broom (version 1.0.4)

tidy.cv.glmnet: Tidy a(n) cv.glmnet object

Description

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.

Usage

# S3 method for cv.glmnet
tidy(x, ...)

Value

A tibble::tibble() with columns:

lambda

Value of penalty parameter lambda.

nzero

Number of non-zero coefficients for the given lambda.

std.error

The standard error of the regression term.

conf.low

lower bound on confidence interval for cross-validation estimated loss.

conf.high

upper bound on confidence interval for cross-validation estimated loss.

estimate

Median loss across all cross-validation folds for a given lamdba

Arguments

x

A cv.glmnet object returned from glmnet::cv.glmnet().

...

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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

See Also

tidy(), glmnet::cv.glmnet()

Other glmnet tidiers: glance.cv.glmnet(), glance.glmnet(), tidy.glmnet()

Examples

if (FALSE) { # rlang::is_installed(c("glmnet", "ggplot2"))

# load libraries for models and data
library(glmnet)

set.seed(27)

nobs <- 100
nvar <- 50
real <- 5

x <- matrix(rnorm(nobs * nvar), nobs, nvar)
beta <- c(rnorm(real, 0, 1), rep(0, nvar - real))
y <- c(t(beta) %*% t(x)) + rnorm(nvar, sd = 3)

cvfit1 <- cv.glmnet(x, y)

tidy(cvfit1)
glance(cvfit1)

library(ggplot2)

tidied_cv <- tidy(cvfit1)
glance_cv <- glance(cvfit1)

# plot of MSE as a function of lambda
g <- ggplot(tidied_cv, aes(lambda, estimate)) +
  geom_line() +
  scale_x_log10()
g

# plot of MSE as a function of lambda with confidence ribbon
g <- g + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)
g

# plot of MSE as a function of lambda with confidence ribbon and choices
# of minimum lambda marked
g <- g +
  geom_vline(xintercept = glance_cv$lambda.min) +
  geom_vline(xintercept = glance_cv$lambda.1se, lty = 2)
g

# plot of number of zeros for each choice of lambda
ggplot(tidied_cv, aes(lambda, nzero)) +
  geom_line() +
  scale_x_log10()

# coefficient plot with min lambda shown
tidied <- tidy(cvfit1$glmnet.fit)

ggplot(tidied, aes(lambda, estimate, group = term)) +
  scale_x_log10() +
  geom_line() +
  geom_vline(xintercept = glance_cv$lambda.min) +
  geom_vline(xintercept = glance_cv$lambda.1se, lty = 2)
  
}