lvglasso: Latent variable graphical LASSO

In lvnet: Latent Variable Network Modeling

Description

The lvglasso algorithm to estimate network structures containing latent variables, as proposed by Yuan (2012). Uses the glasso package (Friedman, Hastie and Tibshirani, 2014) and mimics input and output of the glasso function.

Usage

lvglasso(S, nLatents, rho = 0, thr = 1e-04, maxit = 10000, lambda)

Arguments

S	Sample variance-covariance matrix
nLatents	Number of latent variables.
rho	The LASSO tuning parameter
thr	The threshold to use for convergence
maxit	Maximum number of iterations
lambda	The lambda argument containing factor loadings, only used for starting values!

Value

A list of class lvglasso containing the following elements:

w	The estimated variance-covariance matrix of both observed and latent variables
wi	The estimated inverse variance-covariance matrix of both observed and latent variables
pcor	Estimated partial correlation matrix of both observed and latent variables
observed	Logical vector indicating which elements of w, wi and pcor are observed
niter	The number of iterations used
lambda	The estimated lambda matrix, when result is transformed to EFA model
theta	The estimated theta matrix
omega_theta	The estimated omega_theta matrix
psi	The estimated psi matrix

Author(s)

Sacha Epskamp <[e-mail]>

References

Yuan, M. (2012). Discussion: Latent variable graphical model selection via convex optimization.The Annals of Statistics,40, 1968-1972.

Jerome Friedman, Trevor Hastie and Rob Tibshirani (2014). glasso: Graphical lasso-estimation of Gaussian graphical models. R package version 1.8. http://CRAN.R-project.org/package=glasso

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