How to perform lm.ridge summary?

Question

I wonder is there a way to output summary for ridge regression in R? It is a result of lm.ridge{MASS} function.

For standard linear model you just do summary(lm_model) but what about ridge regression model? Thanks for help.

Answer 1

I just added a method that summarizes (or more precisely, tidies) "ridgelm" objects to my broom package. This takes the form of two S3 generics: tidy and glance. You can install it with devtools::install_github("dgrtwo/broom") (though you'll need to install devtools first).

As an example, let's set up a ridge regression:

library(MASS)
names(longley)[1] <- "y"
fit <- lm.ridge(y ~ ., longley, lambda = seq(0.001, .05, .001))

The tidy function provides a data frame that shows each combination of lambda and the estimated term:

library(broom)
td <- tidy(fit)
head(td)
##   lambda    GCV term estimate
## 1  0.001 0.1240  GNP    23.02
## 2  0.002 0.1217  GNP    21.27
## 3  0.003 0.1205  GNP    19.88
## 4  0.004 0.1199  GNP    18.75
## 5  0.005 0.1196  GNP    17.80
## 6  0.006 0.1196  GNP    16.99

While the glance function creates a one-row summary, particularly the choices of lambda by various methods:

g <- glance(fit)
g
##       kHKB     kLW lambdaGCV
## 1 0.006837 0.05267     0.006

This is useful because it makes it easy to plot and explore the data yourself rather than relying on MASS's plotters:

library(ggplot2)
ggplot(td, aes(lambda, estimate, color = term)) + geom_line()

# plot of GCV versus lambda
ggplot(td, aes(lambda, GCV)) + geom_line() +
    geom_vline(xintercept = g$lambdaGCV, col = "red", lty = 2)

For more on these methods, see ?ridgelm_tidiers, or see the package's vignettes for more about the tidy and glance methods in general.

Answer 2

There is no summary method for the ridgelm class:

> methods(class = 'ridgelm')
[1] coef.ridgelm*   plot.ridgelm*   print.ridgelm*  select.ridgelm*

What should this summary return? You can extract all the information you need from the ridgelm-object.

However, you also could write your own summary methods for your purposes (check the code for summary.lm() for a start). If you're happy with it, you could send it to the maintainers of MASS.

Answer 3

you can use my lmridge package from CRAN.