[R-sig-ME] [R] Problem specifying Gamma distribution in lme4/glmer

Matthew Giovanni matthewgiovanni at gmail.com
Sun May 2 04:04:58 CEST 2010

Hey, everyone.  Thanks so much for your input.  I apologize for not
attaching an example data file before.  I'm attaching an example csv file,
used with the following R script:


I also noticed that Stephani posted an inquiry describing the same problem (
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2008q4/001684.html), where
the Gamma specification works with glm, and the lognormal specification
works with glmer, but the Gamma with glmer produces the same error for the
both of us:

Error in asMethod(object) : matrix is not symmetric [1,2]

Stephani, have you had any luck understanding this error?  I have not been
able to figure it out.  Thanks again for your input, everyone.  Best

Matt Giovanni, Ph.D.
NSERC Visiting Research Fellow
Canadian Wildlife Service
2365 Albert St., Room 300
Regina, SK S4P 4K1
306-780-6121 work
402-617-3764 mobile

On Sun, Mar 21, 2010 at 7:25 PM, Ben Bolker <bolker at ufl.edu> wrote:

>  [Moving this to r-sig-mixed-models ]
> David Winsemius wrote:
> > On Mar 21, 2010, at 5:16 PM, Ben Bolker wrote:
> >
> >> Dieter Menne <dieter.menne <at> menne-biomed.de> writes:
> >>
> >>> Ben Bolker wrote:
> >>>> 3. zero-inflated data may not be particularly well-represented
> >>>> by a Gamma distribution: if you actually have a significant number
> >>>> of exactly-zero values, you may want to analyze your data in two
> >>>> stages, first as a presence-absence problem and then as a
> >>>> conditional
> >>>> density (i.e., what is the distribution of the non-zero values)?
> >>> [...] Do you know of a example where this was done (independent
> >>> of lmer)?  [...]
> >>  Nothing springs to mind, but it seems sensible.
> >
> > I thought this was what hurdle and ZIF models were supposed to handle
> > gracefully?
>  hurdle/zero-inflated/zero-altered models are typically developed in
> the context of discrete (count) data, where the base model has some
> non-zero probability of recording a zero and has to be altered to
> account for the presence of extra (or missing) zeros.  In this case
> (continuous data) the gamma distribution has an infinitesimal
> probability of producing an exact zero, so it's actually easier to deal
> with the data as a mixture of zeros (with probability p) and
> Gamma-distributed values (with shape and scale or rate parameters
> specified).  If it's OK to model the mixture process and the conditional
> density separately this is actually easier than a hurdle or ZIF model.
>  Another possibility, which I've heard of but not ever looked at
> carefully, would be to use Tweedie distributions with 1<p<2:
> http://en.wikipedia.org/wiki/Tweedie_distributions
> http://cran.r-project.org/web/packages/tweedie/index.html
>  Incorporating random effects could be tricky, though: a mean-variance
> relationship is given for Tweedie distributions (V = phi*mu^p), so
> conceivably the fitting could be done as a two-dimensional search over
> the GLMM fits obtained for fixed values of (phi,p).  (Yikes.)
>  Ben
> --
> Ben Bolker
> Associate professor, Biology Dep't, Univ. of Florida
> bolker at ufl.edu / people.biology.ufl.edu/bolker
> GPG key: people.biology.ufl.edu/bolker/benbolker-publickey.asc

More information about the R-sig-mixed-models mailing list