cv.glmnet {glmnet}R Documentation

Cross-validation for glmnet

Description

Does k-fold cross-validation for glmnet, produces a plot, and returns a value for lambda

Usage

cv.glmnet(x, y, ..., nfolds, foldid, type)

Arguments

x x matrix as in glmnet.
y response y as in glmnet.
... Other arguments that can be passed to glmnet.
nfolds number of folds - default is 10.
foldid an optional vector of values between 1 and nfold identifying whhat fold each observation is in. If supplied, nfold can be missing.
type loss to use for cross-validation. Currently two options. The default is type="response", which uses squared-error for gaussian models, and deviance for logistic regression. type="class" applies to logistic regression only, and gives misclassification error.

Details

The function runs glmnet nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. This function is a preliminary version, since it does not allow the full range of data-types for glmnet yet.

Value

an object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit.

lambda the values of lambda used in the fits.
cvm The mean cross-validated error - a vector of length length(lambda).
cvsd estimate of standard error of svm.
cvup upper curve = cvm+cvsd.
cvlo lower curve = cvm-cvsd.
nzero number of non-zero coefficients at each lambda.
name a text string indicating type of measure (for plotting purposes).
lambda.min value of lambda that gives minimum cvm.
lambda.1se largest value of lambda such that error is within 1 standard error of the minimum.

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie hastie@stanford.edu

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descenthttp://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

See Also

glmnet and plot method for "cv.glmnet" object.

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)

[Package glmnet version 1.1-5 Index]