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RE: st: RE: : unpaired regression


From   Buzz Burhans <wsb2@cornell.edu>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: RE: : unpaired regression
Date   Wed, 12 May 2004 07:13:34 -0400

At 09:56 AM 5/12/2004 +0100, you wrote:
It seems to me that lining them up is
imparting structure which goes beyond
the structure of data production. Also,
if different assays are on the same footing,
taking one as response adds another arbitrary
decision.

Nick
n.j.cox@durham.ac.uk
Nick,
I am not so sure of your concerns here,

1. If the process that generates these pairs can be construed as having been a random process, it doesn't seem it should be a problem. The 2nd level variable (batch) will account for the lack of independence within batch, the question becomes then is there a further source of non randomness within batch.

2. If there are non random elements in the generation of the results or pairings, for instance an assay time sequence within batch, or a location in the assay system such as location of wells on a plate, or an order that the aliquots etc. were obtained, couldn't it just be added as an ordinal predictor variable?

3. Selecting one as an outcome and one as a predictor should not change the significance of the level one relationship. It would require that the two models be run in order to evaluate the "batch" at level two for the two assays separately.



Alternatively, if the relationship of the within pairs is a concern, I imagine one could come up with a random subsampling scheme that included different pairings than the default original listing and bootstrapped the results.

Buzz Burhans








> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of
> Winfield Scott
> Burhans
> Sent: 12 May 2004 01:18
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: RE: : unpaired regression
>
>
> John,
> One more possibility, last one from me.  Assuming your
> interest is in the
> variance between batches, use either xtreg or gllamm.
>
> Line up the results as Scott suggested, then do either xtreg
> or gllamm.
> Rather than being interested in the significance of the
> coefficient on the
> predictor assay, the outcome of interest would be the significance of
> either  sigma_u (xtreg) or the level two term "batch" in gllamm. In
> gllamm, you could use -gllapred-  with the ustd option to identify
> specific outlier batches
>
> xtreg assay1 assay2, i(batch)
>
> or
>
> gllamm  assay1 assay2, i(batch) adapt
> gllamm, allc
>
> Buzz Burhans
>
>
> >> I have two measures of batch performance on which I'd like to
> >> perform a
> >> regression.  The measurements are taken on separate samples
> >> from the batch,
> >> and typically look something like:
> >>             Assay1 Assay2
> >> Btch1    5400
> >> Btch1    5320
> >> Btch1    5670
> >> Btch1                0.900
> >> Btch1                0.905
> >> Btch1                0.898
> >> Btch2    8600
> >> Btch2    7840
> >> Btch2    7550
> >> Btch2                0.962
> >> Btch2                0.955
> >> Btch2                0.943
> >> ...etc (on for multiple batches which show correlated
> >> measures for the two
> >> assays)
> >> -collapse- ing them to batch averages and then performing the
> >> regression is
> >> one approach, but it doesn't take variance of the measures
> >> themselves into
> >> account in the regression.  Is there a system for performing
> >> this type of
> >> analysis?
> >>
> >
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Buzz Burhans
wsb2@cornell.edu


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