R/permReg.R

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

permReg = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL , hash = FALSE, stat_hash = NULL, 
                      pvalue_hash = NULL, threshold = 0.05, R = 999) {
  # TESTINDREG Conditional Independence Test for continous class variables 
  # PVALUE = TESTINDREG(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 (continuous) 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] Norman R. Draper and Harry Smith. Applied Regression 
  # Analysis, Wiley, New York, USA, third edition, May 1998.
  #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);
    }
  }
  #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] <- 1;#.set(pvalue_hash , key , 1)
    }
    results <- list(pvalue = 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 testIndReg : 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];
  n = length(target)
  #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] <- 1;   #.set(pvalue_hash , key , 1)
        }
        results <- list(pvalue = 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] <- 1;    #.set(pvalue_hash , key , 1)
          }
          results <- list(pvalue = 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 ) {
    fit2 = lm( target ~ x, weights = wei, y = FALSE, model = FALSE )
    stat = anova(fit2)[1, 4]
    step <- 0
    j <- 1		
    n <- summary(fit2)[[10]][3] + 2
    while ( j <= R & step < thres ) {
      xb <- sample(x, n)  
      bit2 <- lm(target ~ xb, weights = wei, y = FALSE, model = FALSE )
      step <- step + ( anova(bit2)[1, 4] > stat )
      j <- j + 1
    }
    pvalue <- log( (step + 1) / (R + 1) )
    
  } else {
    fit2 = lm( target ~ cs + x, weights = wei )
    stat = anova(fit2)[2, 4]
    step <- 0
    j <- 1		
    n <- summary(fit2)[[10]][3] + 2
    while ( j <= R & step < thres ) {
     xb <- sample(x, n)  
     bit2 <- lm(target ~ cs + xb, weights = wei, y = FALSE, model = FALSE )
     step <- step + ( anova(bit2)[2, 4] > stat )
     j <- j + 1
    }
    pvalue <- 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)
    }
  }
  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);
}