glmnet (version 2.0-9)

plot.cv.glmnet: 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.

Usage

# S3 method for 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.

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

Run this code
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)

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