[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Mak, Timothy" <timothy.mak07@imperial.ac.uk> |

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

Subject |
st: RE: RE: curious behavior of glm |

Date |
Fri, 5 Jun 2009 17:37:31 +0100 |

Perhaps I'm not making myself clear. There are two issues in my original post. 1. Why is it that -glm- refuses to calculate a binomial proportion (when r != 0 & r != n)? 2. Why doesn't -glm- give an error message and give up (as would -logit-) in a case where the coefficients are clearly non-estimable by ML? Both problems are trivial - there are easy ways to work around it. I was just hoping that if there weren't any theoretical reason for -glm- to behave this way, that a future update may make -glm- behaves more like -logit- in these situations. Yours, Tim -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of jhilbe@aol.com Sent: 05 June 2009 17:09 To: statalist@hsphsun2.harvard.edu Subject: st: RE: curious behavior of glm Regarding the estimation of 1) a single observation logistic model, and 2) a two observation logistic model, having the binomial form with a y being the binomial numerator and n the denominator: When you use cii, or engage in a simple case where the estimated coefficient or odds ratio is computed directly from the binomial PDF you are of course more likely to get a meaningful result. Using maximum likelihood entails assumptions which are not met in such a situation. In fact, you cannot even get results using exact logistic regression via the -exlogistic- command. On the other hand, -exlogistic- estimates the second situation where you have two observations, each with response y, binomial denominator n, and binary predictor x. However, you do not get exact values, but rather median unbiased estimates. y n x -------------- 10 100 1 0 100 0 Model the above using -exlogistic-: . input r n x r n x 1. 10 100 1 2. 0 100 0 3. end . exlogistic y x, binomial(n) coef estc Enumerating sample-space combinations: observation 1: enumerations = 11 observation 2: enumerations = 101 observation 3: enumerations = 10201 note: CMLE estimate for x is +inf; computing MUE note: CMLE estimate for _cons is -inf; computing MUE note: .975 quantile estimate for _cons failed to bracket the value Exact logistic regression Number of obs = 200 Binomial variable: n Model score = 10.47368 Pr >= score = 0.0015 ------------------------------------------------------------------------- -- y | Coef. Suff. 2*Pr(Suff.) [95% Conf. Interval] -------------+----------------------------------------------------------- -- x | 2.722305* 10 0.0015 .8727845 +Inf _cons | 0* 10 0.0000 -Inf +Inf ------------------------------------------------------------------------- -- (*) median unbiased estimates (MUE) I requested estimation of a constant although it is obvious that it is not meaningful in such a situation. Compare the above with the clearly mistaken "estimated coefficients" that you provided in your output. . glm r x, fam(bin n) Generalized linear models No. of obs = 2 Optimization : ML Residual df = 0 Scale parameter = 1 Deviance = 2.00000e-08 (1/df) Deviance = . Pearson = 1.00000e-08 (1/df) Pearson = . Variance function: V(u) = u*(1-u/n) [Binomial] Link function : g(u) = ln(u/(n-u)) [Logit] AIC = 4.025974 Log likelihood = -2.025973987 BIC = 2.00e-08 - ------------------------------------------------------------------------- ----- | OIM r | Coef. Std. Err. z P>|z| [95% Conf. Interval] - -------------+----------------------------------------------------------- ----- x | 23.87722 10000 0.00 0.998 -19575.76 19623.52 _cons | -26.07444 10000 -0.00 0.998 -19625.71 19573.56 - ------------------------------------------------------------------------- ----- These coefficients indicate a problem with convergence. Exponentiate to obtain an odds ratio: . di %12.0f exp(23.87722) 23428521860 We have an odds ratio here of some 23.4 billion. No surprise. The problem is that the assumptions upon which ML estimation is based are not met here. I tried your examples with several other commercial applications, as well as R, with the same results. The bottom line is that there is nothing wrong with -glm- here. Joseph Hilbe * * 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: RE: curious behavior of glm***From:*jhilbe@aol.com

- Prev by Date:
**RE: st: RE: AW: Sample selection models under zero-truncated negative binomial models** - Next by Date:
**st: RE: Nested locals?** - Previous by thread:
**st: RE: curious behavior of glm** - Next by thread:
**st: RE: Logistic Regression Models** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |