Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for ridgelm
tidy(x, ...)
A ridgelm
object returned from MASS::lm.ridge()
.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
Other ridgelm tidiers:
glance.ridgelm()
A tibble::tibble()
with columns:
Generalized cross validation error estimate.
Value of penalty parameter lambda.
The name of the regression term.
estimate of scaled coefficient using this lambda
Scaling factor of estimated coefficient
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("MASS", quietly = TRUE)) {
# load libraries for models and data
library(MASS)
names(longley)[1] <- "y"
# fit model and summarizd results
fit1 <- lm.ridge(y ~ ., longley)
tidy(fit1)
fit2 <- 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)
}