lamvec: returns a vector of values of lambda for given value of gamma

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

Description

returns 10 values of lambda for each gamma.

Usage

lamvec(c, gam, p)

Arguments

c	c is absolute maximum of off-diagonal entries of sample covariance matrix.
gam	gamma is a non-negative constant.
p	p is number of rows/columns of matrix.

Details

The lamvec function retuns a 10 values of lambda for each value of gamma. A larger value of lambda yields sparse estimate but need not be positive definite, however at least one combination of (lambda, gamma) will yield a positive definite solution. If two different combination of (lambda, gamma) yeilds same cross validation error, a larger values of lambda will be selected which results in more sparse solution.

Value

A vector of values of lambda for each combination of gama. By choosing c as the maximum of off-diagonal elements of sample covariance matrix, the largest value of lambda yields an estimate which diagonal matrix with elements proportional to the diagonal elements of sample covariance matrix.

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, jpen.inv, jpen.tune, jpen.tune.inv

Examples

p=10;n=100;Sig=diag(p);
y=rmvnorm(n,mean=rep(0,p),sigma=Sig);
gam=c(0.5);
S=var(y);
c=max(abs(S[row(S)!=col(S)]));
lambda=lamvec(c,gam,p);

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