[R-sig-ME] MCMCglmm Prior-set for zipoisson with continuous and categorical randoms

[martinab]

Hi everyone,


I'm trying to run a zero-inflated MCMCglmm model from a repeated 
measures data set of aphid abundance on plants (2680 observations) with 
four random variables: 2 categorical (Plant (n = 140) and Block (n = 6)) 
and 2 continuous (Date (n = 15) and Temperature (n = 13)).

Without specifying a prior, this is the further I can go modelling:

pent_Plant_Block_Date_Temp <- MCMCglmm(Aphids~Flowers + Flowers_block, 
random = ~ idh(trait):Plant + idh(trait):Block + Temperature + Date, 
family="zipoisson", rcov=~us(trait):units, burnin = 2000, nitt = 100000, 
thin = 100, pr= TRUE, verbose= FALSE, data=RF2013_st_pentatomidae)

Nevertheless, the model is terrible in terms of effective size and 
autocorrelation, I can´t even plot it as it states "margins are too 
large". I guess I should fit a proper prior and run the model with the 
following random structure:

pent_Plant_Block_Date_Temp <- MCMCglmm(Aphids~Flowers + Flowers_block, 
random = ~ us(trait):Plant + us(trait):Block + Temperature + Date, 
family="zipoisson", rcov=~us(trait):units, burnin = 2000, nitt = 100000, 
thin = 100, prior= ???, pr= TRUE, verbose= FALSE, 
data=RF2013_st_pentatomidae)

I have tried plenty of different priors and none seems to have worked, 
as I always get "V is the wrong dimension for some prior$G/prior$R 
elements". I would appreciate the following:

a) advice for a proper prior-set
b) In case I would run the model as family="categorical" by observing 
just aphid presence/absence instead of abundance with rcov=~trait:units, 
how would it affect the prior??


Thanks in advance. Best,


Martin Aguirrebengoa
PhD student
Zoology Department
University of Granada