EBIClvglasso function

Latent variable graphical LASSO using EBIC to select optimal tuning parameter

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

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

Returns

The optimal result of lvglasso, with two more elements: - rho: The selected tuning parameter

• ebic: The optimal EBIC

References

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

Author(s)

Sacha Epskamp mail@sachaepskamp.com

See Also

lvglasso