lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood

• Main Author: Po-Hsien Huang
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2020-04-01
• Series: Journal of Statistical Software
• Subjects: structural equation modeling; factor analysis; penalized likelihood; r
• Online Access: https://www.jstatsoft.org/index.php/jss/article/view/3359

Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations among a large set of variables. This paper describes an R package called lslx that implements PL methods for semi-confirmatory structural equation modeling (SEM). In this semi-confirmatory approach, each model parameter can be specified as free/fixed for theory testing, or penalized for exploration. By incorporating either a L1 or minimax concave penalty, the sparsity pattern of the parameter matrix can be efficiently explored. Package lslx minimizes the PL criterion through a quasi-Newton method. The algorithm conducts line search and checks the first-order condition in each iteration to ensure the optimality of the obtained solution. A numerical comparison between competing packages shows that lslx can reliably find PL estimates with the least time. The current package also supports other advanced functionalities, including a two-stage method with auxiliary variables for missing data handling and a reparameterized multi-group SEM to explore population heterogeneity.