gen.ridge: Penalized Regression

In mda: Mixture and Flexible Discriminant Analysis

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.
...	currently not used.

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.

See Also

laplacian

mda documentation built on July 9, 2023, 7:14 p.m.