Re: Get Pairwise Comparison in GEE - Ordinal logistic model!

Posted by Bruce Weaver on Aug 14, 2018; 6:43pm
URL: http://spssx-discussion.165.s1.nabble.com/Get-Pairwise-Comparison-in-GEE-Ordinal-logistic-model-tp5736468p5736494.html

Normally, I would use EMMEANS with COMPARE to get pairwise contrasts.  But as
the FM says regarding this particular model:

"Estimated marginal means are not available if the multinomial distribution
is used. If DISTRIBUTION = MULTINOMIAL on the MODEL subcommand and the
EMMEANS subcommand is specified, then EMMEANS is ignored and a warning is
issued."

I don't immediately see another straightforward approach.  

One can get GENLIN to spit out a vector of regression coefficients and a
covariance matrix for the parameters.  So with some application of matrix
algebra, one should be able to make whatever comparisons they wish.  Several
years ago, I wrote a macro to do that (when the boss was asking awkward
questions).  It works well for ordinary (binary) logistic regression, but I
never considered whether it would work with a multinomial outcome variable.
I would not be surprised if it needed some tweaking.  You can find that
macro (!MLEcomp) with an example here:

 
https://sites.google.com/a/lakeheadu.ca/bweaver/Home/statistics/spss/olscomp_mlecomp

If this is a one-time job and elegance is not high on the priority list,
another clunky but less complicated way might be to estimate the model
multiple times, changing the reference category for the variable(s) of
interest each time.  By doing this, I think you should be able to get all of
the comparisons of interest.  (If I was going to try this, I'd use OMS to
write the parameter estimates out to another data set.)  

But with any luck, someone else will come through with a much simpler
approach.  

HTH.



sara2016 wrote

> Here is the Syntax
>
> DATASET ACTIVATE DataSet1.
> * Generalized Estimating Equations.
> GENLIN FamilarityScore (ORDER=ASCENDING) BY Repetition InputMode
> (ORDER=ASCENDING) WITH
>     ProficiencyLevel
>   /MODEL Repetition InputMode ProficiencyLevel Repetition*InputMode
>  DISTRIBUTION=MULTINOMIAL LINK=CUMLOGIT
>   /CRITERIA METHOD=FISHER(1) SCALE=1 MAXITERATIONS=100 MAXSTEPHALVING=5
> PCONVERGE=1E-006(ABSOLUTE)
>     SINGULAR=1E-012 ANALYSISTYPE=3(WALD) CILEVEL=95 LIKELIHOOD=FULL
>   /REPEATED SUBJECT=Participant WITHINSUBJECT=ItemNo*Repetition*InputMode
> SORT=YES
>     CORRTYPE=INDEPENDENT ADJUSTCORR=YES COVB=ROBUST MAXITERATIONS=100
> PCONVERGE=1e-006(ABSOLUTE)
>     UPDATECORR=1
>   /MISSING CLASSMISSING=EXCLUDE
>   /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION (EXPONENTIATED).
>
>
> Thank you.
>
>
>
>
>
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