optimize.cv.lambda

Searches the lambda range to minimize CV-MSE using Brent's method via `optimize()`.

internal

Because your linear models deserve better than console output.
A sleek color palette and kable styling to make your regression results look sharper than they are.
Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms,
along with a unified interface for model fitting and fabulous LaTeX and console output formatting.
See the package website at
<https://finitesample.space/snazzier>.

Aidan J. Wagner

snazzieR

Chic and Sleek Functions for Beautiful Statisticians

optimize.cv.lambda function

<dl><dt>x</dt>
<dd>A standardized design matrix (n × p)</dd>
<dt>y</dt>
<dd>A centered response vector (n × 1)</dd>
<dt>lambda.range</dt>
<dd>A numeric vector of length 2 specifying the search interval</dd>
<dt>folds</dt>
<dd>Number of cross-validation folds</dd></dl>

Arguments

Optimize Lambda Using Cross-Validation — optimize.cv.lambda

<dl>

<dt>x</dt>
<dd>A standardized design matrix (n × p)</dd>


<dt>y</dt>
<dd>A centered response vector (n × 1)</dd>


<dt>lambda.range</dt>
<dd>A numeric vector of length 2 specifying the search interval</dd>


<dt>folds</dt>
<dd>Number of cross-validation folds</dd>

</dl>

optimize.cv.lambda: Optimize Lambda Using Cross-Validation

Description

Usage

Value

Arguments