jpen.tune: Tuning parameter selection based on minimization of 5 fold...

In JPEN: Covariance and Inverse Covariance Matrix Estimation Using Joint Penalty

Description

Returns optimal values of tuning parameters lambda and gamma which minimizes the K-fold crossvalidation error on

Usage

jpen.tune(Ytr, gama, lambda=NULL)

Arguments

Ytr	Ytr is matrix of observations.
gama	gama is vector of gamma values. gamma is non-negative.
lambda	lambda is vector of lambda values. lambda is non-negative.

Details

Returns the value of optimal tuning parameters. The function uses K-fold cross validation to select the best tuning parameter from among a set of of values of lambda and gamma.

Value

Returns the optimal values of lambda and gamma.

Author(s)

Ashwini Maurya, Email: [e-mail].

References

A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using Joint Penalty. Submitted. http://arxiv.org/pdf/1412.7907v2.pdf

See Also

jpen

Examples

p=10;n=100;
Sig=diag(p);
y=rmvnorm(n,mean=rep(0,p),sigma=Sig);
gama=c(0.5,1.0);
opt=jpen.tune(Ytr=y,gama);

JPEN documentation built on May 2, 2019, 5:54 a.m.