I'm working with abundance data of some species, but containing too zero

values, and the factors are the ones typical in a BACI experiment

(Before-and-After-Control-Impact). Thus, these are two fixed factors. As the

data does not holds the normality and homogeneity of variances assumptions

of clasiccal ANOVA, I'm trying to fit a zero-altered model using the MCMC

glmm library.

I've two questions:

1.- how I can include an interaction between the BA (before and after) and

the CI (control-impact) components in this kind of models? I'm searching in

the notes available in the models but found no clear answer. My first

approach to this wil be to wrote a formula like: Abundance~BA+CI+BA*CI.

2.- Even when I try to fit a model without interactions I can't do it

because I obtain the following error:

fit<-MCMCglmm(Abundancia~BA+CI, random=NULL,

family="zapoisson",data=Trucha)

Error in MCMCglmm(Abundancia ~ BA + CI, random = NULL, family = "zapoisson",family="zapoisson",data=Trucha)

:

please use idh(trait):units or us(trait):units or trait:units for error

structures involving multinomial data with more than 2 categories or

zero-infalted/altered/hurdle models

I don't know where is the problem, maybe because my original data is

organised as (obviously with much more data):

Abundance BA CI

5 1 1

3 2 1

6 1 2

Any idea or suggestion? Many thanks for your help and patience, best regards

Pablo

8 2 2

--

View this message in context: http://r.789695.n4.nabble.com/MCMC-glmm-tp3304916p3304916.html

Sent from the R help mailing list archive at Nabble.com.

View this message in context: http://r.789695.n4.nabble.com/MCMC-glmm-tp3304916p3304916.html

Sent from the R help mailing list archive at Nabble.com.