# --------------------------------------
# Author: Andreas Alfons
# Erasmus Universiteit Rotterdam
# --------------------------------------
#' Resampling-based prediction error for fitted models
#'
#' Estimate the prediction error of a fitted model via (repeated) \eqn{K}-fold
#' cross-validation, (repeated) random splitting (also known as random
#' subsampling or Monte Carlo cross-validation), or the bootstrap. Methods are
#' available for least squares fits computed with \code{\link[stats]{lm}} as
#' well as for the following robust alternatives: MM-type models computed with
#' \code{\link[robustbase]{lmrob}} and least trimmed squares fits computed with
#' \code{\link[robustbase]{ltsReg}}.
#'
#' @name perry-methods
#'
#' @param object the fitted model for which to estimate the prediction error.
#' @param splits an object of class \code{"cvFolds"} (as returned by
#' \code{\link[perry]{cvFolds}}) or a control object of class
#' \code{"foldControl"} (see \code{\link[perry]{foldControl}}) defining the
#' folds of the data for (repeated) \eqn{K}-fold cross-validation, an object of
#' class \code{"randomSplits"} (as returned by
#' \code{\link[perry]{randomSplits}}) or a control object of class
#' \code{"splitControl"} (see \code{\link[perry]{splitControl}}) defining
#' random data splits, or an object of class \code{"bootSamples"} (as returned
#' by \code{\link[perry]{bootSamples}}) or a control object of class
#' \code{"bootControl"} (see \code{\link[perry]{bootControl}}) defining
#' bootstrap samples.
#' @param fit a character string specifying for which fit to estimate the
#' prediction error. Possible values are \code{"reweighted"} (the default) for
#' the prediction error of the reweighted fit, \code{"raw"} for the prediction
#' error of the raw fit, or \code{"both"} for the prediction error of both
#' fits.
#' @param cost a cost function measuring prediction loss. It should expect
#' the observed values of the response to be passed as the first argument and
#' the predicted values as the second argument, and must return either a
#' non-negative scalar value, or a list with the first component containing
#' the prediction error and the second component containing the standard
#' error. The default is to use the root mean squared prediction error
#' for the \code{"lm"} method and the root trimmed mean squared prediction
#' error for the \code{"lmrob"} and \code{"lts"} methods (see
#' \code{\link[perry]{cost}}).
#' @param ncores a positive integer giving the number of processor cores to be
#' used for parallel computing (the default is 1 for no parallelization). If
#' this is set to \code{NA}, all available processor cores are used.
#' @param cl a \pkg{parallel} cluster for parallel computing as generated by
#' \code{\link[parallel]{makeCluster}}. If supplied, this is preferred over
#' \code{ncores}.
#' @param seed optional initial seed for the random number generator (see
#' \code{\link{.Random.seed}}). Note that also in case of parallel computing,
#' resampling is performed on the manager process rather than the worker
#' processes. On the parallel worker processes, random number streams are
#' used and the seed is set via \code{\link{clusterSetRNGStream}}.
#' @param \dots additional arguments to be passed to the prediction loss
#' function \code{cost}.
#'
#' @return An object of class \code{"perry"} with the following components:
#' \describe{
#' \item{\code{pe}}{a numeric vector containing the estimated prediction
#' errors. For the \code{"lm"} and \code{"lmrob"} methods, this is a single
#' numeric value. For the \code{"lts"} method, this contains one value for
#' each of the requested fits. In case of more than one replication, those
#' are average values over all replications.}
#' \item{\code{se}}{a numeric vector containing the estimated standard
#' errors of the prediction loss. For the \code{"lm"} and \code{"lmrob"}
#' methods, this is a single numeric value. For the \code{"lts"} method,
#' this contains one value for each of the requested fits.}
#' \item{\code{reps}}{a numeric matrix containing the estimated prediction
#' errors from all replications. For the \code{"lm"} and \code{"lmrob"}
#' methods, this is a matrix with one column. For the \code{"lts"} method,
#' this contains one column for each of the requested fits. However, this
#' is only returned in case of more than one replication.}
#' \item{\code{splits}}{an object giving the data splits used to estimate the
#' prediction error.}
#' \item{\code{y}}{the response.}
#' \item{\code{yHat}}{a list containing the predicted values from all
#' replications.}
#' \item{\code{call}}{the matched function call.}
#' }
#'
#' @note The \code{perry} methods extract the data from the fitted model and
#' call \code{\link[perry]{perryFit}} to perform resampling-based prediction
#' error estimation.
#'
#' @author Andreas Alfons
#'
#' @seealso \code{\link[perry]{perryFit}}
#'
#' @example inst/doc/examples/example-perry.R
#'
#' @keywords utilities
#'
#' @import perry
NULL
## LS regression
#' @rdname perry-methods
#' @method perry lm
#' @export
#' @import stats
perry.lm <- function(object, splits = foldControl(), cost = rmspe,
ncores = 1, cl = NULL, seed = NULL, ...) {
## initializations
matchedCall <- match.call()
# retrieve data from model fit
if(is.null(data <- object$model)) {
haveDataArgument <- !is.null(object$call$data)
if(haveDataArgument) {
# try to retrieve data from 'x' and 'y' components
# this only works if the data argument was used to fit the model
if(!is.null(x <- object[["x"]]) && !is.null(y <- object$y)) {
x <- removeIntercept(x)
data <- data.frame(y, x)
}
}
if(!haveDataArgument || is.null(data)) {
# try to retrieve data from terms component
data <- try(model.frame(object$terms), silent=TRUE)
if(inherits(data, "try-error")) stop("model data not available")
}
}
if(is.null(y <- object$y)) y <- model.response(data)
## call function perryFit() to estimate the prediction error
out <- perryFit(object, data=data, y=y, splits=splits, cost=cost,
costArgs=list(...), envir=parent.frame(), ncores=ncores,
cl=cl, seed=seed)
out$call <- matchedCall
out
}
## MM and SDMD regression
#' @rdname perry-methods
#' @method perry lmrob
#' @export
#' @import robustbase
perry.lmrob <- function(object, splits = foldControl(), cost = rtmspe,
ncores = 1, cl = NULL, seed = NULL, ...) {
## initializations
matchedCall <- match.call()
# retrieve data from model fit
if(is.null(data <- object$model)) {
haveDataArgument <- !is.null(object$call$data)
if(haveDataArgument) {
# try to retrieve data from 'x' and 'y' components
# this only works if the data argument was used to fit the model
if(!is.null(x <- object[["x"]]) && !is.null(y <- object$y)) {
x <- removeIntercept(x)
data <- data.frame(y, x)
}
}
if(!haveDataArgument || is.null(data)) {
# try to retrieve data from terms component
data <- try(model.frame(object$terms), silent=TRUE)
if(inherits(data, "try-error")) stop("model data not available")
}
}
if(is.null(y <- object$y)) y <- model.response(data)
## call function perryFit() to estimate the prediction error
out <- perryFit(object, data=data, y=y, splits=splits, cost=cost,
costArgs=list(...), envir=parent.frame(), ncores=ncores,
cl=cl, seed=seed)
out$call <- matchedCall
out
}
## LTS regression
#' @rdname perry-methods
#' @method perry lts
#' @export
#' @import robustbase
perry.lts <- function(object, splits = foldControl(),
fit = c("reweighted", "raw", "both"), cost = rtmspe,
ncores = 1, cl = NULL, seed = NULL, ...) {
## initializations
matchedCall <- match.call()
object <- object
if(is.null(x <- object$X) || is.null(y <- object$Y)) {
if(is.null(data <- object$model)) {
if(is.null(x)) x <- try(model.matrix(object$terms), silent=TRUE)
if(is.null(y)) y <- try(model.response(object$terms), silent=TRUE)
if(inherits(x, "try-error") || inherits(y, "try-error")) {
stop("model data not available")
}
} else {
x <- model.matrix(object$terms, data)
y <- model.response(data)
}
}
# predictor matrix is stored with column for intercept (if any)
x <- removeIntercept(x)
## prepare cross-validation
# extract function call for model fit
call <- object$call
call[[1]] <- as.name("ltsReg")
# if the model was fitted with formula method, 'formula' and 'data'
# arguments are removed from call and 'x' and 'y' are used instead
call$formula <- NULL
call$data <- NULL
call$intercept <- object$intercept
## call function perryFit() to estimate the prediction error
out <- perryFit(call, x=x, y=y, splits=splits, predictArgs=list(fit=fit),
cost=cost, costArgs=list(...), envir=parent.frame(),
ncores=ncores, cl=cl, seed=seed)
out$call <- matchedCall
out
}
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