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Can you, erase my email the list, plz
Best wishes,
Gabriel Sanchez
Biofeed International, Inc.
408 Jasmine Trail
Athens,Georgia 30606
[hidden email]
1+706-540-6528.
U.S.A
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--------------------------------------------------------
From: SPSSX(r)
Discussion [mailto:[hidden email]] On
Behalf Of Rich Ulrich
Sent: Sunday, February 19, 2012
10:06 PM
To: [hidden email]
Subject: Re: overestimated model
(GLM)
What I had in mind --
Enter Must-belong in block 1, and examine the out-statistics on the rest.
Figure out what to do next.
There never was a block 2, so far as Entering was concerned.
Data-mining is potentially legitimate, but stepwise inclusion
from EVERYTHING has extremely limited value. Almost none.
When you start with a very large sample, you can use some
cases for "training" and most of the cases for very extensive
cross-validation. Otherwise, your results are mainly capitalizing
on chance.
I presume that you want something that might replicate.
Selecting from 100 variables almost guarantees that your
next variables, beyond the obvious and face-valid ones, will
include a large share of "random contributors". You can
Google for < Frank Harrell stepwise > to get some good
comments on the drawbacks of stepwise.
Especially with limited N -- I would want to get rid of variables,
either by dumping a bunch entirely, or by creating composites
to replace them.
--
Rich Ulrich
> Date: Sun, 19 Feb 2012 14:10:29 -0800
> From: [hidden email]
> Subject: Re: overestimated model (GLM)
> To: [hidden email]
>
> @ Rich Ulrich
>
> /
> "Whatever works, do it." Or just,
> "Whatever works ...."/
>
>
> Sure. Sorry for my google translations.
>
>
> However: Would it make sense use blocks, and to put into block 1 of the
> equation
> only the one or two variables that I am sure must belong
> there and to use all the others in a block 2 ?
>
> (to print out anything wouldn't make sense, since the system is expected
to
> run automatically and as fast as possible, and during the night, when I
want
> to sleep ;- )
>
>...
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