st: Conditional probabilities with one fixed effect at a constant

[Michael Ingre <Michael.Ingre@ipm.ki.se>]

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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