R/waldBinom.R

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

waldBinom = 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);
    }
  }
  #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( any(xIndex < 0) || any(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);
  }
  #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 (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);
        }
      }
    }
  }
  y = target[, 1]
  wei = target[, 2]
      #if the conditioning set (cs) is empty, we use a simplified formula
      if ( length(cs) == 0 )  {
        fit = glm(y / wei ~ x, binomial, weights = wei, model = FALSE)
      } else   fit = glm( y / wei ~., weights = wei, data = as.data.frame( dataset[, c(csIndex, xIndex)] ), binomial, model = FALSE )
	      if ( any( is.na( fit$coefficients ) ) ) {
        stat <- 0
    		pvalue <- log(1)
      } else {		
        mod = summary(fit)[[ 12 ]]
        pr = dim(mod)[1]
        stat = mod[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);
}