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From |
Joseph Coveney <jcoveney@bigplanet.com> |

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

Subject |
Re: st: STATA help for GLM misleading? |

Date |
Mon, 14 Nov 2005 17:32:46 +0900 |

Rijo John wrote (excerpted): The STATA help for GLM with the family(binomial) link(logit) option says "For family(binomial) link(logit) models, we recommend using the logistic command in preference to glm. Both produce the same answers, but logistic provides useful post-estimation commands". [Cut] This is actually misleading. When we have independent variables that are fractions which can take any values between 1 and 0 including 1 and zero, using family(binomial) link(logit) along with a robust option is certainly different from logistic regression. [Cut] And the stata help as written above sort of asserted that using family(binomial) link(logit) is going to give the same result as logistic, giving us the impression that STATA treats all the non-zero values in the dependent variable as 1 thus resulting a (0,1) Bernoulli distribution. But for me family(binomial) link(logit) with a robust option gave a better result than logistic command. [Cut] -------------------------------------------------------------------------------- Stata's help for -glm- and -logistic- is not misleading: you'll see that you get identical results for fractional logistic regression in the example below. Cut and paste it into Stata's do-file editor to run it. Just be aware that -logistic- only recognizes zeros and nonzeroes for the response (as the help file for -logistic- states), so you need to set up your dataset to make sense to -logistic- See the do-file below for how. This shouldn't be taken as an endorsement of fractional logistic regression for your data. Alternatives were suggested last month by other list members. Joseph Coveney clear set more off set seed `=date("2005-11-14", "ymd")' set obs 200 generate byte group = _n > _N / 2 generate float proportion = uniform() // No claim as to distribution glm proportion group, family(binomial) link(logit) robust eform nolog rename proportion proportion1 generate float proportion0 = 1 - proportion1 generate int row = _n quietly reshape long proportion, i(row) j(positive) logistic positive group [pweight = proportion], cluster(row) exit * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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