fdars (version 0.3.3)

fdata2basis_cv: Cross-Validation for Basis Function Number Selection

Description

Selects the optimal number of basis functions using k-fold cross-validation or generalized cross-validation.

Usage

fdata2basis_cv(
  fdataobj,
  nbasis.range = 4:20,
  type = c("bspline", "fourier"),
  criterion = c("GCV", "CV", "AIC", "BIC"),
  kfold = 10,
  lambda = 0
)

Value

A list with:

optimal.nbasis

Optimal number of basis functions

scores

Score for each nbasis value

nbasis.range

The tested nbasis values

criterion

The criterion used

fitted

fdata object fitted with optimal nbasis

Arguments

fdataobj

An fdata object.

nbasis.range

Vector of nbasis values to evaluate (default: 4:20).

type

Basis type: "bspline" (default) or "fourier".

criterion

Selection criterion: "GCV" (default), "CV", "AIC", or "BIC".

kfold

Number of folds for k-fold CV (default 10). Ignored if criterion is "GCV", "AIC", or "BIC".

lambda

Smoothing parameter (default 0).

Examples

set.seed(42)
t <- seq(0, 1, length.out = 50)
X <- matrix(0, 20, 50)
for (i in 1:20) X[i, ] <- sin(4 * pi * t) + rnorm(50, sd = 0.1)
fd <- fdata(X, argvals = t)

# Find optimal nbasis
cv_result <- fdata2basis_cv(fd, nbasis.range = 5:15, type = "fourier")
print(cv_result$optimal.nbasis)