plot.cv.glmnet {glmnet} | R Documentation |
Plots the cross-validation curve, and upper and lower standard deviation
curves, as a function of the lambda
values used.
## S3 method for class 'cv.glmnet' plot(x, sign.lambda, ...)
x |
fitted |
sign.lambda |
Either plot against |
... |
Other graphical parameters to plot |
A plot is produced, and nothing is returned.
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie <hastie@stanford.edu>
Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent
glmnet
and cv.glmnet
.
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)