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Title:	lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Authors:	黃柏僩 Huang, Po-Hsien
Contributors:	心理系
Date:	2020-04
Issue Date:	2021-08-10 16:44:37 (UTC+8)
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.
Relation:	Journal of Statistical Software, Vol.93, No.7, pp.1851
Data Type:	article
DOI 連結:	https://doi.org/10.18637/jss.v093.i07
DOI:	10.18637/jss.v093.i07
Appears in Collections:	[心理學系] 期刊論文

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