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From |
Joseph Coveney <jcoveney@bigplanet.com> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: [multivariate ordered probit models in Stata] |

Date |
Sat, 22 May 2004 13:27:18 +0900 |

Mouratidis Kostas wrote: Dear all, I want to estimate a Mutivariate ordered probit model. The ado program written from {HYPERLINK "/RAS/pca117.htm"}Lorenzo Cappellari and {HYPERLINK "/RAS/pje7.htm"}Stephen Jenkins allow you to estimate a mutivariate binary probit models. So Has any body extent their program from binary to ordered probit model? ------------------------------------------------------------------------------- I believe that it might be doable using -gllamm-. It's my understanding that a multivariate binary probit or logit model is fitted in -gllamm- by (i) generating indicator (dummy) variables to identify the multiple dependent variables, (ii) creating equations corresponding to each of the dummy variables for use in defining random effects in -gllamm-'s -nrf() eqs()- options, and (iii) using the dummy variables also as the predictors (independent variables) in a no-constant model. This sets up the indicator for each response variable to be included in both fixed- and random-effect design matrixes, which in turn allows a general (unstructured) variance-covariance matrix of the latent variables (behind the manifest categories) to be fitted as for a multivariate model. But ordered categorical models are parameterized in Stata with the intercept assumed to be zero in order to identify the model; the first cut-point takes the place normally held by the intercept in other models, such as binary probit or logistic, which do the converse, i.e., hold the threshold at zero and fit the intercept. -gllamm- follows this convention, so the -nocons- option is not permitted in -gllamm- for the ordered-probit or ordered-logit link. It might be possible, i.e., legitimate, to use -constraint define- to fix the first cut- point at zero in order to identify the model (as is done normally behind the scenes for -logit- or -probit-), and then to proceed with -gllamm- analogously as for multivariate binary probit or logit models as described above. (Look at the discussion earlier this month on Statalist in the thread "bivariate random effects meta-analysis of diagnostic test" for an example of fitting a multivariate logistic model with -gllamm-.) With more than a few dependent variables, fitting a multivariate model with -gllamm- can be time-consuming with a dozen or more integration points and with adaptive quadrature. In my experience, ordered categorical models are sometimes difficult to fit (attain convergence); those with random effects, e.g., -reoprob-, -gllamm-, are even more often so. Depending upon your scientific objective, for example, if it's to test a hypothesis of group differences and not to fit the completely specified multivariate ordered categorical model, then -oprobit , score()- coupled with -estimates store- and -suest , cluster()- is a faster alternative to -gllamm-. Joseph Coveney * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Regression query***From:*"Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>

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