fdars (version 0.3.3)

fregre.np.cv: Cross-Validation for Nonparametric Functional Regression

Description

Performs k-fold cross-validation to select the optimal bandwidth parameter (h) for nonparametric functional regression.

Usage

fregre.np.cv(
  fdataobj,
  y,
  kfold = 10,
  h.range = NULL,
  metric = metric.lp,
  seed = NULL,
  ...
)

Value

A list with components:

optimal.h

Optimal bandwidth parameter

cv.errors

Mean squared prediction error for each h

cv.se

Standard error of cv.errors

model

Fitted model with optimal h

Arguments

fdataobj

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

y

Response vector.

kfold

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

h.range

Range of bandwidth values to try. If NULL, automatically determined from the distance matrix.

metric

Distance metric function. Default is metric.lp.

seed

Random seed for fold assignment.

...

Additional arguments passed to the metric function.