glinternet.cv: Cross-validation for glinternet

In glinternet: Learning Interactions via Hierarchical Group-Lasso Regularization

Description

Does k-fold cross validation for glinternet and returns a value of lambda.

Usage

glinternet.cv(X, Y, numLevels, nFolds = 10, lambda=NULL, nLambda=50,
lambdaMinRatio=0.01, interactionCandidates=NULL, interactionPairs=NULL,
screenLimit=NULL, family=c("gaussian", "binomial"), tol=1e-5, maxIter=5000,
verbose=FALSE, numCores=1)

Arguments

X	X matrix as in glinternet.
Y	Target Y as in glinternet.
numLevels	Number of levels numLevels as in glinternet.
nFolds	Number of folds - default is 10.
lambda	lambda as in glinternet.
nLambda	nLambda as in glinternet.
lambdaMinRatio	lambdaMinRatio as in glinternet.
interactionCandidates	interactionCandidates as in glinternet.
interactionPairs	interactionPairs as in glinternet.
screenLimit	screenLimit as in glinternet.
family	family as in glinternet.
tol	tol as in glinternet.
maxIter	maxIter as in glinternet.
verbose	verbose as in glinternet.
numCores	numCores as in glinternet.

Details

The lambda sequence is computed using all the data. nFolds models are fit, each time with one of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. The lambda value that minimizes the average error is returned, and a model with this lambda is fit to the full data set.

Value

An object of class glinternet.cv with the components

call	The user function call.
glinternetFit	Glinternet object fitted on the full data using a lambda sequence that terminates at lambdaHat.
fitted	Vector for fitted values (same length as Y). This is from the model fitted at lambdaHat.
activeSet	activeSet is a list variables found for the model fitted with lambdaHat.
betahat	Unstandardized coefficients for the variables in activeSet.
lambda	The actual sequence of lambda values used for the cross validation.
lambdaHat	The value of lambda that minimizes the cv error curve.
lambdaHat1Std	The largest value of lambda that produces a cv error that is within 1 standard deviation of the minimum cv error. This will always be at least as large as lambdaHat.
cvErr	The vector of cross validation errors. Same length as lambda.
cvErrStd	Standard deviation for cv errors across the nFolds folds.
family	The response type.
numLevels	Input number of levels for each variable.
nFolds	The number of folds used.

Author(s)

Michael Lim and Trevor Hastie
Maintainer: Michael Lim [e-mail]

See Also

glinternet, predict.glinternet, predict.glinternet.cv, plot.glinternet.cv

Examples

Y = rnorm(100)
numLevels = sample(1:5, 10, replace=TRUE)
X = sapply(numLevels, function(x) if (x==1)
rnorm(100) else sample(0:(x-1), 100, replace=TRUE))
fit = glinternet.cv(X, Y, numLevels, nFolds=3)

Example output

Loaded glinternet 1.0.5

glinternet documentation built on Sept. 5, 2021, 5:28 p.m.