Differing results with PROC GENMOD and PROC MIXED

Differing results with PROC GENMOD and PROC MIXED

Post by davidlcass » Tue, 11 Oct 2005 08:36:01


XXXX@XXXXX.COM wrote:

I'll disagree here. I think your results are very consistent. Your 3-way
interaction may be driven more by errors and noise than by real effects.
Everything else looks surprisingly close. However, an artificial
less-or-more-
than-0.05 call will point out what look to me to be really minor differences
in places. If you call p-values of .053 and .036 'different', then you are
making an arbitrary judgment.


[1] Is there a problem with the model specifications? I can't tell. You
have a
complex model, with no residual diagnostics and no residual plots and no
evaluation
of the underlying assumptions. In the PROC MIXED formulation, you are
assuming
a simple autoregressive time series structure. In the PROC GENMOD
formulation,
you are using GEE with an analogous covariance structure. Perhaps neither
is
reaosnable, given the data. I *do* suspect that your DIST=NORMAL option
is not correct. Is Y *really* continuous, or does it just have a number of
different
possible values? You have stated that it is bound between 0 and 7, so it
doesn't
meet the specs I would expect to see in a real normally distributed
variable.

[2] Are the two methods not equivalent? They are not. See my above
discussion. And be apprised that the underlying methods used to fit the
models
are not exactly the same, either.

[3] If they are, which result is more valid?? I'm going to guess here that
NEITHER is valid. I'm going to guess that the normal errors are not
correct.
I'm going to guess that the AR(1) model may not be reasonable. I'm going
to guess that when you do your diagnostics, you'll find at least one outlier
or leverage point which could distort your results.

HTH,
David
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