Tidy a(n) ridgelm object

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.

Value

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

See also

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

Other ridgelm tidiers: glance.ridgelm

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)