# cv.glmnet

0th

Percentile

##### Cross-validation for glmnet

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

Keywords
models, regression
##### 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
##### 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.
• lambdathe values of lambda used in the fits.
• cvmThe mean cross-validated error - a vector of length length(lambda).
• cvsdestimate of standard error of svm.
• cvupupper curve = cvm+cvsd.
• cvlolower curve = cvm-cvsd.
• nzeronumber of non-zero coefficients at each lambda.
• namea text string indicating type of measure (for plotting purposes).
• lambda.minvalue of lambda that gives minimum cvm.
• lambda.1selargest value of lambda such that error is within 1 standard error of the minimum.

##### 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

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

• 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 1.1-5, License: GPL-2

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