1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
|
#' @rdname varImp
#' @export
#' @importFrom recipes juice all_predictors all_outcomes
"varImp.train" <- function(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...) {
code <- object$modelInfo
if(is.null(code$varImp)) useModel <- FALSE
if(useModel) {
checkInstall(code$library)
for(i in seq(along.with = code$library))
do.call("requireNamespaceQuietStop", list(package = code$library[i]))
imp <- code$varImp(object$finalModel, ...)
modelName <- object$method
} else {
if(inherits(object, "train.recipe")) {
x_dat <- recipes::juice(object$recipe, all_predictors())
x_dat <- as.data.frame(x_dat, stringsAsFactors = FALSE)
y_dat <- recipes::juice(object$recipe, all_outcomes())
y_dat <- getElement(y_dat, names(y_dat))
} else {
isX <- which(!(colnames(object$trainingData) %in% ".outcome"))
x_dat <- object$trainingData[, isX,drop = FALSE]
y_dat <- object$trainingData[, -isX]
}
imp <- filterVarImp(x_dat, y_dat,
nonpara = nonpara,
...)
modelName <- ifelse(is.factor(y_dat),
"ROC curve",
ifelse(nonpara, "loess r-squared", "Linear model"))
}
if(scale) {
if(class(object$finalModel)[1] == "pamrtrained") imp <- abs(imp)
imp <- imp - min(imp, na.rm = TRUE)
imp <- imp/max(imp, na.rm = TRUE)*100
}
out <- list(importance = imp,
model = modelName,
calledFrom = "varImp")
structure(out, class = "varImp.train")
}
|
