Computational Statistics

, Volume 28, Issue 4, pp 1385–1452

Linear latent variable models: the lava-package

Original Paper

DOI: 10.1007/s00180-012-0344-y

Cite this article as:
Holst, K.K. & Budtz-Jørgensen, E. Comput Stat (2013) 28: 1385. doi:10.1007/s00180-012-0344-y

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 Structural equation model  Maximum likelihood Serotonin Seasonality SERT 

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of CopenhagenCopenhagenDenmark

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