Axing an cv.glmnet.

Source: R/cv.R

cv.glmnet objects are created from carrying out k-fold cross-validation from the glmnet package.

# S3 method for cv.glmnet
axe_fitted(x, verbose = FALSE, ...)

Arguments

x

A model object.
verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.
...

Any additional arguments related to axing.

Value

Axed cv.glmnet object.

Examples

# Load libraries
suppressWarnings(suppressMessages(library(glmnet)))

# Example 1
n <- 500
p <- 30
nzc <- trunc(p/10)
x <- matrix(rnorm(n*p), n, p)
beta <- matrix(rnorm(30), 10, nzc)
beta <- rbind(beta, matrix(0, p-10, nzc))
f <- x %*% beta
p <- exp(f)
p <- p/apply(p, 1, sum)
g <- rmult(p)
set.seed(10101)
cvfit <- cv.glmnet(x, g, family="multinomial", keep = TRUE)

out <- butcher(cvfit, verbose = TRUE)
#> ✔ Memory released: '864,040 B'

# Example 2
n <- 1000
p <- 100
nzc <- trunc(p/10)
x <- matrix(rnorm(n*p), n, p)
beta <- rnorm(nzc)
fx <- x[, seq(nzc)] %*% beta
eps <- rnorm(n)*5
y <- drop(fx+eps)
px <- exp(fx)
px <- px/(1+px)
ly <- rbinom(n = length(px), prob = px, size = 1)
cvfit2 <- cv.glmnet(x, ly,
                    family = "binomial",
                    type.measure = "auc",
                    keep = TRUE)

out <- butcher(cvfit2, verbose = TRUE)
#> ✔ Memory released: '576,080 B'