In the meantime you could also have it both ways
using -nlcom-, or so I imagine.
Nick
[email protected]
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of Nick Cox
> Sent: 31 October 2005 10:36
> To: [email protected]
> 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
> [email protected]
>
> 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.
> >
>
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