broom (version 0.5.6)

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 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.

Usage

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

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. 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 value of the penalization parameter lambda in x and columns:

lambda

Value of the penalty parameter lambda.

estimate

Median loss across all cross-validation folds for a given lambda.

std.error

Standard error of the cross-validation estimated loss.

conf.low

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

conf.high

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

nzero

Number of coefficients that are exactly zero for given lambda

.

See Also

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

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

Examples

# NOT RUN {
if (requireNamespace("glmnet", quietly = TRUE)) {

    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)
}

# }