Re: st: generating predicted values for growth models

[Amy Hsin <hsin.amy@gmail.com>]

Sorry, I re-ran it exactly as you did and it worked.

But what is the difference between what I wrote:
xtmixed gpach i.wave##i.asian i.wave##i.female i.wave##c.psescomk8 if
gpachmiss!=1 || id: wave , cov(unstructured) variance emiterate(250)
mle;

And what you wrote:
 xtmixed gpach i.wave##(i.race i.female c.psescomk8) <other stuff>

Are they not the same?

Amy

On Fri, Jun 29, 2012 at 4:53 PM, Amy Hsin <hsin.amy@gmail.com> wrote:
> H Maarten,
>
> Thanks! That was helpful.  However, I still encounter the same problem
> when I revise the code.
>
> Now the command is the following.  However, the predicted values
> remain unchanged (i.e. they are the same as the observed).  Any
> suggestions?
>
> >
> margins, over(asian wave);
>
>
> Thank you,
> Amy
>
> On Fri, Jun 29, 2012 at 4:23 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> One thing you did wrong is that you used the -xi:- prefix and the
>> -margins- post-estimation command. -margins- needs to know about the
>> interactions you created, and -xi:- will does not leave that
>> information behind. So you need to use the factor variable notation
>> instead, see -help fvvarlist-. Translating your command would be:
>>
>>  xtmixed gpach i.wave##(i.race i.female c.psescomk8) <other stuff>
>>
>> -- Maarten
>>
>> On Fri, Jun 29, 2012 at 4:03 PM, Amy Hsin wrote:
>>> Hi,
>>>
>>> I am trying to generate predicted values from linear growth curves and
>>> am running into problems. The predicted values that I am generating
>>> using models that adjusts for a variety of covariates are the same as
>>> the observed values, which does not make sense. Is there something
>>> that I am obviously doing wrong?
>>>
>>> Here is a simplified version of my model.
>>>
>>> xi: xtmixed gpach i.wave*race i.wave*female i.wave*psescomk8 if
>>> gpachmiss!=1 || id: wave , cov(unstructured) variance emiterate(250)
>>> mle;
>>> margins, over(race wave);
>>>
>>> -gpach are test scores evaluated at time 0, 1, 2 and 3
>>> -race and female are both dichotomous variables
>>> -psescomk8 a time invariant continuous covariate
>>> In this model, I'd like to measure time nonparametrically so I am
>>> including it as a series of dummy variables.
>>> When I use the "margins" command to estimate predicted test scores for
>>> each racial category at each point in time, I get values that are
>>> identical to the observed values.
>>>
>>> Any ideas as to what I am doing wrong?
>>>
>>> Thank you in advance.
>>>
>>>
>>> Amy Hsin
>>> *
>>> *   For searches and help try:
>>> *   http://www.stata.com/help.cgi?search
>>> *   http://www.stata.com/support/statalist/faq
>>> *   http://www.ats.ucla.edu/stat/stata/
>>
>>
>>
>> --
>> --------------------------
>> Maarten L. Buis
>> Institut fuer Soziologie
>> Universitaet Tuebingen
>> Wilhelmstrasse 36
>> 72074 Tuebingen
>> Germany
>>
>>
>> http://www.maartenbuis.nl
>> --------------------------
>>
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>
>
>
> --
> Amy Hsin
> Assistant Professor of Sociology
> Queens College, City University of New York
> http://qcpages.qc.cuny.edu/~ahsin



-- 
Amy Hsin
Assistant Professor of Sociology
Queens College, City University of New York
http://qcpages.qc.cuny.edu/~ahsin

*
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