plot.cv.glmnet: plot the cross-validation curve produced by cv.glmnet

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

plot the cross-validation curve produced by cv.glmnet

Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the lambda values used. If the object has class "cv.relaxed" a different plot is produced, showing both lambda and gamma

Usage

## S3 method for class 'cv.glmnet'
plot(x, sign.lambda = 1, ...)

## S3 method for class 'cv.relaxed'
plot(x, se.bands = TRUE, ...)

Arguments

x	fitted "cv.glmnet" object
sign.lambda	Either plot against log(lambda) (default) or its negative if sign.lambda=-1.
...	Other graphical parameters to plot
se.bands	Should shading be produced to show standard-error bands; default is TRUE

Details

A plot is produced, and nothing is returned.

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie [e-mail]

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent

See Also

glmnet and cv.glmnet.

Examples

set.seed(1010)
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)
cvob1 = cv.glmnet(x, y)
plot(cvob1)
title("Gaussian Family", line = 2.5)
cvob1r = cv.glmnet(x, y, relax = TRUE)
plot(cvob1r)
frame()
set.seed(1011)
par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1))
cvob2 = cv.glmnet(x, ly, family = "binomial")
plot(cvob2)
title("Binomial Family", line = 2.5)
## set.seed(1011)
## cvob3 = cv.glmnet(x, ly, family = "binomial", type = "class")
## plot(cvob3)
## title("Binomial Family", line = 2.5)

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.