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題名: | lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood | 作者: | 黃柏僩 Huang, Po-Hsien |
貢獻者: | 心理系 | 日期: | Apr-2020 | 上傳時間: | 10-Aug-2021 | 摘要: | 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. | 關聯: | Journal of Statistical Software, Vol.93, No.7, pp.1851 | 資料類型: | article | DOI: | https://doi.org/10.18637/jss.v093.i07 |
Appears in Collections: | 期刊論文 |
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