R/waldNB.R

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

waldNB = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL , hash = FALSE, stat_hash=NULL, pvalue_hash=NULL) {
  #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);
    }
  }
  #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( any(xIndex < 0) || any(csIndex < 0) ) {
    message(paste("error in testIndNB : 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 (any(x != cs) == FALSE) {   #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 (any(x != cs[,col]) == FALSE ) {    #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);
        }
      }
    }
  }
      if ( length(cs) == 0 )  {
        #compute the relationship between x,target directly
        fit = MASS::glm.nb( target ~ x, weights = wei )
      } else {
        fit = MASS::glm.nb( target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), weights = wei )  
        if ( is.null(fit) )  fit = MASS::glm.nb( target ~., data = as.data.frame( dataset[, c(xIndex, csIndex)] ), weights = wei ) 
      }
  	  if ( any( is.na( fit$coefficients ) ) ) {
        stat <- 0
		pvalue <- log(1)
      } else {		
        res = summary(fit)[[ 11 ]]
        pr = dim(res)[1]
        stat = res[pr, 3]^2
        pvalue = pchisq(stat, 1, 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);
}