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

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

Subject |
RE: st: Fractional Logit |

Date |
Mon, 31 Oct 2005 10:41:28 -0000 |

In the meantime you could also have it both ways using -nlcom-, or so I imagine. Nick n.j.cox@durham.ac.uk > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox > Sent: 31 October 2005 10:36 > To: statalist@hsphsun2.harvard.edu > Subject: RE: st: Fractional Logit > > > This seems fair comment. The history of -betafit- is > that it grew out of some work I did in fitting beta > distributions to single variables, so no covariates > were in sight. However, adding the options for > dependence on covariates was very easy given the > facilities of -ml- and Stephen Jenkins' prior work > on programs for fitting other distributions given > covariates. I am not surprised that other parameterisations > can make more scientific sense. As I think that -betafit- > can do some things that -mlbeta- can't, supporting > an alternative parameterisation is on the to do list, > but no promises. > > P.S. picky point: there's no stop after "et" in "et al.". In Latin > (and in French) "et" is a complete word meaning "and". > > Nick > n.j.cox@durham.ac.uk > > Maarten Buis > > > The -betafit- command fits the beta distribution in the > > conventional parameterization (e.g. Evans et. al. 2000), the > > -mlbeta- command reparameterizes the distribution in terms of > > a mean and a scale factor for the variance (the variance also > > depends on the mean). In a regression context the > > parameterization of -mlbeta- makes more sense, because you > > usually want to model the how the mean response changes when > > your explanatory variable changes. If you want to use > > -betafit- in a regression context you have to give > > substantive meaning to the alpha and beta parameter. If the > > outcome is a proportion than the alpha and beta parameter can > > be seen as expected counts for the two groups. However, I > > have only used this as a rule of thumb (within a bayesian > > context when I formulate a prior). I am not sure whether this > > is interpretation is applicable within a regression context, > > since you don't observe the counts, just the proportions. The > > expected proportions are inferred from the combinat > > ion of spread and mean. However inferring counts from a > > proportion (without knowing the total) seems like an > > Ecological Inference problem to me, and I would stay away > > from that if you can. So I would advise you to use -mlbeta- > > for your problem instead of -betafit-. > > > > Hope this helps, > > Maarten > > > > Merran Evans, Nicholas Hastings and Brian Peacock (2000), > > "Statistical Distributions", Third edition. New York: Wiley > > Inter-science. > > > > * > * 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/ > * * 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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