Michael,
Can't you do this by creating a dataset of observations with the fixed
values you want, appending it to the full dataset, and using the -fsample-
option of gllapred?
Buzz Burhans
> Dear list
>
> I would like to predict (and plot) conditional probabilities for a
> response as a function of one independent variable keeping a second
> variable at a constant.
>
> More specifically, I would like to plot the predicted probabilities for
> _leng_ by _day_ while _time_ == 4 from the model below.
>
> It is very easy to predict probabilities keeping the random effects at
> a given value with -gllapred- but I have yet to find a way for keeping
> a fixed effect in -gllamm- at a constant.
>
> I appreciate all suggestions.
>
> I think it must be possible to first predict the fixed effects from the
> model with _time_ at a constant (4) and then add the random effects
> from the intercept and the random effect of _leng_.
>
> Thanks for your time.
>
> Michael
>
>
> Partial display of model:
>
> . gen const = 1
> . eq intercept: const
> . eq time: time
> . eq leng: leng
>
> . char day[omit] 2
>
> . xi i.day*leng i.day*time
> . xi: gllamm resp _Iday_1 _Iday_3 leng _IdayXleng_1 _IdayXleng_3 time
> _IdayXtime_1 _IdayXtime_ ///
> . , i(id) nrf(3) eqs(intercept leng time) link(logit) fam(binomial)
> adapt
>
> ------------------------------------------------------------------------
> ------
> resp | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------
> +----------------------------------------------------------------
> _Iday_1 | .5922521 .893279 0.66 0.507 -1.158543
> 2.343047
> _Iday_3 | .2531786 .9334512 0.27 0.786 -1.576352
> 2.082709
> leng | 1.571584 1.170692 1.34 0.179 -.7229305
> 3.866099
> _IdayXleng_1 | 2.473041 1.195547 2.07 0.039 .1298116
> 4.81627
> _IdayXleng_3 | .4036673 1.225166 0.33 0.742 -1.997614
> 2.804949
> time | .2418225 .2774821 0.87 0.383 -.3020324
> .7856774
> _IdayXtime_1 | -.1086444 .2195245 -0.49 0.621 -.5389045
> .3216156
> _IdayXtime_3 | .2298701 .2278498 1.01 0.313 -.2167072
> .6764475
> _cons | -5.118715 1.023391 -5.00 0.000 -7.124525
> -3.112905
> ------------------------------------------------------------------------
> ------
>
> Variances and covariances of random effects
> ------------------------------------------------------------------------
> ------
>
>
> ***level 2 (subject)
>
> var(1): 4.0975884 (2.5381436)
> cov(2,1): -1.2187546 (1.9747622) cor(2,1): -.41187287
>
> var(2): 2.1368686 (2.1924798)
> cov(3,1): -.37716119 (.56908407) cor(3,1): -.26415951
> cov(3,2): -.79399432 (.56427558) cor(3,2): -.77007299
>
> var(3): .49750012 (.24800011)
> ------------------------------------------------------------------------
> ------
>
>
>
> ------------------------------------------------
> Michael Ingre , PhD student & Research Associate
> Department of Psychology, Stockholm University &
> National Institute for Psychosocial Medicine IPM
> Box 230, 171 77 Stockholm, Sweden
>
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