Thanks Emil & Mark for the replies,
I indeed was using the stepwise approach to find the best model fit. I now
tried the standard least squares approach and the problem is some of my
-previously- significant variables are no longer signicant.
I therefore would prefer to keep using the hierarchical approach that is
used by JMP in the stepwise model building.
Is this approach common practice (read: acceptable) when reporting research
results? Truth is I´ve never seen this approach in research articles before.
Thank you,
Tom
-----Mensaje original-----
De: jmp-l-owner@lists.biostat.wustl.edu
[mailto:jmp-l-owner@lists.biostat.wustl.edu] En nombre de Mark Bailey
Enviado el: woensdag 16 juli 2008 23:35
Para: jmp-l@lists.biostat.wustl.edu
Asunto: Re: [jmp-l] interpretation categorical variables
> I´m afraid this could be a basic question, but I did not find the
> answer to my question in the jmp help files, so I turn my hopes to
> this list.
You must be using the Stepwise personality in the Fit Model platform.
If you look in Chapter 19 of the JMP Statistics & Graphics Guide, you will
find an explanation in the Models with Nominal and Ordinal Terms section.
The best that one can do is understand how JMP creates these groups. The
interpretation depends on the nature of the data and your study.
> My variable is soil class (soil - 7 classes). How do I interpret this
> dummy variable JMP creates: " Soil{4&3&1 - 6&7&5} "
It has combined the levels that are most alike (4,3,1) and (6,7,5) into two
groups which are the most different, as far as the response goes.
You can control this behavior and disable it if you find it counter-
productive. Select Rules > Whole Effects. The clusters remain but they now
act as one effect in the fitting process. If you select Make Model, it is
copied as one effect and no columns are created in the data table.
Mark
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