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, heterosced...

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Veröffentlicht in:Multivariate behavioral research Jg. 59; H. 5; S. 1098 - 1105
1. Verfasser: Sørensen, Øystein
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Routledge 02.09.2024
Taylor & Francis Ltd
Schlagworte:
Algorithms
Computer Simulation - statistics & numerical data
Data Interpretation, Statistical
Generalized additive models
Humans
item response theory
Latent Class Analysis
Matrix methods
mixed models
mixed response
Modelling
Models, Statistical
Multilevel Analysis - methods
Software
Sparse matrices
structural equation modeling
R
mixed models
item response theory
structural equation modeling
Generalized additive models
mixed response
ISSN:0027-3171, 1532-7906, 1532-7906
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:0027-3171
1532-7906
1532-7906
DOI:10.1080/00273171.2024.2385336