[R-sig-ME] MCMCglmm zero-altered

[Jarrod Hadfield]

Hi,

Then I think your first model is appropriate, although you may still  
want to simplify by having some terms constant over the two processes.

Cheers,

Jarrod


Quoting "Brickhill, Daisy" <r01db11 at abdn.ac.uk> on Wed, 1 Aug 2012  
09:18:14 +0100:

> Thanks Jarrod. I'm afraid it was a case of copying code without  
> testing it and, as you say, it should have read
> prior1ZA = list(R = list(V=diag(1), n=0.002), G = list(G1 =  
> list(V=diag(1), n=0.002))) without fix=2. I have tried the  
> trait:units model and I get significant traitza terms so I would  
> like to use the zapoisson rather than the overdispersed poisson.
> I suppose what I am asking is can I now go on to use my more complex  
> model with rcov  = ~ idh(trait):units?
> Thanks for your help.
>
> -----Original Message-----
> From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk]
> Sent: 31 July 2012 18:53
> To: Brickhill, Daisy
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] MCMCglmm zero-altered
>
> Hi,
>
> To use a ZAP model to test whether there is any zero inflation or  
> deflation effects you want to hold the parameters constant across  
> the Poisson and zero-altered part and compare them to a model in  
> which they vary.
>
> For the random effects this comparison would be random=~colony  
> versus something more complex (idh(trait):colony or  
> us(trait):colony). For the fixed effects you want to compare ~1  
> versus ~trait and
> percent.grass2 versus trait:percent.grass2 etc.
>
>   For the overdispersion term MCMCglmm will not allow you to have  
> the same "residual" for both parts, but ~trait:units allows the  
> "residuals" for both parts to have the same distribution (although
> information regarding its variance only comes from the Poisson part).
> I believe this still allows valid testing of whether there is any  
> zero-alteration or not (but as always, could be wrong). In the  
> trait:units model you do NOT want to fix the variance: in your  
> second prior you had fix=2 despite estimating a single  
> variance(V=diag(1)) so it was probably ignored anyway.  I thought I  
> had implemented MCMCglmm so it would generate an error if  
> fix>nrow(V) - did you not get this?
>
> Cheers,
>
> Jarrod
>
>
>
>
> Quoting "Brickhill, Daisy" <r01db11 at abdn.ac.uk> on Tue, 31 Jul 2012
> 16:18:50 +0100:
>
>> Hi,
>> I am currently modelling the effect of different habitat variables on
>> the numbers of tipulid larvae found in soil cores using MCMCglmm.
>> The data is slightly zero inflated so I am trying a zero-altered model
>> (among others). I have used the following priors and model:
>>
>> prior1ZA = list(R = list(V=diag(2), n=0.002, fix=2), G = list(G1 =
>> list(V=diag(2), n=0.002)))
>>
>> model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht +
>> mean.soil.moisture + juldate + year),random = ~ idh(trait):colony,rcov
>> = ~ idh(trait):units, family = "zapoisson", data = data, prior =
>> prior1ZA, burnin = 3000, nitt = 1003000, thin=1000)
>>
>>
>> However I have read in a previous post by the immensely helpful Jarrod
>> Hadfield that "It is usual in zero-altered models to have the zero bit
>> and the truncated poisson bit have the same over-dispersion. You do
>> this by fitting the  interaction rcov=~traits:units."
>>
>> I thought that ensuring the poisson and the zero process have the same
>> over-dispersion would require priors and model of the form:
>>
>> prior1ZA = list(R = list(V=diag(1), n=0.002, fix=2), G = list(G1 =
>> list(V=diag(1), n=0.002)))
>>
>> model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht +
>> mean.soil.moisture + juldate + year),random = ~ trait:colony, rcov = ~
>> trait:units, family = "zapoisson", data = data, prior = prior1ZA,
>> burnin = 3000, nitt = 1003000, thin=1000)
>>
>>
>> But looking at other posts I am beginning to think I am missing
>> something and that I *can* use my priors and model (with different
>> variances for the zero and poisson parts of the model). Is this true?
>> Can anyone tell me which of the two residual variance and random
>> effect structures is most advisable?
>>
>> Many thanks,
>> Daisy
>>
>>
>>
>>
>>
>> The University of Aberdeen is a charity registered in Scotland, No SC013683.
>>
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>>
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>>
>
>
>
> --
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> Scotland, with registration number SC005336.
>
>
>
>
> The University of Aberdeen is a charity registered in Scotland, No SC013683.
>
>



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.