Perform a penalized regression, as used in penalized discriminant analysis.
gen.ridge(x, y, weights, lambda=1, omega, df, …)
- x, y, weights
the x and y matrix and possibly a weight vector.
the shrinkage penalty coefficient.
a penalty object; omega is the eigendecomposition of the penalty matrix, and need not have full rank. By default, standard ridge is used.
an alternative way to prescribe lambda, using the notion of equivalent degrees of freedom.
currently not used.
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
laplacian() creates a two-dimensional penalty
object, suitable for (small) images.