R/testIndRQ.R

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

testIndRQ <- function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL, 
                     hash = FALSE, stat_hash=NULL, pvalue_hash=NULL) {
  # TESTINDRQ Conditional Independence Test for numerical class variables 
  # PVALUE = TESTINDRQ(Y, DATA, XINDEX, CSINDEX)
  # This test provides a p-value PVALUE for the NULL hypothesis H0 which is
  # X is independent by TARGET given CS. The pvalue is calculated following
  # nested models
  # This method requires the following inputs
  #   TARGET: a numeric vector containing the values of the target (numerical) variable. 
  #   Its support can be R or any number betweeen 0 and 1, i.e. it contains proportions.
  #   DATASET: a numeric data matrix containing the variables for performing the test. They can be mixed variables. 
  #   XINDEX: the index of the variable whose association with the target we want to test. 
  #   CSINDEX: the indices if the variable to condition on. 
  # this method returns: the pvalue PVALUE, the statistic STAT.
  # References
  # [1] Koenker, Roger. Quantile regression. New York, Cambridge        
  # university press, 2005.
  #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]) == 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);
    }
  }
  #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 testIndRQ : 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(
{
  #if the conditioning set (cs) is empty, we use a simplified formula
  if (length(cs) == 0) {
    #compute the relationship between x,target directly
    fit1 < quantreg::rq(target ~ 1, weights = wei)
    fit2 <- quantreg::rq(target ~ x, weights = wei)
  } else {
    kapa <- length( csIndex )
    ff1 <- as.formula(paste("target ~ ",paste("dataset[,",csIndex[1:kapa],"]",sep="",collapse="+"), sep=""))
    ff2 <- as.formula(paste(paste("target ~ ",paste("dataset[,",csIndex[1:kapa],"]",sep="",collapse="+"), sep="") , "+dataset[,",xIndex,"]", sep = ""))
    fit1 <- quantreg::rq( ff1, weights = wei )
    fit2 <- quantreg::rq( ff2, weights= wei )
  }
    mod <- anova(fit1,fit2, test = "rank")
    df1 <- as.numeric( mod[[1]][1] )
    df2 <- as.numeric( mod[[1]][2] )
    stat <- as.numeric( mod[[1]][3] )
    pvalue <- pf(stat, df1, df2, 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)
    }
  }
  #testerrorcaseintrycatch(4);
  results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
  return(results);
  
},
error = function(cond) {
  #error case
  pvalue <- log(1);
  stat <- 0;
  results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
  return(results)
},
finally = {}
)    
return(res);
}