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RE: st: Omnibus effects following xtmelogit with margins

From   "Ploutz-Snyder, Robert (JSC-SK)[USRA]" <>
To   "" <>
Subject   RE: st: Omnibus effects following xtmelogit with margins
Date   Fri, 24 Sep 2010 14:46:01 -0500

Thank you Phil for chiming in.  

Indeed, my one thread has turned into two --one about whether a 2 or 3 level model is best, and the second on my original question about getting omnibus main effects and interactions following xtmelogit.  

I originally tried anovalator prior to my post but I was not using the predict(passthru) option that you used here. That option is mentioned briefly in the anovalator help file, with much more thorough coverage at  (link referenced in the help file).  

Thank you very much.  This is what I needed for my original question (yes, I caught that missing snip of code telling anovalator to run main effects or 2way) 

...still contemplating Michaels ideas...


-----Original Message-----
From: [] On Behalf Of Philip Ender
Sent: Friday, September 24, 2010 2:06 PM
Subject: Re: st: Omnibus effects following xtmelogit with margins

"Ploutz-Snyder, Robert (JSC-SK)[USRA]" wrote
... I also ran into a snag using -margins- as I usually do after an
-xtmixed- model to obtain omnibus main effects for an ordinal factor
But my use of -margins- to get the omnibus main effect for time (as I
would with xtmixed) failed:

. margins time, asbalanced atmeans
default prediction is a function of possibly stochastic quantities
other than e(b)

I've been following your discussion with Michael Mitchell about the
design of your analysis.  I would like to pick up on your second
question concerning using -margins-, for which I have two comments.

1)  The default predictor for -xtmelogit- is mu which has uses both
fixed and random effects.  If you are interested in having the
predictive margins scaled as a linear predictor, you can use

.   margins time, asbalanced atmeans predict(xb)

If you want to have your predictive margins scaled as a probabilities,
then -margins- can (will) only use the fixed effects of the model,
like this.
.  margins time, asbalanced atmeans predict(mu fixedonly)

2)  In any case, neither of the above is a "true" main effect because
the factor variables use dummy coding.  You can use the -anovalator-
to get main effects (findit anovalator).

.  anovalator time, predict(xb)   /* or */
.  anovalator time, predict(mu fixedonly)

I hope this is not too confusing.

Phil Ender
UCLA Statistical Consulting Group
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