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 ridgelm
tidy(x, ...)

Arguments

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.

See also

tidy(), MASS::lm.ridge()

Other ridgelm tidiers: glance.ridgelm

Value

A tibble::tibble() with columns:

GCV

generalized cross validation value for this lambda

lambda

Value of penalty parameter lambda.

term

The name of the regression term.

estimate

estimate of scaled coefficient using this lambda

scale

Scaling factor of estimated coefficient

Examples

names(longley)[1] <- "y" fit1 <- MASS::lm.ridge(y ~ ., longley) tidy(fit1)
#> # A tibble: 6 x 5 #> lambda term estimate scale xm #> <dbl> <chr> <dbl> <dbl> <dbl> #> 1 0 GNP 25.4 96.2 388. #> 2 0 Unemployed 3.30 90.5 319. #> 3 0 Armed.Forces 0.752 67.4 261. #> 4 0 Population -11.7 6.74 117. #> 5 0 Year -6.54 4.61 1954. #> 6 0 Employed 0.786 3.40 65.3
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)