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From |
Sophia Rabe-Hesketh <sophiarh@berkeley.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Predictions based on reoprob and gllamm |

Date |
Fri, 27 Feb 2004 11:43:31 -0800 |

The thresholds are applied to the latent response y* underlying the observed response y. y* can take on any value from - infinity to + infinity. If the lowest observed response category, say 1, happens very rarely, the lowest threshold may have to be a large negative number because Pr(y=1) = Pr(y*<threshold) and similarly for the largest category. I am not sure what predictions you have considered. If you are referring to 'xb' (the linear predictor), then this is the mean of the latent response y* which doesn't need to lie on the range of y. In gllamm, you can obtain the predicted (population averaged) cumulative probabilities, e.g., Pr(Y>1) using gllapred probgr1, mu above(1) marg and similarly for the other categories. You can also get predicted probabilities for particular values of the random effects or posterior mean probabilities. Sophia Erik Melander wrote:

I have a panel dataset with an ordinal dependent variable, judgmentally coded from 0 to 4. There is considerable inertia in the dependent variable and I thus want to include a lagged dependent variable (actually a set of 4 dummies since it is ordinal scale) to control for autocorrelation. I have tried to run random effects ordinal probit and logit for panel data, using for example the stata commands below: reoprob dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1 dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, i (panelunit) gllamm dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1 dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, link(oprobit) i(panelunit) gllamm dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1 dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, link(ologit) i(panelunit) In the output, one thing that seems a little weird is that the cuts/thresholds give a broader range than the dependent variable itself. The range of the coefficients of the categories of the lagged variable is only about half the range of the cuts/thresholds. Most disturbing of all is that the resulting models give rise to predictions that are outside the range of the dependent variable. Why is this so, and is there anything I can do in order to arrive at models with more reasonable predictions? Thanks for your attention. Erik Melander * * 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/

-- Sophia Rabe-Hesketh, Professor Educational Statistics Graduate School of Education 3659 Tolman Hall University of California, Berkeley Berkeley, CA 94720-1670 WWW: http://www.gllamm.org/sophia.html * * 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**:**RE: st: Predictions based on reoprob and gllamm***From:*"Erik Melander" <erik.melander@pcr.uu.se>

**Re: st: Predictions based on reoprob and gllamm***From:*Buzz Burhans <wsb2@cornell.edu>

**References**:**st: Predictions based on reoprob and gllamm***From:*"Erik Melander" <erik.melander@pcr.uu.se>

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