| jpen.inv.tune {JPEN} | R Documentation |
Tuning parameter Selection for inverse covariance matrix estimation based on minimization of Gaussian log-likelihood.
Description
Returns optimal values of tuning parameters lambda and gamma
Usage
jpen.inv.tune(Ytr, gama, lambda=NULL)
Arguments
Ytr |
Ytr is matrix of observations. |
gama |
A vector of gamma values. |
lambda |
Optional vector of values of lambda. If optional, the algorithm automatically calculates 10 values of lambda for each gamma and finds the optimal values of (lambda,gamma) that minimizes the negative of Gaussian likelihood function using K-fold cross validation. |
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: mauryaas@msu.edu.
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.inv.tune(var(y),gama);