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

[Dena Paris]

Hi Jarrod,

Thanks for the very prompt reply! I’m still struggling with this a bit. My understanding was that by removing the global intercept (-1) all factor levels would be estimated, and if I have only one fixed effect, say Arthropod, this is exactly what happens. However, when I include the second fixed effect, Size, it returns estimates for all 16 levels of Arthropod, but estimates for only 2 of the 3 Size classes. So, in the case with two fixed effects and the global intercept removed, is there a way to calculate the estimate for the third Size class, or is it the level from which the other levels deviate? 

Thanks again for all your help.

Dena

> On 5 Dec. 2016, at 5:49 pm, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> 
> 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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