cv.clime {clime}

k-fold cross validation for clime object

Description

Perform a k-fold cross validation for selecting lambda

Usage

cv.clime(clime.obj, loss=c("likelihood", "tracel2"), fold=5) 

Arguments

clime.obj

clime object output from clime. Note that this requires that the input to clime is x instead of the sample covariance matrix.

loss

loss to be used in cross validation. Currently, two losses are available: "likelihood" and "tracel2". Default "likelihood".

fold

number of folds used in cross validation. Default 5.

Details

Perform a k-fold cross validation for selecting the tuning parameter lambda in clime. Two losses are implemented currently:

\textrm{likelihood: } Tr[\Sigma \Omega] - \log|\Omega| - p

\textrm{tracel2: } Tr[ diag(\Sigma \Omega - I)^2].

Value

An object with S3 class "cv.clime". You can use it as a regular R list with the following fields:

lambdaopt

the lambda selected by cross validation to minimize the loss over the grid values of lambda.

loss

the name of loss used in cross validation.

lambda

sequence of lambda used in the program.

loss.mean

average k-fold loss values for each grid value lambda.

loss.mean

standard deviation of k-fold loss values for each grid value lambda.

lpfun

Linear programming solver used.

Author(s)

T. Tony Cai, Weidong Liu and Xi (Rossi) Luo
Maintainer: Xi (Rossi) Luo [e-mail]

References

Cai, T.T., Liu, W., and Luo, X. (2011). A constrained \ell_1 minimization approach for sparse precision matrix estimation. Journal of the American Statistical Association 106(494): 594-607.

Examples

## trivial example
n <- 50
p <- 5
X <- matrix(rnorm(n*p), nrow=n)
re.clime <- clime(X)
re.cv <- cv.clime(re.clime)
re.clime.opt <- clime(X, re.cv$lambdaopt)