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GLLAMM Manual

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Author Info

  • Sophia Rabe-Hesketh

    (Graduate School of Education and Graduate Group in Biostatistics, UC Berkeley)

  • Anders Skrondal

    (Biostatistics Group, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway)

  • Andrew Pickles

    (School of Epidemiology and Health Science and CCSR, The University of Manchester , England)

Abstract

This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous, ordered and unordered categorical responses and rankings. The latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution. Examples of models in this class are multilevel generalized linear models or generalized linear mixed models, multilevel factor or latent trait models, item response models, latent class models and multilevel structural equation models. The program can be downloaded from http://www.gllamm.org.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1160&context=ucbbiostat
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Bibliographic Info

Paper provided by Berkeley Electronic Press in its series U.C. Berkeley Division of Biostatistics Working Paper Series with number 1160.

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Date of creation: 25 Oct 2004
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Handle: RePEc:bep:ucbbio:1160

Note: oai:bepress.com:ucbbiostat-1160
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Related research

Keywords: GLLAMM; generalized linear mixed model; latent variable model; item response model; structural equation model; Stata; adaptive quadrature;

References

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  1. Anders Skrondal & Sophia Rabe-Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745.
  2. Anders Skrondal & Sophia Rabe-Hesketh, 2003. "Multilevel logistic regression for polytomous data and rankings," Psychometrika, Springer, vol. 68(2), pages 267-287, June.
  3. Sophia Rabe-Hesketh & Andrew Pickles & Colin Taylor, 2000. "Generalized linear latent and mixed models," Stata Technical Bulletin, StataCorp LP, vol. 9(53).
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