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.

Keywords
models, regression
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.

Aliases
  • plot.cv.glmnet
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)
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 2.0-10, License: GPL-2

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