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

In jeffwong/glmnet: Lasso and elastic-net regularized generalized linear models

Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the lambda values used.

Usage

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

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

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)
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)

jeffwong/glmnet documentation built on May 19, 2019, 4:02 a.m.