Nothing
compVarImp = function(X, y,rForest,nPerm = 1){
if (!inherits(rForest, "randomForest")) stop("rForest is not of class randomForest")
if(is.null(rForest$inbag)) stop("in randomForest keep.inbag must be set to True")
if(is.null(rForest$forest)) stop("in randomForest keep.forest must be set to True")
n = nrow(X)
p = ncol(X)
if (n == 0) stop("data (x) has 0 rows")
x.col.names <- if (is.null(colnames(X))) 1:p else colnames(X)
if (is.data.frame(X)) {
X = data.matrix(X)
}
if (!is.null(y)) {
if (length(y) != n) stop("length of response must be the same as predictors")
}
if (any(is.na(X))) stop("NA not permitted in predictors")
if (any(is.na(y))) stop("NA not permitted in response")
classRF = is.factor(y)
if (classRF) {
if (!all(levels(y) == levels(rForest$y))) stop("y and rForest$y must have the same levels")
if(rForest$type == "regression") stop("rForest$type = regression !! y a factor ")
nclass = length(levels(y))
classOut = .Call('vita_Rcpp_compVarImpCL', PACKAGE = 'vita',
t(X), as.integer(y), n, p,
rForest$ntree, as.integer(nclass),
rForest$forest$treemap[,1,], rForest$forest$treemap[,2,],
rForest$forest$nodestatus, rForest$forest$xbestsplit,
rForest$forest$nodepred, rForest$forest$bestvar,
rForest$inbag, rForest$forest$ndbigtree,
rForest$forest$ncat, rForest$forest$maxcat )
dimnames(classOut$importance) = list(x.col.names, c(levels(y), "MeanDecreaseAccuracy"))
dimnames(classOut$importanceSD) = list(x.col.names, c(levels(y), "MeanDecreaseAccuracy"))
return( list( importance = classOut$importance,
importanceSD = classOut$importanceSD,
type = "classification"
)
)
}else {
if(rForest$type == "classification") stop("rForest$type = classification !! y not a factor ")
regOut = .Call('vita_Rcpp_compVarImpReg', PACKAGE = 'vita', t(X),y, n,p,
rForest$ntree,nPerm,rForest$forest$leftDaughter,
rForest$forest$rightDaughter,rForest$forest$nodestatus,
rForest$forest$xbestsplit,rForest$forest$nodepred,
rForest$forest$bestvar,rForest$inbag,rForest$forest$ndbigtree,
rForest$forest$ncat, max(rForest$forest$ncat))
return( list( importance = matrix(regOut$importance, p, 1,
dimnames=list(x.col.names, c("%IncMSE")) ),
importanceSD = matrix(regOut$importanceSD, p, 1,
dimnames=list(x.col.names, c("%IncMSE")) ),
type = "regression"
)
)
}
}
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