Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA
.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
# S3 method for cv.glmnet
glance(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.
Other glmnet tidiers:
glance.glmnet()
,
tidy.cv.glmnet()
,
tidy.glmnet()
A tibble::tibble()
with exactly one row and columns:
The value of the penalization parameter lambda that results in the sparsest model while remaining within one standard error of the minimum loss.
The value of the penalization parameter lambda that achieved minimum loss as estimated by cross validation.
Number of observations used.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("glmnet", quietly = TRUE)) {
# 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)
}
#> Loaded glmnet 4.1-4