crit() calculates the critical value of the FGL objective funciton. It is used to confirm that the FGL algorithm is converging.
crit(theta, S, n, lam1, lam2, penalize.diagonal)crit, the critical value of the list of inverse covariance matrices.
A list of pXp inverse covariance matrices.
A list of pXp empirical covariance matrices.
A vector of sample sizes to attribute to each of the K data matrices. n controls the relative weights of the classes: for example, with n==c(1,1), each class's theta will be penalized equally.
The tuning parameter for the graphical lasso penalty.
The tuning parameter for the fused lasso penalty.
Logical value determing whether the graphical lasso penalty should also be applied to the diagonal of the inverse covariance matrices.
Patrick Danaher
A function called by FGL to calculate the critical value of the objective function.
Patrick Danaher, Pei Wang and Daniela Witten (2011). The joint graphical lasso for inverse covariance estimation across multiple classes. http://arxiv.org/abs/1111.0324