Nothing
wald.mmpc <- function(target, dataset, max_k = 3, threshold = 0.05, test = NULL, ini = NULL, wei = NULL, user_test = NULL,
hash = FALSE, hashObject = NULL, ncores = 1, backward = FALSE) {
##############################
# initialization part of MMPC
#############################
runtime <- proc.time()
stat_hash <- NULL
pvalue_hash <- NULL
if ( hash ) {
if (is.null(hashObject) ) {
stat_hash <- Rfast::Hash();
pvalue_hash <- Rfast::Hash();
} else if ( is.list( hashObject ) ) {
stat_hash <- hashObject$stat_hash;
pvalue_hash <- hashObject$pvalue_hash;
} else stop('hashObject must be a list of two hash objects (stat_hash, pvalue_hash)')
}
###################################
# dataset checking and initialize #
###################################
#if ( !is.null(dataset) ) {
# if ( is.matrix(target) ) {
# if ( !is.Surv(target) ) stop('Invalid dataset class. For survival analysis provide a dataframe-class dataset');
# }
#}
if ( is.null(dataset) || is.null(target) ) { #|| (dim(as.matrix(target))[2] != 1 & class(target) != "Surv" ))
stop('invalid dataset or target (class feature) arguments.');
} else target <- target;
if ( any(is.na(dataset)) ) {
warning("The dataset contains missing values (NA) and they were replaced automatically by the variable (column) median (for numeric) or by the most frequent level (mode) if the variable is factor")
dataset <- apply( dataset, 2, function(x){ x[which(is.na(x))] = median(x, na.rm = TRUE) ; return(x) } )
}
##################################
# target checking and initialize #
##################################
targetID <- -1;
#check if the target is a string
if (is.character(target) & length(target) == 1) {
findingTarget <- target == colnames(dataset);#findingTarget <- target %in% colnames(dataset);
if (!sum(findingTarget)==1){
warning('Target name not in colnames or it appears multiple times');
return(NULL);
}
targetID <- which(findingTarget);
target <- dataset[ , targetID];
}
#checking if target is a single number
if (is.numeric(target) & length(target) == 1){
if (target > dim(dataset)[2]){
warning('Target index larger than the number of variables');
return(NULL);
}
targetID <- target;
target <- dataset[ , targetID];
}
################################
# test checking and initialize #
################################
la <- length( unique( as.numeric(target) ) )
if (typeof(user_test) == "closure") {
test = user_test;
} else {
#auto detect independence test in case of not defined by the user and the test is null or auto
if (is.null(test) || test == "auto") {
if ( la == 2 ) target <- as.factor(target)
#if target is a factor then use the Logistic test
if ( "factor" %in% class(target) ) {
if ( is.ordered(target) & la > 2 ) {
test = waldOrdinal
} else {
test = waldLogistic
}
} else if ( ( is.numeric(target) || is.integer(target) ) & survival::is.Surv(target) == FALSE ) {
if ( sum( floor(target) - target ) == 0 & la > 2 ) {
test = "waldPois";
}
} else if ( survival::is.Surv(target) ){
test = "waldCR";
} else stop('Target must be a factor, vector, or a Surv object');
}
#cat("\nConditional independence test used: ");cat(test);cat("\n");
#available conditional independence tests
av_tests = c("waldBeta", "waldCR", "waldWR", "waldER", "waldLLR", "waldClogit", "waldLogistic", "waldPois", "waldNB",
"waldBinom", "auto", "waldZIP", "waldMMReg", "waldIGreg", "waldOrdinal", "waldGamma",
"waldNormLog", "waldTobit", "waldQPois", "waldQBinom", NULL);
ci_test = test
#cat(test)
if ( length(test) == 1 ) { #avoid vectors, matrices etc
test = match.arg(test, av_tests, TRUE);
#convert to closure type
if (test == "waldBeta") {
test = waldBeta;
} else if (test == "waldMMReg") {
test = waldMMReg;
} else if (test == "waldIGreg") {
test = waldIGreg;
} else if (test == "waldPois") {
test = waldPois;
} else if (test == "waldNB") {
test = waldNB;
} else if (test == "waldGamma") {
test = waldGamma;
} else if (test == "waldNormLog") {
test = waldNormLog;
} else if (test == "waldZIP") {
test = waldZIP;
} else if (test == "waldTobit") {
test = waldTobit;
} else if (test == "waldCR") {
test = waldCR;
} else if (test == "waldWR") {
test = waldWR;
} else if (test == "waldER") {
test = waldER;
} else if (test == "waldLLR") {
test = waldLLR;
} else if (test == "waldBinom") {
test = waldBinom;
} else if (test == "waldLogistic") {
test = waldLogistic;
} else if (test == "waldOrdinal") {
test = waldOrdinal;
} else if (test == "waldQPois") {
test = waldQPois;
} else if (test == "waldQBinom") {
test = waldQBinom;
}
#more tests here
} else {
stop('invalid test option');
}
}
###################################
# options checking and initialize #
###################################
#extracting the parameters
max_k <- floor(max_k);
varsize <- ncol(dataset);
#option checking
if ( (typeof(max_k)!="double") || max_k < 1 ) stop('invalid max_k option');
if ( max_k > varsize ) max_k = varsize;
if ( (typeof(threshold) != "double" ) || threshold <= 0 || threshold > 1 ) stop('invalid threshold option');
#######################################################################################
if ( !is.null(user_test) ) ci_test = "user_test";
#call the main MMPC function after the checks and the initializations
oop <- options(warn = -1)
on.exit( options(oop) )
results <- wald.Internalmmpc(target, dataset, max_k, log(threshold), test, ini, wei, user_test, hash, varsize, stat_hash,
pvalue_hash, targetID, ncores = ncores);
varsToIterate <- results$selectedVarsOrder
if ( backward & length( varsToIterate ) > 0 ) {
varsToIterate <- results$selectedVars
varsOrder <- results$selectedVarsOrder
bc <- mmpcbackphase(target, dataset[, varsToIterate], test = test, wei = wei, max_k = max_k, threshold = threshold )
met <- bc$met
results$selectedVars <- varsToIterate[met]
results$selectedVarsOrder <- varsOrder[met]
results$pvalues[varsToIterate] <- bc$pvalues
results$n.tests <- results$n.tests + bc$counter
}
runtime <- proc.time() - runtime
MMPCoutput <-new("MMPCoutput", selectedVars = results$selectedVars, selectedVarsOrder = results$selectedVarsOrder,
hashObject = results$hashObject, pvalues = results$pvalues, stats = results$stats, univ = results$univ,
max_k = results$max_k, threshold = results$threshold, n.tests = results$n.tests, runtime = runtime, test = ci_test);
return(MMPCoutput);
}
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