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RE: st: RE: : unpaired regression


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: : unpaired regression
Date   Wed, 12 May 2004 09:56:16 +0100

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 

> -----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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