plot.cv.glmnet

0th

Percentile

plot the cross-validation curve produced by cv.glmnet

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

Keywords
models, regression
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.

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
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 1.8-5, License: GPL-2

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