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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? > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: : unpaired regression***From:*"Winfield Scott Burhans" <wsb2@cornell.edu>

**Re: st: RE: RE: RE: RE: unpaired regression***From:*"Winfield Scott Burhans" <wsb2@cornell.edu>

**Re: st: RE: RE: RE: RE: unpaired regression***From:*"Winfield Scott Burhans" <wsb2@cornell.edu>

**Re: st: RE: unpaired regression***From:*"Winfield Scott Burhans" <wsb2@cornell.edu>

**Re: st: RE: unpaired regression***From:*"Winfield Scott Burhans" <wsb2@cornell.edu>

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