R/waldLogistic.R

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

waldLogistic = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL, hash = FALSE, stat_hash = NULL, pvalue_hash = NULL)
{
  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 test cannot performed succesfully these are the returned values
  pvalue = log(1);
  stat = 0;
  #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 testIndLogistic : wrong input of xIndex or csIndex"))
    results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
    return(results);
  }

  x = dataset[ , xIndex];
  cs = dataset[ , csIndex];
  if(length(cs) == 0 || any( is.na(cs) ) )  cs = NULL;
  #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) {
      fit = glm(target ~ x, binomial, weights = wei, model = FALSE)
    } else   fit = glm(target ~ cs + x, binomial, weights = wei, model = FALSE)
	if ( any (is.na(fit$coefficients) ) ) {
	stat <- 0
    pvalue <- log(1)
    } else {	
      res = summary(fit)[[ 12 ]]
      pr = dim(res)[1]
      stat = res[pr, 3]^2 
      pvalue = pchisq(stat, 1, lower.tail = FALSE, log.p = TRUE); 
	}  
    if (hash) {
      stat_hash[key] <- stat;     #.set(stat_hash , key , stat)
      pvalue_hash[key] <- pvalue;        #.set(pvalue_hash , key , pvalue)
    }
    #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);
}