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

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)
  
 }