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| 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. | ||||||
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Page views:: 1476. Submitted: 2018-01-18. Published: 2020-04-27. |
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| Paper: |
lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
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| DOI: |
10.18637/jss.v093.i07
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This work is licensed under the licenses 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. |