# gen.ridge

From mda v0.4-10
by Trevor Hastie

##### Penalized Regression

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

- Keywords
- regression

##### 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

*Documentation reproduced from package mda, version 0.4-10, License: GPL-2*

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