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| From | Richard Goldstein <richgold@ix.netcom.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Predicting sdres in stata |
| Date | Wed, 20 Jul 2011 08:40:49 -0400 |
I think regression is the best way; I am not familiar with how either of the two concepts are measured; for general guidance on this kind of adjustment, I suggest the following two articles (which have different but related points): Rosenbaum, PR and Rubin, DB (1984), "Difficulties with regression analyses of age-adjusted rates," _Biometrics_ 40: 437-443 Kronmal, RA (1993), "Spurious correlation and the fallacy of the ratio standard revisited," _Journal of the Royal Statistical Society, Series A_, 156(3): 379-392; comments and reply in the same journal (1995), 158(3): 619-625 Rich On 7/20/11 8:28 AM, Lars Folkestad wrote: > Thank You For the swift answare. > I was indeed trying to predict the residuals for the regression model. > > What i am trying to do is to adjust a Bone Density Value for the > participants Body surface area. Is there a better way to do this than > regression? > > Will figure wich option fits best. > > Lars > > Den 20/07/11 14.19 skrev "Richard Goldstein" <richgold@ix.netcom.com> > følgende: > >> without knowing what depenVar1 and depenVar2 are, it is not possible to >> fully answer the question >> >> however, note that what you are asking for are the predicted values from >> the equation and this depends solely on the value of the constant and >> the value of the coefficient for BSA; apparently, these are "very >> similar" in the two regressions; do you mean to ask for the predicted >> values or are you trying to predict some kind of residual? if you want >> some kind of residual, you will need to add an option; see -h regress >> postestimation- and click on "predict" >> >> Rich >> >> On 7/20/11 8:05 AM, Lars Folkestad wrote: >>> Hi Stata Listers >>> >>> This is probably a simple question for you all. I just cannot see my way >>> through it. >>> >>> I am doing liniar regression for different variables as a way to adjust for >>> Body Surface Area. I do the following >>> >>> . regress depenVar1 BSA, vce(robust) >>> . predict sdres >>> . qnorm sdres >>> . swilk sdres >>> . predict adjdepenVar1 >>> . drop sdres >>> >>> . regress depenVar2 BSA, vce(robust) >>> . predict sdres >>> . qnorm sdres >>> . swilk sdres >>> >>> The two swilks tests give the exact same p-value and the qnorm graf is >>> identical. >>> >>> I cannot understand how. For your information i am new to stata and >>> regression and my statistically knowledge is low. >>> >>> Why is the two swilks tests and qnorms the same? >>> >>> lars > -- > Lars Folkestad > Læge, PhD-studerende > Endokrinologisk Afdeling M / Endokrinologisk afdeling / Klinisk Institut > Odense Universitets Hospital / Sydvestjysk Sygehus Esbjerg / Syddansk > Universitet * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/