Nothing
testIndBinom = function(target, dataset, xIndex, csIndex, wei = NULL, univariateModels = NULL , hash = FALSE, stat_hash = NULL,
pvalue_hash = NULL) {
# TESTINDPOIS Conditional Independence Test for discrete class variables
# PVALUE = TESTINDPOIS(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 (discrete) 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] McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
#########################################################################################################
#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);
#extract the data
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);
}
}
}
}
y = target[, 1]
wei = target[, 2]
#if the conditioning set (cs) is empty, we use a simplified formula
if ( length(cs) == 0 ) {
fit2 = glm(y / wei ~ x, binomial, weights = wei, model = FALSE)
stat = fit2$null.deviance - fit2$deviance
dof = length( fit2$coefficients ) - 1
} else {
fit1 = glm( y / wei ~., weights = wei, data = as.data.frame( cs ), binomial, model = FALSE )
fit2 = glm( y / wei ~., weights = wei, data = as.data.frame( dataset[, c(csIndex, xIndex)] ), binomial, model = FALSE )
stat = fit1$deviance - fit2$deviance
dof = length( fit2$coefficients ) - length( fit1$coefficients )
}
pvalue = pchisq( stat, dof, 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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