Re: st: ordinal mixed effects model with interaction and quadratic terms

[Darcy Hannibal <dlhannibal@ucdavis.edu>]

You did not mention which version of Stata you are using. If you are 
using at least 12 or higher then you do not need to create an 
interaction variable, you can just add into the syntax "sex##c.agez" 
along with your independent variables. I'm not sure about versions prior 
to 12, but I don't think it is possible. If that is so and you have a 
version older than 12, then yes you will have to create the variable for 
the interaction.
For ordinal models, I haven't worked with predicted values the way you 
are describing in item 2 below. After running meologit (or meglm) I then 
use "predict Pr*, pr". The "Pr" is the stub for new variables of 
predicted probabilities for each category of an outcome variable. After 
running this, you will have 4 variables for each of your 4 categories of 
your outcome variable that will be named Pr0, Pr1, etc. (you can of 
course use a stub other than Pr). I then use these variables with the 
predicted probabilities to create plots. If you are interested in this I 
can send you examples of the syntax I've used and the resulting plots.
I hope that helps,
Darcy

On 3/28/2014 12:59 PM, Pritsch, Julian wrote:
> Dear statalist-users,
>
> I am estimating a ordinal multilevel model using the -meologit- command. My dependent variable has 4 categories (0-3).
> In my Level-1 model I introduced a dummy for sex (sex) and a z-standardized version of age (agez). Additionally, I introduced and z-standardized squared-term of age (age_sqz) because I want to show non-linear effects of age on my DV.
>
>
> My question is twofold:
> (1)  I want to introduce an interaction term of sex & agez: Do I also need to form an interaction term of sex and the squared version of agez to specify my model correctly?
>
> (2) After estimating my model I would like to find out (using the -marginsplot- command), if there are any differences  between male and female respondents regarding the age effect.
>
> Regarding question (2) I tried the following:
>
> ---------------------code-----------------------------------------------
> *for outcome(0)
> margins sex, at(agez=(-1.88 -1.19 0.01 1.11 1.85)) vsquish ///
> 		  level(99) ///
> 		  predict(outcome(0) fixedonly)
>
> --------------------------------------------------------------------------
> *Note: the values for agez are the 2/15/50/85/98-percentile to represent -2SD/-1SD/ 0 /+1SD/+2SD
>
>
> I repeat that syntax for every outcome(0,1,2,3) and will try to combine the graphs.
>
> Is there a more elegant way to do this? And what about the interaction of sex and the squared term of age (age_sqz)
>
> Any advice would be appreciated.
>
> Julian
> _________________
>
> Julian Pritsch, Dipl.-Soz.
>
> Max-Planck-Institut für ausländisches
> und internationales Strafrecht
> Günterstalstraße 73
> 79100 Freiburg i. Br.
> Tel.: +49 (761) 7081-291
> e-Mail: j.pritsch@mpicc.de
> homepage: www.mpicc.de
>
> Max-Planck-Institute for Foreign
> and International Criminal Law
> Guenterstalstrasse 73
> 79100 Freiburg i. Br.
> Germany
> Phone: +49 (761) 7081-291
> e-Mail: j.pritsch@mpicc.de
> homepage: www.mpicc.de
>
>
>
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--
Darcy L. Hannibal, PhD
Staff Research Associate III Supervisor

McCowan Animal Behavior Laboratory for Welfare and Conservation
Department of Population Health and Reproduction

Behavior Management
Brain, Mind, and Behavior Unit
California National Primate Research Center

University of California at Davis

Office: 3029-B CNPRC
Phone: 530-752-1586


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