Nothing
testIndOrdinal = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL, hash = FALSE, stat_hash = NULL,
pvalue_hash = NULL) {
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 test cannot performed succesfully these are the returned values
pvalue = log(1);
stat = 0;
#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 testIndLogistic : wrong input of xIndex or csIndex"))
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
x = dataset[ , xIndex];
cs = dataset[ , csIndex];
if (length(cs) == 0 || any( is.na(cs) ) ) cs = NULL;
#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);
}
}
}
}
if (length(cs) == 0) {
#if the univariate models have been already compute
if ( !is.null(univariateModels) ) {
pvalue <- univariateModels$pvalue[[xIndex]];
stat <- univariateModels$stat[[xIndex]];
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results);
}
#Fitting ordinal Logistic regression
fit1 <- ordinal::clm(target ~ 1, weights = wei )
fit2 <- ordinal::clm(target ~ x, weights = wei )
stat <- 2 * fit2$logLik - 2 * fit1$logLik
dof <- length( coef(fit2) ) - length( coef(fit1) )
} else {
#Fitting ordinal Logistic regression
fit1 <- ordinal::clm( target ~., data = data.frame( cs ), weights = wei )
fit2 <- ordinal::clm(target ~., data = data.frame( dataset[, c(csIndex, xIndex)] ), weights = wei )
stat <- 2 * fit2$logLik - 2 * fit1$logLik
dof <- length( coef(fit2) ) - length( coef(fit1) )
}
pvalue = pchisq(stat, dof, lower.tail = FALSE, log.p = TRUE);
#update hash objects
if (hash) {
stat_hash[key] <- stat; #.set(stat_hash , key , stat)
pvalue_hash[key] <- pvalue; #.set(pvalue_hash , key , pvalue)
}
#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);
}
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