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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Published in:Multivariate behavioral research Vol. 59; no. 5; pp. 1098 - 1105
Main Author: Sørensen, Øystein
Format: Journal Article
Language:English
Published: United States Routledge 02.09.2024
Taylor & Francis Ltd
Subjects:
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 Access:Get full text
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Abstract 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.
AbstractList 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.
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.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.
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.
Author Sørensen, Øystein
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  organization: Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo
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Snippet We present the R package galamm , whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation...
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...
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SubjectTerms 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
Title Multilevel Semiparametric Latent Variable Modeling in R with "galamm"
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