Cross-Validation for Functional Basis Regression — fregre.basis.cv • fdars

Performs k-fold cross-validation to select the optimal regularization parameter (lambda) for functional basis regression.

Usage

fregre.basis.cv(fdataobj, y, kfold = 10, lambda.range = NULL, seed = NULL, ...)

Arguments

fdataobj: An object of class 'fdata' (functional covariate).
y: Response vector.
kfold: Number of folds for cross-validation (default 10).
lambda.range: Range of lambda values to try. Default is 10^seq(-4, 4, length.out = 20).
seed: Random seed for fold assignment.
...: Additional arguments passed to fregre.basis.

Value

A list with components:

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