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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Predicting sdres in stata |

Date |
Wed, 20 Jul 2011 09:10:08 -0500 |

No. Perhaps you should study the documentation more, especially that in [U], on robust variance estimation in Stata. It is not identical to robust regression. But even with regression, normality of residuals is usually the least important assumption. As you have bivariate regressions, plotting scatter and residual plots is just about the most important checking you can do. Nick On Wed, Jul 20, 2011 at 9:01 AM, Lars Folkestad <lfolkestad@health.sdu.dk> wrote: > 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/

**References**:**Re: st: Predicting sdres in stata***From:*Lars Folkestad <lfolkestad@health.sdu.dk>

**Re: st: Predicting sdres in stata***From:*Richard Goldstein <richgold@ix.netcom.com>

**Re: st: Predicting sdres in stata***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Predicting sdres in stata***From:*Lars Folkestad <lfolkestad@health.sdu.dk>

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