fit.ridge

Computes ridge regression coefficients and standard errors using the closed-form solution:
\(\hat{\beta} = (X^T X + \lambda I)^{-1} X^T y\)

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

fit.ridge 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></dl>

Arguments

Fit Ridge Regression with Closed-Form Solution — fit.ridge

<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>

</dl>

fit.ridge: Fit Ridge Regression with Closed-Form Solution

Description

Usage

Value

Arguments