broom (version 0.4.2)

ridgelm_tidiers: Tidying methods for ridgelm objects from the MASS package

Description

These methods tidies the coefficients of a ridge regression model chosen at each value of lambda into a data frame, or constructs a one-row glance of the model's choices of lambda (the ridge constant)

Usage

# S3 method for ridgelm
tidy(x, ...)
# S3 method for ridgelm
glance(x, ...)

Arguments

x

An object of class "ridgelm"

...

extra arguments (not used)

Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

tidy.ridgelm returns one row for each combination of choice of lambda and term in the formula, with columns:

lambda

choice of lambda

GCV

generalized cross validation value for this lambda

term

the term in the ridge regression model being estimated

estimate

estimate of scaled coefficient using this lambda

scale

Scaling factor of estimated coefficient

glance.ridgelm returns a one-row data.frame with the columns

kHKB

modified HKB estimate of the ridge constant

kLW

modified L-W estimate of the ridge constant

lambdaGCV

choice of lambda that minimizes GCV

This is similar to the output of select.ridgelm, but it is returned rather than printed.

Examples

# NOT RUN {
names(longley)[1] <- "y"
fit1 <- MASS::lm.ridge(y ~ ., longley)
tidy(fit1)

fit2 <- MASS::lm.ridge(y ~ ., longley, lambda = seq(0.001, .05, .001))
td2 <- tidy(fit2)
g2 <- glance(fit2)

# coefficient plot
library(ggplot2)
ggplot(td2, aes(lambda, estimate, color = term)) + geom_line()

# GCV plot
ggplot(td2, aes(lambda, GCV)) + geom_line()

# add line for the GCV minimizing estimate
ggplot(td2, aes(lambda, GCV)) + geom_line() +
    geom_vline(xintercept = g2$lambdaGCV, col = "red", lty = 2)
# }

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