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
Examples
# \donttest{
fd <- fdata(matrix(rnorm(500), nrow = 50), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
cv_result <- fregre.basis.cv(fd, y, kfold = 5,
lambda.range = 10^seq(-2, 2, length.out = 10))
cv_result$optimal.lambda
#> [1] 100
# }