cvrisk.FDboostLSS function

Cross-validation for FDboostLSS

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.

## S3 method for class 'FDboostLSS'
cvrisk(
  object,
  folds = cvLong(id = object[[1]]$id, weights = model.weights(object[[1]])),
  grid = NULL,
  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: defaults to a grid up to the current number of boosting iterations. The default generates the grid according to the defaults of cvrisk.mboostLSS which are different for models with cyclic or noncyclic fitting.
• 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.

Returns

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.

Details

The function cvrisk.FDboostLSS is a wrapper for cvrisk.mboostLSS in package gamboostLSS. It overrides 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).

See Also

cvrisk.mboostLSS in package gamboostLSS.