plot.cv.glmnet

0th

Percentile

plot the cross-validation curve produced by cv.glmnet

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

Keywords
models, regression
Usage
# S3 method for cv.glmnet
plot(x, sign.lambda = 1, ...)

# S3 method for 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.

References

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

See Also

glmnet and cv.glmnet.

Aliases
  • plot.cv.glmnet
  • plot.cv.relaxed
Examples
# NOT RUN {
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)

# }
Documentation reproduced from package glmnet, version 3.0-2, License: GPL-2

Community examples

Looks like there are no examples yet.