R/permZIP.R

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

permZIP = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL , hash = FALSE, stat_hash=NULL, pvalue_hash=NULL,
                     threshold = 0.05, R = 999) {
  #initialization
  #if the test cannot performed succesfully these are the returned values
  pvalue = log(1);
  stat = 0;
  csIndex[ which( is.na(csIndex) ) ] = 0;
  thres <- threshold * R + 1
  
  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]) == FALSE) {
      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 testIndZIP : 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);
        }
      }
    }
  }
  res <- tryCatch(
    {
      n <- length(target) 
      lgy <- sum( lgamma(target + 1) )  
      #if the conditioning set (cs) is empty, we use a simplified formula
      if (length(cs) == 0) {
        #compute the relationship between x,target directly
        if ( !is.null(wei) )  fit1 <- zipmle.wei(target, wei)  else  fit1 <- Rfast::zip.mle(target)
        lik2 <- zip.reg(target, x, wei = wei, lgy = lgy)$loglik 
        stat <- 2 * abs( lik2 - fit1$loglik )
		    if(stat > 0) {
          step <- 0
          j <- 1		
          while (j <= R & step < thres ) {
            xb <- x[sample(n, n), ]  
            bit2 <- zip.reg(target, xb, wei = wei, lgy = lgy)$loglik 
            step <- step + ( bit2 > lik2 )
            j <- j + 1
          }
          pvalue <- log( log( (step + 1) / (R + 1) ))
		}  

      } else {
        fit1 <- zip.reg(target, cs, wei = wei, lgy = lgy) 
        fit2 <- zip.reg(target, cbind(cs, x), wei = wei, lgy = lgy) 
        stat <- 2 * abs( fit2$loglik - fit1$loglik )
		    if (stat > 0) {
          step <- 0
          j <- 1		
          while (j <= R & step < thres ) {
            xb <- x[sample(n, n), ]  
            bit2 <- zip.reg(target, cbind(cs, xb), wei = wei, lgy = lgy)$loglik 
            step <- step + ( bit2 > lik2 )
            j <- j + 1
          }
          pvalue <- log( log( (step + 1) / (R + 1) ))
		}  
      } 
      #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)
        }
      }
      #testerrorcaseintrycatch(4);
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
      
    },
    error=function(cond) {
      pvalue = log(1)
      stat = 0;
      results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
      return(results);
    },
    finally={}
  )    
  return(res);
}