{"title":"利用 \"galamm \"在 R 中进行多层次半参数潜在变量建模。","authors":"Øystein Sørensen","doi":"10.1080/00273171.2024.2385336","DOIUrl":null,"url":null,"abstract":"<p><p>We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1098-1105"},"PeriodicalIF":5.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multilevel Semiparametric Latent Variable Modeling in R with \\\"galamm\\\".\",\"authors\":\"Øystein Sørensen\",\"doi\":\"10.1080/00273171.2024.2385336\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.</p>\",\"PeriodicalId\":53155,\"journal\":{\"name\":\"Multivariate Behavioral Research\",\"volume\":\" \",\"pages\":\"1098-1105\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multivariate Behavioral Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/00273171.2024.2385336\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/14 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multivariate Behavioral Research","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/00273171.2024.2385336","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/14 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
我们介绍 R 软件包 galamm,它的目标是为结构方程建模和混合效应模型提供共同基础。它支持使用任意数量的交叉或嵌套随机效应、平滑样条、混合响应类型、因子结构、异方差残差和随机缺失数据对模型进行估计。使用稀疏矩阵方法和自动微分实现,确保了计算效率。在此,我们将简要介绍实现方法,概述软件包并举例说明其使用方法。
Multilevel Semiparametric Latent Variable Modeling in R with "galamm".
We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.
期刊介绍:
Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.