R/waldGamma.R

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

waldGamma = 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);
  }
  
  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 the conditioning set (cs) is empty, we use a simplified formula
  if (length(cs) == 0)  {
    fit = glm(target ~ x, family = Gamma(link = log), weights = wei)
  } else    fit = glm(target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), family = Gamma(link = log), weights = wei)
  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 / summary(fit)[[ 14 ]]
    pvalue = pchisq( stat, 1, lower.tail = FALSE, log.p = TRUE ) 
  }	
  if ( is.na(pvalue) || is.na(stat) ) {
    pvalue = log(1);
    stat = 0;
  } else {
    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);
  
}