R/min_assoc.gee.R

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

min_assoc.gee = function(target, reps, group, dataset, test, max_k, cvar, wei, selectedVars, pvalues, stats, univariateModels, 
                              selectedVarsOrder, hash, stat_hash, pvalue_hash, correl, se) {
  
  ma_pvalue = pvalues[[cvar]];    #CHANGE
  ma_stat = stats[[cvar]];    #CHANGE
  selectedVars = which(selectedVars==1);
  k = min( c(max_k , length(selectedVars)) );
  ck = 1;
  
  while ( ck <= k )  {
    #lastvar = unique(which(selectedVarsOrder == max(selectedVarsOrder)));
    lastvar = which(selectedVarsOrder == max(selectedVarsOrder))[1]; #CHANGE
    
    tempCS = setdiff(selectedVars, lastvar) #CHANGE
    if (ck == 1) {     #CHANGE
      subsetcsk = as.matrix(lastvar);      #CHANGE
    } else {
      subsetcsk = as.matrix( nchoosek(tempCS, ck - 1) )
      numSubsets = dim(subsetcsk)[2];     #CHANGE
      subsetcsk = rbind(subsetcsk, lastvar*rep(1,numSubsets));    #CHANGE
    }
    
    for ( i in 1:ncol(subsetcsk) ) {
      s = subsetcsk[,i];
      cur_results = test(target, reps, group, dataset, cvar, s, wei = wei, univariateModels, hash = hash, stat_hash, pvalue_hash, correl = correl, se = se);
      stat_hash = cur_results$stat_hash;
      pvalue_hash = cur_results$pvalue_hash;
      #check if the pvalues and stats should be updated
      if ( !compare_p_values(cur_results$pvalue, ma_pvalue, cur_results$stat, ma_stat) ) {
        ma_pvalue = cur_results$pvalue;
        pvalues[[cvar]] = cur_results$pvalue;
        ma_stat = cur_results$stat;
        stats[[cvar]] = cur_results$stat;
      }
    }
    ck = ck+1;
  }
  results <- list(pvalue = ma_pvalue, stat = ma_stat, pvalues = pvalues, stats = stats, stat_hash=stat_hash, pvalue_hash = pvalue_hash, correl = correl, se = se);
  return(results);
}