cv.mse

Computes mean squared error via k-fold cross-validation for a fixed lambda value.

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

cv.mse 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</dt>
<dd>A non-negative regularization scalar</dd>
<dt>folds</dt>
<dd>Number of cross-validation folds (default: 5)</dd></dl>

Arguments

Compute Cross-Validated Mean Squared Error — cv.mse

<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</dt>
<dd>A non-negative regularization scalar</dd>


<dt>folds</dt>
<dd>Number of cross-validation folds (default: 5)</dd>

</dl>

cv.mse: Compute Cross-Validated Mean Squared Error

Description

Usage

Value

Arguments