pca.ridge: Transform Ridge Estimates to PCA Space
In genridge: Generalized Ridge Trace Plots for Ridge Regression

Description Usage Arguments Value Author(s) References See Also Examples

The function pca.ridge transforms a ridge object from parameter space, where the estimated coefficients are β_k with covariance matrices Σ_k, to the principal component space defined by the right singular vectors, V, of the singular value decomposition of the scaled predictor matrix, X.

In this space, the transformed coefficients are V β_k, with covariance matrices

V Σ_k V^T

This transformation provides alternative views of ridge estimates in low-rank approximations.

1	pca.ridge(x, ...)

`x`	A `ridge` object, as fit by `ridge`
`...`	Other arguments passed down. Not presently used in this implementation.

An object of class c("ridge", "pcaridge"), with the same components as the original ridge object.

Michael Friendly

Friendly, M. (2012). The Generalized Ridge Trace Plot: Visualizing Bias and Precision. In press, Journal of Computational and Graphical Statistics, 21.

ridge

longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])

lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)

plridge <- pca.ridge(lridge)
traceplot(plridge)
pairs(plridge)
# view in space of smallest singular values
plot(plridge, variables=5:6)