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

[Jarrod Hadfield]

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
>
>



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