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 modelling function. This includes the name of the modelling function or any arguments passed to the modelling 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.

# S3 method for cv.glmnet
glance(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.

See also

Value

A tibble::tibble() with exactly one row and columns:

lambda.1se

The value of the penalization parameter lambda that results in the sparsest model while remaining within one standard error of the minimum loss.

lambda.min

The value of the penalization parameter lambda that achieved minimum loss as estimated by cross validation.

Examples

library(glmnet)
#> Loading required package: foreach
#> Loaded glmnet 2.0-16
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)
#> # A tibble: 73 x 6 #> lambda estimate std.error conf.low conf.high nzero #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 1.45 17.3 1.96 15.3 19.3 0 #> 2 1.32 17.2 1.98 15.2 19.1 1 #> 3 1.20 16.9 1.95 15.0 18.9 1 #> 4 1.09 16.7 1.93 14.8 18.6 1 #> 5 0.997 16.7 1.92 14.8 18.6 1 #> 6 0.909 16.8 1.91 14.9 18.7 2 #> 7 0.828 16.9 1.92 15.0 18.8 3 #> 8 0.754 17.0 1.94 15.1 18.9 5 #> 9 0.687 17.1 1.96 15.2 19.1 7 #> 10 0.626 17.3 1.98 15.3 19.3 7 #> # ... with 63 more rows
glance(cvfit1)
#> # A tibble: 1 x 2 #> lambda.min lambda.1se #> <dbl> <dbl> #> 1 0.997 1.45
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)