[R-sig-ME] variance structure
j.hadfield at ed.ac.uk
Wed Apr 6 20:33:42 CEST 2011
I agree with Ben. In addition, MCMCglmm will not fit ZIP models to
these data (because the data are not integers) and ZIG (zero-inflated
Gaussian) models are not implemented. In fact, I can't really see what
the ZIG likelihood would look like, but anyway ...
If the non-zero data are well separated from the zero's (i.e. if
pnorm(0, mean(y[which(y!=0)]), sd(y[which(y!=0)])) is small) then
fitting a bivariate binary/gaussian model is one option, but perhaps
more complex than your problem requires.
On 6 Apr 2011, at 02:51, Ben Bolker wrote:
> On 11-04-05 06:30 PM, Cristina Gomes wrote:
>> I'm running a model where the response is normally distributed and
>> has an excess
>> of zeros. Because I was familiar with the lmer package I used it to
>> run a GLMM
>> on this response, and addressed the problem of the excess of zeros
>> by running
>> two models: one with the response as a binary one, using the
>> complete data set,
>> and another excluding all the zeros and using the remaining
>> response values in a
>> Gaussian model. This seemed to work fine. However, a reviewer
>> suggested using
>> the whole data set and a zero-inflated poisson error structure in
>> the MCMCglmm
>> package. I donâ€™t know if this is appropriate as my response are
>> rates (grams of
>> meat consumed per hr of observation) and not discrete values.
> I would give advice on how to get zero-inflated models working with
> MCMCglmm (mainly, see Ch 5 of the "CourseNotes" vignette that comes
> MCMCglmm, but I think the reviewer is wrong to think that you could
> a zero-inflated Poisson. I think the way you did it is fine.
> if you *wanted* to be fancy you might (after careful reading of Ch 5,
> and thought) be able to set up a zero-inflated normal model in
> in a way analogous to the way zero-inflated Poissons are set up.
> Ben Bolker
> R-sig-mixed-models at r-project.org mailing list
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