Uses the Rust backend to select the optimal number of FPC components
via k-fold cross-validation for the functional linear model.
Usage
fregre.lm.cv(fdataobj, y, scalar.covariates = NULL, k.range = NULL, nfold = 10)
Arguments
- fdataobj
An object of class 'fdata'.
- y
Response vector.
- scalar.covariates
Optional matrix of scalar covariates.
- k.range
Range of FPC component counts to try.
- nfold
Number of CV folds (default 10).
Value
A list with optimal.k, cv.errors, and model.
Examples
# \donttest{
fd <- fdata(matrix(rnorm(500), nrow = 50), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
cv_result <- fregre.lm.cv(fd, y, k.range = 1:5, nfold = 5)
cv_result$optimal.k
#> [1] 2
# }