# gen.ridge

0th

Percentile

##### 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.

laplacian