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.
lvglasso(S, nLatents, rho = 0, thr = 1e-04, maxit = 10000, lambda)Sample variance-covariance matrix
Number of latent variables.
The LASSO tuning parameter
The threshold to use for convergence
Maximum number of iterations
The lambda argument containing factor loadings, only used for starting values!
A list of class lvglasso containing the following elements:
The estimated variance-covariance matrix of both observed and latent variables
The estimated inverse variance-covariance matrix of both observed and latent variables
Estimated partial correlation matrix of both observed and latent variables
Logical vector indicating which elements of w, wi and pcor are observed
The number of iterations used
The estimated lambda matrix, when result is transformed to EFA model
The estimated theta matrix
The estimated omega_theta matrix
The estimated psi matrix
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