glmnet (version 1.1-5)

cv.glmnet: 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

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.

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.

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)