broom (version 0.3.4)

glmnet_tidiers: Tidiers for LASSO or elasticnet regularized fits

Description

Tidying methods for regularized fits produced by glmnet, summarizing the estimates across values of the penalty parameter lambda.

Usage

## S3 method for class 'glmnet':
tidy(x, ...)
## S3 method for class 'glmnet':
glance(x, ...)

Arguments

x

a "glmnet" object

...

extra arguments (not used)

Value

• All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

  tidy produces a data.frame with one row per combination of coefficient (including the intercept) and value of lambda, with the columns:

• term
  coefficient name (V1...VN by default, along with "(Intercept)")
• step
  which step of lambda choices was used
• estimate
  estimate of coefficient
• lambda
  value of penalty parameter lambda
• dev.ratio
  fraction of null deviance explained at each value of lambda
• glance returns a one-row data.frame with the values
• nulldev
  null deviance
• npasses
  total passes over the data across all lambda values

Details

Note that while this representation of GLMs is much easier to plot and combine than the default structure, it is also much more memory-intensive. Do not use for extremely large, sparse matrices.

No augment method is yet provided even though the model produces predictions, because the input data is not tidy (it is a matrix that may be very wide) and therefore combining predictions with it is not logical. Furthermore, predictions make sense only with a specific choice of lambda.

Examples

if (require("glmnet", quietly = TRUE)) {
    set.seed(2014)
    x <- matrix(rnorm(100*20),100,20)
    y <- rnorm(100)
    fit1 <- glmnet(x,y)

    head(tidy(fit1))
    glance(fit1)

    library(dplyr)
    library(ggplot2)

    tidied <- tidy(fit1) %>% filter(term != "(Intercept)")

    ggplot(tidied, aes(step, estimate, group = term)) + geom_line()
    ggplot(tidied, aes(lambda, estimate, group = term)) +
        geom_line() + scale_x_log10()

    ggplot(tidied, aes(lambda, dev.ratio)) + geom_line()

    # works for other types of regressions as well, such as logistic
    g2 <- sample(1:2, 100, replace=TRUE)
    fit2 <- glmnet(x, g2, family="binomial")
    head(tidy(fit2))
}