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
[email protected]
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of
> Winfield Scott
> Burhans
> Sent: 12 May 2004 01:18
> To: [email protected]
> 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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> >
>
> *
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*
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