cvrisk.FDboostLSS: Cross-validation for FDboostLSS

In FDboost: Boosting Functional Regression Models

Description

Multidimensional cross-validated estimation of the empirical risk for hyper-parameter selection, for an object of class FDboostLSS setting the folds per default to resampling curves.

Usage

## S3 method for class 'FDboostLSS'
cvrisk(object, folds = cvLong(id = object[[1]]$id,
  weights = model.weights(object[[1]])), grid = make.grid(mstop(object)),
  papply = mclapply, trace = TRUE, fun = NULL, ...)

Arguments

object	an object of class FDboostLSS.
folds	a weight matrix a weight matrix with number of rows equal to the number of observations. The number of columns corresponds to the number of cross-validation runs, defaults to 25 bootstrap samples, resampling whole curves
grid	a matrix of stopping parameters the empirical risk is to be evaluated for. Each row represents a parameter combination. The number of columns must be equal to the number of parameters of the GAMLSS family. Per default, make.grid(mstop(object)) is used.
papply	(parallel) apply function, defaults to mclapply, see cvrisk.mboostLSS for details
trace	print status information during cross-validation? Defaults to TRUE.
fun	if fun is NULL, the out-of-sample risk is returned. fun, as a function of object, may extract any other characteristic of the cross-validated models. These are returned as is.
...	additional arguments passed to mclapply.

Details

The function cvrisk.FDboostLSS is a wrapper for cvrisk.mboostLSS in package gamboostLSS. It overrieds the default for the folds, so that the folds are sampled on the level of curves (not on the level of single observations, which does not make sense for functional response).

Value

An object of class cvriskLSS (when fun was not specified), basically a matrix containing estimates of the empirical risk for a varying number of bootstrap iterations. plot and print methods are available as well as an mstop method, see cvrisk.mboostLSS.

See Also

cvrisk.mboostLSS in packge gamboostLSS.

FDboost documentation built on May 2, 2019, 6:48 p.m.