Hi Bert,
That was distinctly unhelpful, and your outward hostility to a field you
obviously don't understand reveals a regrettable level of ignorance.
By the way, my research is Anthropology despite my job title.
Michelle
On Tue, May 1, 2018 at 2:48 PM, Bert Gunter <bgunter.4...@gmail.com> wrote:
> 1. (Mainly) Statistical issues are generally off topic on this list.
> You might want to try the r-sig-mixed-models list instead.
>
> 2. However, I think a better answer is to seek local statistical
> expertise in order to have an extended discussion about your research
> intent in order to avoid producing yet more irreproducible
> psychological research.
>
> Cheers,
> Bert
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Tue, May 1, 2018 at 2:15 PM, Michelle Kline
> <michelle.ann.kl...@gmail.com> wrote:
> > 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
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
--
Michelle A. Kline, PhD
Assistant Professor
Department of Psychology
Simon Fraser University
[[alternative HTML version deleted]]
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