# RE: st: Predictions based on reoprob and gllamm

 From "Erik Melander" <[email protected]> To <[email protected]> Subject RE: st: Predictions based on reoprob and gllamm Date Sat, 28 Feb 2004 12:19:52 +0200

```Thanks for the explanation!

Now my problem is that whatever I try with gllapred I get "insufficient
observations" in return. I have 2237 observations of 153 countries from the
years 1976-1995 (unbalanced dataset).

Given that I have too few observations for gllapred, how can I then present
my results so that the substantial impact of the most interesting
independent variables can be appreciated?

Erik

> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of Sophia
> Rabe-Hesketh
> Sent: Friday, February 27, 2004 9:44 PM
> To: [email protected]
> Subject: Re: st: Predictions based on reoprob and gllamm
>
>
> 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

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