fdars (version 0.3.3)

fregre.pc.cv: Cross-Validation for Functional PC Regression

Description

Performs k-fold cross-validation to select the optimal number of principal components for functional PC regression.

Usage

fregre.pc.cv(fdataobj, y, kfold = 10, ncomp.range = NULL, seed = NULL, ...)

Value

A list with components:

optimal.ncomp

Optimal number of components

cv.errors

Mean squared prediction error for each ncomp

cv.se

Standard error of cv.errors

model

Fitted model with optimal ncomp

Arguments

fdataobj

An object of class 'fdata' (functional covariate).

y

Response vector.

kfold

Number of folds for cross-validation (default 10).

ncomp.range

Range of number of components to try. Default is 1 to min(n-1, ncol(data)).

seed

Random seed for fold assignment.

...

Additional arguments passed to fregre.pc.

Examples

# Create functional data with a linear relationship
set.seed(42)
t <- seq(0, 1, length.out = 50)
X <- matrix(0, 100, 50)
for (i in 1:100) X[i, ] <- sin(2*pi*t) * i/100 + rnorm(50, sd = 0.1)
beta_true <- cos(2*pi*t)
y <- X %*% beta_true + rnorm(100, sd = 0.5)
fd <- fdata(X, argvals = t)

# Cross-validate to find optimal number of PCs
cv_result <- fregre.pc.cv(fd, y, ncomp.range = 1:10)