Nothing
testIndSPML = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels=NULL,
hash = FALSE, stat_hash = NULL, pvalue_hash = NULL) {
# 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.
#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 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];
#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 the conditioning set (cs) is empty, we use a simplified formula
if (length(cs) == 0) {
if ( is.matrix(target) ) {
y <- ( atan(target[, 2]/target[, 1]) + pi * I(target[, 1] < 0) ) %% (2 * pi)
fit1 <- Rfast::spml.mle(y)
} else fit1 <- Rfast::spml.mle(target)
fit2 <- try( Rfast::spml.reg(target, x), silent = TRUE)
if ( identical( class(fit2), "try-error" ) ) {
pvalue <- log(1)
stat <- 0
} else {
stat <- 2 * fit2$loglik - 2 * fit1$loglik
pvalue <- pchisq(stat, 2, lower.tail = FALSE, log.p = TRUE)
}
} else {
fit1 <- try( Rfast::spml.reg(target, cs), silent = TRUE)
fit2 <- try( Rfast::spml.reg( target, dataset[, c(csIndex, xIndex)] ), silent = TRUE)
if ( identical( class(fit1), "try-error" ) | identical( class(fit2), "try-error" ) ) {
pvalue <- log(1)
stat <- 0
} else {
stat <- 2 * fit2$loglik - 2 * fit1$loglik
pvalue <- pchisq(stat, 2, 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)
}
}
results <- list(pvalue = pvalue, stat = stat, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
return(results)
}
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