Linear latent variable models: the lava-package

Klaus Kähler Holst; Esben Budtz-Jørgensen

doi:10.1007/s00180-012-0344-y

Linear latent variable models: the lava-package

Klaus Kähler Holst, Esben Budtz-Jørgensen

30 Citationer (Scopus)

Abstract

An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.

Originalsprog	Engelsk
Tidsskrift	Computational Statistics
Vol/bind	28
Udgave nummer	4
Sider (fra-til)	1385-1452
Antal sider	68
ISSN	0943-4062
DOI	https://doi.org/10.1007/s00180-012-0344-y
Status	Udgivet - 1 aug. 2013

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10.1007/s00180-012-0344-y

http://www.scopus.com/inward/record.url?scp=84881073973&partnerID=8YFLogxK

Citationsformater

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abstract = "An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.",

keywords = "Latent variable model, Maximum likelihood, R, Seasonality, Serotonin, SERT, Structural equation model",

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AU - Holst, Klaus Kähler

AU - Budtz-Jørgensen, Esben

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N2 - An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.

AB - An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.

KW - Latent variable model

KW - Maximum likelihood

KW - R

KW - Seasonality

KW - Serotonin

KW - SERT

KW - Structural equation model

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EP - 1452

JO - Computational Statistics

JF - Computational Statistics

IS - 4

ER -

Linear latent variable models: the lava-package

Abstract

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