[R-sig-ME] variance structure

[Jarrod Hadfield]

Hi Cristina,

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.

Cheers,

Jarrod









On 6 Apr 2011, at 02:51, Ben Bolker wrote:

> On 11-04-05 06:30 PM, Cristina Gomes wrote:
>> Hello,
>>
>> 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 don’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  
> with
> MCMCglmm, but I think the reviewer is wrong to think that you could  
> use
> a zero-inflated Poisson.  I think the way you did it is fine.   
> However,
> 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  
> MCMCglmm
> in a way analogous to the way zero-inflated Poissons are set up.
>
>  Ben Bolker
>
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