[R] Specifying priors in a multi-response MCMCglmm

Michelle Kline m|che||e@@nn@k||ne @end|ng |rom gm@||@com
Tue May 1 23:53:49 CEST 2018


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.4567 using 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.kline using 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]]
> >
> > ______________________________________________
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> > 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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