[R-sig-ME] Zipoisson MCMCglmm for abundance data

[Daniel Sol]

Great, Jarrod. Many thanks for all your help.

Cheers,

Dani

2012/11/27 Jarrod Hadfield <j.hadfield at ed.ac.uk>:
> Hi,
>
> Sorry forgot that one. Have,
>
> us(at.level(trait,1)+at.level(trait,1):dens.surr):location
>
> instead of
>
> us(at.level(trait,1)):location
>
> but be careful, this is a complicated model.
>
> Cheers,
>
> Jarrod
>
>
>
>
>
>
> to the random model.
>
> Quoting Daniel Sol <dsolrueda at gmail.com> on Tue, 27 Nov 2012 15:13:29 +0100:
>
>> Dear Jarrod,
>>
>> many thanks for the rapid answer and very useful advice. Following
>> your advice, I'm gonna use the following code.
>>
>> zi.prior <-  list(R = list(V = diag(2), n = 0.002, fix = 2),
>>               G = list(G1 = list(V = 1, n = 0.002),
>>               G2 = list(V = 1, n = 0.002),
>>                    G3 = list(V = 1, n = 0.002),
>>                    G4 = list(V = 1, n = 0.002)))
>>
>> m2 <- MCMCglmm(abund.urb ~ trait-1 + at.level(trait,1):dens.surr,
>>                          random = ~idh(at.level(trait,1)):location      +
>>               idh(at.level(trait,1)):animal +
>>                                        idh(at.level(trait,1)):sp2 +
>>                                        us(mesd):units,
>>                                             rcov = ~ idh(trait):units,
>>                                        prior = zi.prior,
>>                                        pedigree=tr[[1]],
>>                                        data = dat0.phyl, family =
>> "zipoisson", verbose = TRUE,
>>                                        pr = FALSE, pl = FALSE)
>>
>> However, I'm still wondering how to run the same model allowing the
>> relationship between abundance in the city (abund.urb) and density in
>> the surrounding habitats (dens.surr) to vary across locations
>> (location) in both intercepts and slopes.
>>
>> Many thanks again,
>>
>> Dani
>>
>>
>> 2012/11/27 Jarrod Hadfield <j.hadfield at ed.ac.uk>:
>>>
>>> 1/3b You need to drop at.level(trait,1):location from the fixed model as
>>> you
>>> have it in the random part of the model (although this may just be a
>>> typo).
>>> I would also have trait-1 as you do not want the intercept for the
>>> Poisson
>>> process and the zero-inflation to be the same.
>>>
>>> 2. If you have many observations per species then I would put a
>>> non-phylogenetic species effect in too. If there are few (at the limit,
>>> only
>>> one) then it may be hard to separate the phylogenetic from the
>>> non-phylogenetic.
>>>
>>> 3a. this looks fine but make sure to put mesd in dat0.phyl (I presume
>>> this
>>> is the case otherwise MCMcglmm should spit an error, if it did not please
>>> tell me). Not sure how effort is measured, but you may not expect a
>>> linear
>>> relationship between 1/(effort) and the measurement error variance of the
>>> counts on the log scale. (I presume ** should be ^ in your code)
>>>
>>> 4. With  the 2x2 "idh" structure on the residuals I would use nu=0.002
>>> rather than nu=1.002. Only with a 2x2 "us" structure
>>> is the degree of belief for the marginal distribution of a single
>>> variance
>>> 0.002 when specifying nu=1.002. Parameter expanded priors might also be
>>> entertained for the random effect variances. They will also improve
>>> mixing
>>> if the varinaces are close to zero.
>>
>>
>>
>>
>> --
>> Daniel Sol
>> CREAF (Centre for Ecological Research and Applied Forestries)
>> CSIC (Centre for Advanced Studies of Blanes-Spanish National Research
>> Council)
>> Autonomous University of Barcelona, Bellaterra, Catalonia E-08193, Spain
>> TEL: +34 93-5814678
>> FAX: +34 93-5814151
>> E-MAIL: d.sol at creaf.uab.es
>>
>>
>
>
>
> --
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>



-- 
Daniel Sol
CREAF (Centre for Ecological Research and Applied Forestries)
CSIC (Centre for Advanced Studies of Blanes-Spanish National Research Council)
Autonomous University of Barcelona, Bellaterra, Catalonia E-08193, Spain
TEL: +34 93-5814678
FAX: +34 93-5814151
E-MAIL: d.sol at creaf.uab.es