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| From | Lars Folkestad <lfolkestad@health.sdu.dk> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | Re: st: Predicting sdres in stata |
| Date | Wed, 20 Jul 2011 16:01:25 +0200 |
Thank you both of you. Last question: The robust option, does This render the test of residual normality unnessesery? Mvh Lars Folkestad Den 20/07/2011 kl. 15.34 skrev "Nick Cox" <njcoxstata@gmail.com>: > Note that -predict- without options gives you predicted values, What > you call the variable makes no difference to that. > > Nick > > On Wed, Jul 20, 2011 at 7:40 AM, Richard Goldstein > <richgold@ix.netcom.com> wrote: >> 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? >>>>> > > * > * 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/ * * 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/