lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Dublin Core
Title
lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Subject
structural equation modeling, factor analysis, penalized likelihood, R
Description
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.
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.
Creator
Po-Hsien Huang
Source
https://www.jstatsoft.org/article/view/v093i07
Publisher
National Cheng Kung University
Date
April 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Po-Hsien Huang, “lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8126.