[R-sig-ME] Deviations from normality in MCMC models

[Evan Palmer-Young]

Is it possible you could use the "exponential" family in mcmcglmm? It seems
to have a (negative) log link function, which could address the skew in
your data.
Reference this thread on exponential family in mcmcglmm
<https://stat.ethz.ch/pipermail/r-sig-mixed-models/2013q3/020851.html>

On Thu, Jun 1, 2017 at 9:47 PM, Ben Bolker <bbolker at gmail.com> wrote:

>
>  In my experience it's *usually* the case that a log-Normal (i.e.,
> log-transforming the variable and treating it as Normal) should be an
> adequate substitute for a Gamma.  They have the same general qualitative
> range of shapes. If the log-Normal is problematic/your log-transformed
> data are far from Normal, then you're likely to have had similar
> troubles with a Gamma. As one example, you have observations that are
> exactly zero, then you can't log transform them -- but these don't work
> with the Gamma either, as it has a log-likelihood density that is either
> negative infinite, if the shape parameter is >1, or infinite, if the
> shape parameter is <1.
>
> On 17-06-01 02:29 PM, landon hurley wrote:
> > On 01/06/2017 10:04, Christopher Robinson wrote:
> >> The MCMCglmm package in R allows users to specify the distribution
> >> family for each variable. Unfortunately, I cannot fit a gamma
> >> distribution to my data because MCMCglmm currently does not support
> >> this family. My question is how sensitive to deviations from
> >> normality is MCMC? That is, if I apply a gaussian distribution to
> >> this variable, will my results be strongly affected?
> >
> > Chris,
> >
> > Why not fit the normal model, cross-validate, and then decide if its
> > adequate? Depending upon the sample size of course, it may not make a
> > large difference in terms of choice of the prior, beyond support
> > constraints introduced for the variable being modelled if you used
> > something other than Gaussian.
> >
> > If negative predicted responses are of a concern (assuming it's
> > truncated at zero), it looks like you could potentially use the censored
> > Gaussian family options, which would at least restrict you to the same
> > support as a Gamma distribution.
> >
> >
> >
> >
>
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-- 
Evan Palmer-Young
PhD candidate
Department of Biology
221 Morrill Science Center
611 North Pleasant St
Amherst MA 01003
https://scholar.google.com/citations?user=VGvOypoAAAAJ&hl=en
https://sites.google.com/a/cornell.edu/evan-palmer-young/
epalmery at cns.umass.edu
ecp52 at cornell.edu

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