tidy.ridgelm {broom} | R Documentation |
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 cross 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 class 'ridgelm' tidy(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with one row for each combination of lambda and a 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 |
Other ridgelm tidiers:
glance.ridgelm()
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)