contains proportions.
  #   DATASET: a numeric data matrix containing the variables for performing the test. They can be mixed variables. 
  #   XINDEX: the index of the variable whose association with the target we want to test. 
  #   CSINDEX: the indices if the variable to condition on. 
  # this method returns: the pvalue PVALUE, the statistic STAT.
  #if the test cannot performed succesfully these are the returned values
  pvalue = log(1)
  stat = 0;
  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]) ) {
      stat = stat_hash[key];
      pvalue = pvalue_hash[key];
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    }
  }
  #if the xIndex is contained in csIndex, x does not bring any new information with respect to cs
  if ( !is.na(match(xIndex,csIndex)) ) {
    if (hash) {  #update hash objects
      stat_hash[key] <- 0;      #.set(stat_hash , key , 0)
      pvalue_hash[key] <- 1;         #.set(pvalue_hash , key , 1)
    }
    results <- list(pvalue = 1, stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
    return(results);
  }
  
  #check input validity
  if ( xIndex < 0 || csIndex < 0 ) {
    message(paste("error in testIndPois : wrong input of xIndex or csIndex"))
    results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
    return(results);
  }
  
  xIndex = unique(xIndex);
  csIndex = unique(csIndex);
  #extract the data
  x = dataset[, xIndex];
  cs = dataset[, csIndex];
  #That means that the x variable does not add more information to our model due to an exact copy of this in the cs, so it is independent from the target
  if ( length(cs) != 0 ) {
    if ( is.null(dim(cs)[2]) ) {  #cs is a vector
      if ( identical(x, cs) ) { #if(!any(x == cs) == FALSE)
        if (hash) {  #update hash objects
          stat_hash[key] <- 0;#.set(stat_hash , key , 0)
          pvalue_hash[key] <- 1;#.set(pvalue_hash , key , 1)
        }
        results <- list(pvalue = 1, stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
        return(results);
      }
    } else {     #more than one var
      for (col in 1:dim(cs)[2]) {
        if ( identical(x, cs[, col]) ) {     #if(!any(x == cs) == FALSE)
          if (hash) {       #update hash objects
            stat_hash[key] <- 0;           #.set(stat_hash , key , 0)
            pvalue_hash[key] <- 1;        #.set(pvalue_hash , key , 1)
          }
          results <- list(pvalue = 1, stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
          return(results);
        }
      }
    }
  }
  res <- tryCatch(
    {
      #if the conditioning set (cs) is empty, we use a simplified formula
      if ( length(cs) == 0 ) {
        fit2 = glm(target ~ x, family = inverse.gaussian(log), weights = wei)
        dev2 <- fit2$deviance
        stat = fit2$null.deviance - dev2
    	  if ( stat > 0 ) {
          step <- 0
          j <- 1		
          n <- length(target)
          while (j <= R & step < thres ) {
            xb <- sample(x, n)  
            bit2 <- glm(target ~ xb, family = inverse.gaussian(link = log), weights = wei)  
            step <- step + ( bit2$deviance < dev2 )
            j <- j + 1
          }
          pvalue <- log( (step + 1) / (R + 1) )
        } 
      } else {
        fit1 = glm(target ~ cs, family = inverse.gaussian(log), weights = wei)
        fit2 = glm(target ~ cs + x, family = inverse.gaussian(log), weights = wei)
        dev2 <- fit2$deviance
        stat <- fit1$deviance - dev2
    	  if ( stat > 0 ) {
		      step <- 0
          j <- 1		
          n <- length(target)
          while (j <= R & step < thres ) {
            xb <- sample(x, n)  
            bit2 <- glm(target ~ cs + xb, family = inverse.gaussian(link = log), weights = wei)  
            step <- step + ( bit2$deviance < dev2 )
            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)
        }
      }

      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    },
    error=function(cond) {
      pvalue = log(1)
      stat = 0;    
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    },
    finally={}
  )    
  return(res);
}