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From |
Nick Cox <n.j.cox@durham.ac.uk> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: conditional SE of y|X in glm |

Date |
Tue, 24 Apr 2012 13:39:55 +0100 |

I don't understand "replicating" here but do please look in the manual for questions like that. Nick n.j.cox@durham.ac.uk Marco Ventura Thank you Nick, could you please tell me what Stata does exactly for replicating e(dispers) when family is not normal? 4/04/2012 13:29, Nick Cox ha scritto: > With this model, as with every other, you have to decide what you mean by "prediction", i.e. on what scale you are predicting. > > Also, I did write > > "I like to have such measures accessible for comparing -glm- results with those of other models in which rmse appears naturally." > > and I think logit models are stretching the point. > > In essence, what -glmcorr- does in your example is either wrong or irrelevant, depending on your point of view. -glmcorr- can be reconciled with those results by doing instead > > . gen fraction = r/n > > . glm fraction ldose , link(logit) > > Iteration 0: log likelihood = 3.345982 > Iteration 1: log likelihood = 3.7166249 > Iteration 2: log likelihood = 3.7245648 > Iteration 3: log likelihood = 3.724566 > Iteration 4: log likelihood = 3.724566 > > Generalized linear models No. of obs = 24 > Optimization : ML Residual df = 22 > Scale parameter = .0468293 > Deviance = 1.030244611 (1/df) Deviance = .0468293 > Pearson = 1.030244611 (1/df) Pearson = .0468293 > > Variance function: V(u) = 1 [Gaussian] > Link function : g(u) = ln(u/(1-u)) [Logit] > > AIC = -.1437138 > Log likelihood = 3.724566043 BIC = -68.88694 > > ------------------------------------------------------------------------------ > | OIM > fraction | Coef. Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > ldose | 22.43087 5.627079 3.99 0.000 11.402 33.45974 > _cons | -40.34087 10.10823 -3.99 0.000 -60.15264 -20.52909 > ------------------------------------------------------------------------------ > > . glmcorr > > fraction and predicted > > Correlation 0.800 > R-squared 0.640 > Root MSE 0.216 > > . di sqrt(e(dispers)) > .21640079 > > However, that would lose some of the information in the data. > > Otherwise, -glmcorr- uses what -predict- produces by default; if that's wrong for your problem, so will the results 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 Marco Ventura > Sent: 24 April 2012 10:29 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: conditional SE of y|X in glm > > Dear Nick, > thank you very much of your quick replies. > Unfortunately there is something I still do not understand. If I do > use http://www.stata-press.com/data/r10/beetle > glm r ldose, fam(bin n) link (logit) > di sqrt(e(dispers)) > glmcorr > I get two very different values 4.065 against 13.179. Which of the two > is correct? > > Thank you again. > Marco > > Il 24/04/2012 10:57, Nick Cox ha scritto: >> See -glmcorr- (SSC) for one approach here. That calculates an rmse >> which appears similar, if not identical, to what you want. I like to >> have such measures accessible for comparing -glm- results with those >> of other models in which rmse appears naturally. Perhaps it is a >> comfort blanket, but there you go. >> >> Note that putting a constant into a variable is usually overkill as >> >> di sqrt(e(dispers)) >> >> does the calculation. Use a scalar or local macro if you want to store >> the value. >> >> On Tue, Apr 24, 2012 at 9:31 AM, Marco Ventura<mventura@istat.it> wrote: >> >>> from a GLM estimate I want to retrieve the conditional standard error of y >>> given the covariates. If I do >>> >>> gen sigma=sqrt(e(dispers)) >>> >>> do I always get the right thing independently of any family and link? >>> Should I correct it by sqrt(e(dispers)* (_N-1)/_N)? >>> And do you think I should instead use the Pearson residuals such as >>> >>> gen sigma=sqrt(e(dispers_p)) >>> > * > * 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/ * * 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**:**st: conditional SE of y|X in glm***From:*Marco Ventura <mventura@istat.it>

**Re: st: conditional SE of y|X in glm***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: conditional SE of y|X in glm***From:*Marco Ventura <mventura@istat.it>

**RE: st: conditional SE of y|X in glm***From:*Nick Cox <n.j.cox@durham.ac.uk>

**Re: st: conditional SE of y|X in glm***From:*Marco Ventura <mventura@istat.it>

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