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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