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

EBIClvglasso {lvnet}

R Documentation

Latent variable graphical LASSO using EBIC to select optimal tuning parameter

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 <mail@sachaepskamp.com>

References

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