Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Po-Hsien Huang
Title: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Abstract: 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.

Page views:: 1370. Submitted: 2018-01-18. Published: 2020-04-27.
Paper: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood     Download PDF (Downloads: 344)
lslx_0.6.10.tar.gz: R source package Download (Downloads: 24; 1MB)
v93i07.R: R replication code Download (Downloads: 32; 3KB)
v93i07-comparison.R: R replication code Download (Downloads: 30; 14KB)

DOI: 10.18637/jss.v093.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.