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Extensions to gllamm6

Author

Listed:
  • Sophia Rabe-Hesketh

    (Institute of Psychiatry)

  • Andrew Pickles

    (University of Manchester)

  • Anders Skrondal

    (National Institute of Public Health, Oslo)

Abstract

gllamm is a program to fit generalised linear latent and mixed models. Since gllamm6 appeared in the STB (sg129), a large number of new features have been added. Two important extensions will be discussed: 1) More response processes can now be modelled including ordered and unordered categorical responses and rankings. Multilevel models for nominal data and rankings will be described and fitted in gllamm. 2) Multilevel structural equation models can be fitted by specifying regressions of latent variables on other latent variables and on explanatory variables. Examples will be described and fitted in gllamm. Other new features in gllamm include parameter constraints, and a 'post-estimation' program, gllapred, for estimating posterior means and probabilities.

Suggested Citation

  • Sophia Rabe-Hesketh & Andrew Pickles & Anders Skrondal, 2001. "Extensions to gllamm6," United Kingdom Stata Users' Group Meetings 2001 16, Stata Users Group.
    • Handle: RePEc:boc:usug01:16
    as

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    File URL: http://fmwww.bc.edu/RePEc/usug2001/ukgllamm.pdf
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    File URL: http://fmwww.bc.edu/RePEc/usug2001/ukgllamm.ps
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    File URL: http://www.iop.kcl.ac.uk/IoP/Departments/BioComp/programs/gllamm.html
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    Cited by:

    1. Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October.

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