[R-sig-ME] parameter estimates for all factor levels MCMCglmm

[Jarrod Hadfield]

Hi,

You have an intercept which will be the estimate for the first level of 
Arthropod and Size, the remaining effects are deviations from these 
levels (15 and 2 contrasts respectively).

Cheers,

Jarrod



On 05/12/2016 04:29, Dena Paris wrote:
> Hi all,
>
> I'm new to Bayesian stats and the MCMCglmm package. I'm trying to understand how to interpret the results from a fitted model. I've fitted a model with a binomial response (reject/select), two categorical fixed effects (taxon with 16 levels, and size class with 3 levels), and a single random effect (bird ID). My model is mixing well and the results fit the data. My problem is that I'm not getting estimates for all levels of the fixed effects (19). How do I get the post mean and CIs for all levels in order to correctly interpret/write up the results?
>
> As I have (near) complete separation in the data, I've used the fixed effect prior structure suggested in the Course Notes, fixed the residuals, and removed the global intercept.
>
> prior.1 = list(
>    B = list(mu = rep(0, 18), V = (diag(18)) * (1 + pi^2/3)),
>    R = list(fix=1, V=1, n = k - 1),
>    G = list(G1 = list(V = 1, n = 1))
> )?
>
> m.1 <- MCMCglmm(Selected ~ -1 + Arthropod + Size, random = ~bID, family = "categorical", prior = prior.1, data = type.selected, verbose = FALSE, nitt = 5e+05, burnin = 5000, thin = 100)
>
> Thank you for your guidance,
> Dena
>
>
> Dena Paris
>
> -----
> Dena Paris
> Ph.D Candidate
> School of Environmental Sciences
> Institute for Land, Water and Society
> Charles Sturt University
> PO Box 789
> Albury NSW 2640
> M: +61 424 451 858?
>
>
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