On 2004-07-06, at 15.58, Winfield Scott Burhans wrote:
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
Thanks Buzz,
Great idea. And it might have worked too. But to tell you the truth, I
don't understand how.
I might have misunderstood you but I tried something like the syntax
below:
. 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
. estsave , gen(es_model1)
. save data1
. drop es_model1
. replace time = 4
. replace _IdayXtime_1 = 4 if _IdayXtime_1 > 0
. replace _IdayXtime_3 = 4 if _IdayXtime_3 > 0
. save data2
. use data1
. estsave , from(es_model1)
. generate model = 1
. append using data2
. replace model = 2 if model == .
. gllapred prob if model == 2 , mu fsample
If I plot the probabilities, the graphs look great. And exactly as I
thought they would (or better).
However, I have trouble understanding how -gllapred- would know the
subject specific levels of the random factors when I do like this. Are
they really the same as in the estimation sample? Am I doing the right
thing?
I appreciate any explanations that would help me understand this. My
paper is soon to be submitted (hopefully).
Thanks again Buzz.
If this works, I owe you two by now.
Michael
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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------------------------------------------------
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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* http://www.ats.ucla.edu/stat/stata/
