Nothing

```
# ----------------------
# Author: Andreas Alfons
# KU Leuven
# ----------------------
#' Reshape cross-validation results
#'
#' Reshape cross-validation results into an object of class \code{"cvSelect"}
#' with only one column of results.
#'
#' @aliases cvReshape.cv cvReshape.cvSelect
#'
#' @param x an object inheriting from class \code{"cv"} or \code{"cvSelect"}
#' that contains cross-validation results.
#' @param selectBest a character string specifying a criterion for selecting
#' the best model. Possible values are \code{"min"} (the default) or
#' \code{"hastie"}. The former selects the model with the smallest prediction
#' error. The latter is useful for nested models or for models with a tuning
#' parameter controlling the complexity of the model (e.g., penalized
#' regression). It selects the most parsimonious model whose prediction error
#' is no larger than \code{seFactor} standard errors above the prediction error
#' of the best overall model. Note that the models are thereby assumed to be
#' ordered from the most parsimonious one to the most complex one. In
#' particular a one-standard-error rule is frequently applied.
#' @param seFactor a numeric value giving a multiplication factor of the
#' standard error for the selection of the best model. This is ignored if
#' \code{selectBest} is \code{"min"}.
#' @param \dots additional arguments to be passed down.
#'
#' @return An object of class \code{"cvSelect"} with the following components:
#' \item{n}{an integer giving the number of observations.}
#' \item{K}{an integer giving the number of folds used in
#' cross-validation.}
#' \item{R}{an integer giving the number of replications used in
#' cross-validation.}
#' \item{best}{an integer giving the index of the model with the best
#' prediction performance.}
#' \item{cv}{a data frame containing the estimated prediction errors for
#' the models. For repeated cross-validation, those are average values over
#' all replications.}
#' \item{se}{a data frame containing the estimated standard errors of the
#' prediction loss for the models.}
#' \item{selectBest}{a character string specifying the criterion used for
#' selecting the best model.}
#' \item{seFactor}{a numeric value giving the multiplication factor of
#' the standard error used for the selection of the best model.}
#' \item{reps}{a data frame containing the estimated prediction errors
#' for the models from all replications. This is only returned if repeated
#' cross-validation was performed.}
#'
#' @author Andreas Alfons
#'
#' @references
#' Hastie, T., Tibshirani, R. and Friedman, J. (2009) \emph{The Elements of
#' Statistical Learning: Data Mining, Inference, and Prediction}. Springer,
#' 2nd edition.
#'
#' @seealso \code{\link{cvFit}}, \code{\link{cvSelect}}, \code{\link{cvTuning}}
#'
#' @example inst/doc/examples/example-cvReshape.R
#'
#' @keywords utilities
#'
#' @export
cvReshape <- function(x, ...) UseMethod("cvReshape")
#' @rdname cvReshape
#' @method cvReshape cv
#' @export
cvReshape.cv <- function(x,
selectBest = c("min", "hastie"), seFactor = 1, ...) {
# initializations
if(ncv(x) == 0 || isTRUE(nfits(x) == 0)) stop("empty object")
cvNames <- cvNames(x)
# create list of objects with one column
cvName <- defaultCvNames(1)
objects <- lapply(cvNames,
function(s) {
xs <- subset(x, select=s)
cvNames(xs) <- cvName
xs
})
# substitute "CV" in default names by "Fit"
if(identical(cvNames, defaultCvNames(length(cvNames)))) {
fitName <- defaultFitNames(1)
cvNames <- gsub(cvName, fitName, cvNames, fixed=TRUE)
}
# call cvSelect() to combine the model fits
names(objects) <- cvNames
objects$.selectBest <- selectBest
objects$.seFactor <- seFactor
out <- do.call(cvSelect, objects)
if(length(out$K) > 1) out$K <- out$K[1]
if(length(out$R) > 1) out$R <- out$R[1]
out
}
#' @rdname cvReshape
#' @method cvReshape cvSelect
#' @export
cvReshape.cvSelect <- cvReshape.cv
```

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