EBIClvglasso: Latent variable graphical LASSO using EBIC to select optimal...

In lvnet: Latent Variable Network Modeling

Description

This function minimizes the Extended Bayesian Information Criterion (EBIC; Chen and Chen, 2008) to choose the lvglasso tuning parameter. See lvglasso

Usage

EBIClvglasso(S, n, nLatents, gamma = 0.5, nRho = 100, lambda, ...)

Arguments

S	Sample variance-covariance matrix
n	Sample Size
nLatents	Number of latent variables
gamma	EBIC hyper-parameter
nRho	Number of tuning parameters to test
lambda	The lambda argument containing factor loadings, only used for starting values!
...	Arguments sent to lvglasso

Value

The optimal result of lvglasso, with two more elements:

rho	The selected tuning parameter
ebic	The optimal EBIC

Author(s)

Sacha Epskamp <[e-mail]>

References

Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771.

See Also

lvglasso

lvnet documentation built on June 21, 2019, 9:06 a.m.