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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