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waldNormLog = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL , hash = FALSE, stat_hash = NULL, pvalue_hash = NULL)
{
## 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]) ) {
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 testIndPois : 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 ( any(x != cs) == FALSE ) { #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 (any(x != cs[, col]) == FALSE) { #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 the conditioning set (cs) is empty, we use a simplified formula
if (length(cs) == 0) {
fit = glm(target ~ x, family = gaussian(link = log), weights = wei)
} else fit = glm(target ~., data = as.data.frame( dataset[, c(csIndex, xIndex)] ), family = gaussian(link = log), weights = wei)
if ( any (is.na(fit$coefficients) ) ) {
stat <- 0
pvalue <- log(1)
} else {
mod = summary(fit)[[ 12 ]]
pr = dim(mod)[1]
stat = mod[pr, 3]^2 / summary(fit)[[ 14 ]]
pvalue = pchisq( stat, 1, lower.tail = FALSE, log.p = TRUE )
}
if ( is.na(pvalue) || is.na(stat) ) {
pvalue = log(1);
stat = 0;
} else {
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);
}
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