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st: RE: RE: RE: RE: unpaired regression


From   "Wallace, John" <John_Wallace@affymetrix.com>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: RE: RE: unpaired regression
Date   Tue, 11 May 2004 13:25:26 -0700

Thanks Nick, I've been using anova to study this problem as well - what I'm
looking to do is find something like a slope and intercept that you'd get
from a regression to describe the metric from assay1 as a function of
assay2, but with a confidence interval based on the observed variation of
the measurements in the two assays.
In other words, the _averages_ of the two assays are indeed paired for each
batch observation, but the relative variance of the measurements differ.

-----Original Message-----
From: Nick Cox [mailto:n.j.cox@durham.ac.uk] 
Sent: Tuesday, May 11, 2004 1:20 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: RE: RE: unpaired regression

Your problems looks to me like -anova-, 
the flavor depending on what "separate"
means. It is not regression without 
pairing. I don't know what "unpaired regression"
would be. 

Nick 
n.j.cox@durham.ac.uk 

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of 
> Wallace, John
> Sent: 11 May 2004 20:57
> To: 'statalist@hsphsun2.harvard.edu'
> Subject: st: RE: RE: unpaired regression
> 
> 
> Can anyone comment on whether Scott's suggestion would be 
> appropriate for
> the problem I'm working on?  The difference in R^2 between the samples
> indicates that it might be problematic.
> 
> John Wallace | Research Associate | Test Method Development
> AFFYMETRIX, INC. | 3380 Central Expressway | Santa Clara, CA 
> 95051 | Tel: 
> 408-731-5574 | Fax:  408-481-0435
> 
> -----Original Message-----
> From: Wallace, John [mailto:John_Wallace@affymetrix.com] 
> Sent: Monday, May 10, 2004 10:08 PM
> To: 'statalist@hsphsun2.harvard.edu'
> Subject: st: RE: RE: unpaired regression
> 
> Doesn't that imply a relationship between the observations 
> though?  Wouldn't
> it be equally valid to end up with them lined up like
>      +-------------------------+
>      | batch   assay1   assay2 |
>      |-------------------------|
>   1. | Btch1     5400     .905 |
>   2. | Btch1     5320     .898 |
>   3. | Btch1     5670     .9   |
>   4. | Btch2     8600     .943 |
>   5. | Btch2     7840     .955 |
>   6. | Btch2     7550     .962 |
> 
> In the original line-up, the coefficient of determination is 
> 0.968.  In the
> second one above, its 0.8.
> 
> 
> -----Original Message-----
> From: Scott Merryman [mailto:smerryman@kc.rr.com] 
> Sent: Monday, May 10, 2004 6:42 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: unpaired regression
> 
> How about lining up the measurements?
> 
> Something like
> 
> . l
> 
>      +-------------------------+
>      | batch   assay1   assay2 |
>      |-------------------------|
>   1. | Btch1     5400        . |
>   2. | Btch1     5320        . |
>   3. | Btch1     5670        . |
>   4. | Btch1        .       .9 |
>   5. | Btch1        .     .905 |
>      |-------------------------|
>   6. | Btch1        .     .898 |
>   7. | Btch2     8600        . |
>   8. | Btch2     7840        . |
>   9. | Btch2     7550        . |
>  10. | Btch2        .     .962 |
>      |-------------------------|
>  11. | Btch2        .     .955 |
>  12. | Btch2        .     .943 |
>      +-------------------------+
> 
> . by batch: replace assay2 = assay2[_n +3]
> (12 real changes made, 6 to missing)
> 
> . drop if assay1 == .
> (6 observations deleted)
> 
> . l
> 
>      +-------------------------+
>      | batch   assay1   assay2 |
>      |-------------------------|
>   1. | Btch1     5400       .9 |
>   2. | Btch1     5320     .905 |
>   3. | Btch1     5670     .898 |
>   4. | Btch2     8600     .962 |
>   5. | Btch2     7840     .955 |
>      |-------------------------|
>   6. | Btch2     7550     .943 |
>      +-------------------------+
> 
> 
> Scott
> 
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Wallace, John
> Sent: Monday, May 10, 2004 7:48 PM
> To: 'statalist@hsphsun2.harvard.edu'
> Subject: st: unpaired regression
> 
> 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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