Hi all,
I previously emailed about a multinomial model, and after seeking some
additional help, realized that since my response/outcome variables are not
mutually exclusive, I need to use a multi-response model that is *not*
multinomial. I'm now trying to figure out how to specify the priors on the
multi-response model. Any help would be much appreciated.
My data look like this:
X other focal village present r teaching Opp_teacher
Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher
f_Age f_Ed Age Ed1 61 10202 10213 0 15 0.250000000
2 0 0 0 0 2
2 1 0 48 82 63 10203 10213 0 19
0.500000000 6 0 0 4
0 6 10 1 0 27 103 64 10204 10213
0 1 0.250000000 0 0 0 0
0 0 0 1 0 25 94 69 10206
10213 0 6 0.250000000 2 0 0
1 0 1 2 1 0 20 115
72 10207 10213 0 4 0.250000000 0 0
0 0 0 0 0 1 0
18 86 80 10210 10213 0 4 0.250000000 0
0 0 0 0 0 0
1 0 30 127 83 10211 10213 0 8 0.062500000 0
0 0 0 0 0
0 1 0 73 38 85 10212 10213 0 11 0.125000000
8 0 1 1 0
8 10 1 0 9 19 132 10403 10213 0 1
0.000976563 0 0 0 0
0 0 0 1 0 10 010 241 11703 10213
0 3 0.015625000 1 0 0 0
0 1 1 1 0 49 8
Columns Opp_teacher through Eval_Teacher are count data different possible
teaching behaviors that I have observed, with each row being a dyad. The
teaching types are not mutually exclusive. They can co-occur. This is why I
am using a multi-response model but not a multi-nomial model. Focals as
well as others can appear in more than one dyad, so I have included those
as random effects. The fixed effects in the model are r (relatedness) and
present (# observations together).
I've specified my model as follows:
m3.random.present.r <- MCMCglmm(cbind(Opp_teacher , Dir_teacher,
Enh_teacher, SocTol_teacher, Eval_teacher) ~ +present + r + trait -1,
random = ~ other + focal,
prior = prior.m3,
burnin = burn,
nitt = iter,
family =c("poisson","poisson","poisson","poisson","poisson"),
data = data,
pr=TRUE,
pl=TRUE,
DIC = TRUE,
verbose = FALSE)
The prior, prior.m3 is as follows:
prior.m3 <- list(R = list(V = diag(2), nu = 2),
G = list(G1 = list(V = diag(2), nu = 5),
G2 = list(V = diag(2), nu = 5),
G3 = list(V = diag(2), nu = 5),
G4 = list(V = diag(2), nu = 5),
G5 = list(V = diag(2), nu = 5)))
This is based on Hadfield's Course Notes, as well as some advice found in this
post
<https://stackoverflow.com/questions/40617099/mcmcglmm-binomial-model-prior>.
It's consistent with how I've specified priors for simpler models (with
single outcome variables), but I am obviously missing something that must
change with respect to the G-structures when using multiple responses,
because running the model results in the following error:
Error in MCMCglmm(cbind(Opp_teacher, Dir_teacher, Enh_teacher,
SocTol_teacher, : prior$G has the wrong number of structures
I am not sure what this error message refers to. My understanding is that
there should be 5 G-structures listed because I have 5 dependent variables.
(Trial & error suggests this isn't the meaning of the error message - a
different number of G-structures does not change the result). This suggests
the problem has to do with the rest of the G-structure code: I've set `V =
diag(2)` because there are two random effects.
I can't come up with any other rationale, despite having scoured the
internet for additional help.
Thanks,
Michelle
--
Michelle A. Kline, PhD
Assistant Professor
Department of Psychology
Simon Fraser University
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