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, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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
if (requireNamespace("MASS", quietly = TRUE)) { 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) }