Cross-Validation for Nonparametric Functional Regression — fregre.np.cv • fdars

Performs k-fold cross-validation to select the optimal bandwidth parameter (h) for nonparametric functional regression.

Usage

fregre.np.cv(
  fdataobj,
  y,
  kfold = 10,
  h.range = NULL,
  metric = metric.lp,
  seed = NULL,
  ...
)

Arguments

fdataobj: An object of class 'fdata' (functional covariate).
y: Response vector.
kfold: Number of folds for cross-validation (default 10).
h.range: Range of bandwidth values to try. If NULL, automatically determined from the distance matrix.
metric: Distance metric function. Default is metric.lp.
seed: Random seed for fold assignment.
...: Additional arguments passed to the metric function.

Value

A list with components:

optimal.h: Optimal bandwidth parameter
cv.errors: Mean squared prediction error for each h
cv.se: Standard error of cv.errors
model: Fitted model with optimal h