R/testIndIGreg.R

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

testIndIGreg = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL , hash = FALSE, stat_hash = NULL, 
                       pvalue_hash = NULL) {
  # 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.
  # References
  # [1] McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
  #initialization
  #if the test cannot performed succesfully these are the returned values
  pvalue = log(1);
  stat = 0;
  csIndex[which(is.na(csIndex))] = 0;
  
  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] <- log(1);#.set(pvalue_hash , key , 1)
    }
    results <- list(pvalue = log(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);
  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] <- log(1);        #.set(pvalue_hash , key , 1)
        }
        results <- list(pvalue = log(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] <- log(1);     #.set(pvalue_hash , key , 1)
          }
          results <- list(pvalue = log(1), stat = 0, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
          return(results);
        }
      }
    }
  }
  #trycatch for dealing with errors
  res <- tryCatch(
    {
      #if the conditioning set (cs) is empty, we use a simplified formula
      if ( length(cs) == 0 ) {
        fit1 <- glm(target ~ 1, family = inverse.gaussian(log), weights = wei)
        fit2 <- glm(target ~ x, family = inverse.gaussian(log), weights = wei)
	    	tab <- anova(fit1, fit2, test = "F") 
        stat <- tab[2, 5]
        df1 <- tab[2, 3]
        df2 <- tab[2, 1]
        pvalue <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE ) 
        
      } else {
        fit1 <- glm(target ~., data = as.data.frame( cs ), family = inverse.gaussian(log), weights = wei)
        fit2 <- glm(target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), family = inverse.gaussian(log), weights = wei)
        tab <- anova(fit1, fit2, test = "F") 
        stat <- tab[2, 5]
        df1 <- tab[2, 3] 
        df2 <- tab[2, 1]      } 
        pvalue <- pf( stat, df1, df2, lower.tail = FALSE, log.p = TRUE ) 
      #last error check
      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) {
      #error case
      pvalue = log(1);
      stat = 0;     
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    },
    finally={}
  )    
  return(res);
}