fregre.basis.cv

Performs k-fold cross-validation to select the optimal regularization
parameter (lambda) for functional basis regression.

Functional data analysis tools with a high-performance 'Rust' backend.
Provides methods for functional data manipulation, depth computation,
distance metrics, regression, and statistical testing. Supports both
1D functional data (curves) and 2D functional data (surfaces).
Methods are described in Ramsay and Silverman (2005,
ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and
Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data
Analysis".

Dr. Mueller

fdars

Functional Data Analysis in 'Rust'

Simon Müller

fregre.basis.cv function

<dl><dt>fdataobj</dt>
<dd>An object of class 'fdata' (functional covariate).</dd>
<dt>y</dt>
<dd>Response vector.</dd>
<dt>kfold</dt>
<dd>Number of folds for cross-validation (default 10).</dd>
<dt>lambda.range</dt>
<dd>Range of lambda values to try.
Default is 10^seq(-4, 4, length.out = 20).</dd>
<dt>seed</dt>
<dd>Random seed for fold assignment.</dd>
<dt>...</dt>
<dd>Additional arguments passed to fregre.basis.</dd></dl>

Arguments

Cross-Validation for Functional Basis Regression — fregre.basis.cv

<dl>

<dt>fdataobj</dt>
<dd>An object of class 'fdata' (functional covariate).</dd>


<dt>y</dt>
<dd>Response vector.</dd>


<dt>kfold</dt>
<dd>Number of folds for cross-validation (default 10).</dd>


<dt>lambda.range</dt>
<dd>Range of lambda values to try.
Default is 10^seq(-4, 4, length.out = 20).</dd>


<dt>seed</dt>
<dd>Random seed for fold assignment.</dd>


<dt>...</dt>
<dd>Additional arguments passed to fregre.basis.</dd>

</dl>

fregre.basis.cv: Cross-Validation for Functional Basis Regression

Description

Usage

Value

Arguments