# gen.ridge: Penalized Regression

## Description

Perform a penalized regression, as used in penalized discriminant
analysis.

## Usage

gen.ridge(x, y, weights, lambda=1, omega, df, …)

## Arguments

x, y, weights

the x and y matrix and possibly a weight vector.

lambda

the shrinkage penalty coefficient.

omega

a penalty object; omega is the eigendecomposition of
the penalty matrix, and need not have full rank. By default,
standard ridge is used.

df

an alternative way to prescribe lambda, using the notion
of equivalent degrees of freedom.

## Value

A generalized ridge regression, where the coefficients are penalized
according to omega. See the function definition for further details.
No functions are provided for producing one dimensional penalty
objects (omega).
`laplacian()`

creates a two-dimensional penalty
object, suitable for (small) images.