[R-sig-ME] transform glmer model into MCMCglmm: how to define the priors?

[Anne Krause]

Dear Jarrod,
thanks for the link. The problem persisted until I renamed
the model. Now I can call the summary.

I cannot say it often enough: Thank you so much for your
help! In less than a day (!!!) I went from "never going to
solve this problem :-(" to "yeah! this is working! :-D".

Best, Anne 

On Wed, 18 Mar 2015 14:57:35 +0000
 Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> Hi,
> 
> This works fine on my computer and with old versions.  I
> think the problem is not due to MCMCglmm:
> 
>
http://stackoverflow.com/questions/21367922/error-in-getas-characterfun-mode-function-envir-envir
> 
> perhaps open a new session without history, and try
> running the script you sent me. Does the problem persist?
> 
> Jarrod
> 
> 
> Quoting Anne Krause
> <anne.krause at frequenz.uni-freiburg.de> on Wed, 18 Mar
> 2015 14:35:29 +0100:
> 
> > Dear Jarrod,
> > thank you so much for your quick reply and the prior
> which
> > finally got me started on a working model:
> >
> > k <- length(levels(imp$form))
> > IJ <- (1/k) * (diag(k-1) + matrix(1, k-1, k-1))
> >
> > prior<-list(
> >   R=list(V=IJ, fix=1),
> >   G=list(
> >     G1=list(V=diag(2), nu=2, alpha.mu=c(0,0),
> >    alpha.V=diag(2)*1000),
> >     G2=list(V=diag(2), nu=2, alpha.mu=c(0,0),
> > alpha.V=diag(2)*1000)))
> >
> >
> > model1 <- MCMCglmm(form~trait:frequency+trait:recency+
> >                  trait:year-1,
> >
>                  random=~us(trait):author+us(trait):verb,
> >                  rcov=~us(trait):units,
> >                  prior=prior,
> >                  data=imp, family="categorical",)
> >
> > The summary problem unfortunately remains the same. I
> have
> > attached the data and R script and hope that you (or
> > someone else on the list) can figure this out.
> >
> > Thanks a lot!
> >
> > Best, Anne
> >
> > On Wed, 18 Mar 2015 06:18:50 +0000
> >  Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> >> Hi,
> >>
> >> You will need priors for this model: the
> >> observation-level (units) effects are not
> identifiable.
> >> The random effect covariance structures are 2x2
> matrices.
> >> For these models I fix the residual covariance matrix
> at
> >>
> >> IJ <- (1/k) * (diag(k-1) + matrix(1, k-1, k-1))
> >>
> >> where k is the number of categories in the response:
> >>
> >>
> >> A starting prior might look something like:
> >>
> >> prior=list(R=list(V=IJ, fix=1),
> G=list(G1=list(V=diag(2),
> >> nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*1000)))
> >>
> >>
> >> Although I haven't seen it done (although I don't know
> >> the literature on multinomial models) it might make
> sense
> >> to have the random effect covarinace matrix
> proportional
> >> to IJ and estimate a single variance component (rather
> >> than a 2x2 covariance matrix). The rational behind the
> IJ
> >> matrix is that the propensity to belong to a category
> >> varies over random effects, but variation in the
> >> propensity is equal for all categories, and the
> >> propensity for two categories are uncorrelated. In
> >> non-multinomial models such an assumption would be
> >> represented by an identity matrix (i.e. trait:author)
> but
> >> for multinomial models this is a bit trickier because
> >> things are parameterised in terms of differences from
> a
> >> base-line category.
> >>
> >> The error you are getting does not look like it is a
> >> MCMCglmm issue. Could you post the data (or a
> >> reproducible example) so I can check?
> >>
> >>
> >> Cheers,
> >>
> >> Jarrod
> >>
> >>
> >>
> >>
> >> Quoting Anne Krause
> >> <anne.krause at frequenz.uni-freiburg.de> on Tue, 17 Mar
> >> 2015 18:06:48 +0100:
> >>
> >> > Dear list members,
> >> > I am a linguist who would like to use MCMCglmm in
> order
> >> to
> >> > model change in language morphology. I have a
> 3-level
> >> > dependent variable (unordered) which are 3
> realisations
> >> of
> >> > a morphological form in German. Say, I call them A,
> B,
> >> and
> >> > C - A and B share the same vowel, B and C share the
> >> > suffixation.
> >> > I did not know about MCMCglmm until recently;
> therefore
> >> I
> >> > worked with two glmer models, looking at vowel and
> >> > suffixation separately. However, I have been
> criticised
> >> for
> >> > the clumsy model interpretation, and I am sure that
> the
> >> > output of a MCMCglmm would be more straightforward
> and
> >> > convincing.
> >> >
> >> > My glmer looks like this:
> >> > model <- glmer(vowel~frequency+year+recency+
> >> >                   frequency*recency+
> >> >                   (1|verb)+(1|author), data=imp,
> >> >                   family=binomial)
> >> > - where “vowel” differentiates between AB on the one
> >> hand
> >> > and C on the other hand,
> >> > - “frequency” and “year” are numeric fixed variables
> >> and
> >> > “recency” a categorical fixed variable (7 levels),
> >> > - “verb” and “author” are categorical random
> variables
> >> >
> >> > The model for the dependent variable “suffix” is
> >> > practically the same (part of the criticism),
> “suffix”
> >> > distinguishing between A on the one hand and BC on
> the
> >> > other hand.
> >> >
> >> > Trying to transform this into an MCMCglmm, I managed
> to
> >> get
> >> > as far as this:
> >> > model2 <-
> MCMCglmm(form~trait:frequency+trait:recency+
> >> >                   trait:year-1,
> >> >                   random=~us(trait):author,
> >> >                   rcov=~us(trait):units,
> >> >                   data=imp, family="categorical",)
> >> > (“form” now distinguishing between all 3 three
> levels
> >> of
> >> > the dependent variable A, B, and C)
> >> >
> >> > I am well aware that this model is running without
> >> priors.
> >> > Whichever prior I tried gave me the error “V is the
> >> wrong
> >> > dimension for some prior$G/prior$R elements” and I
> have
> >> no
> >> > idea (after reading through the general description,
> >> the
> >> > tutorial, the course notes and entries in this
> mailing
> >> > list) how these priors are defined. I guess there is
> no
> >> > rule of thumb, but I hope this short explanation of
> my
> >> > variables is enough for someone of you to point out
> a
> >> > solution (or a starting point for me). I also need
> to
> >> > include the second random from above (“verb”) and
> the
> >> > interaction between “frequency” and “recency”.
> >> >
> >> > Even though model2 is running, I cannot call the
> >> summary
> >> > for it (error: “Error in get(as.character(FUN), mode
> =
> >> > "function", envir = envir) :   object 'C:\Users\Anne
> >> > Krause\some_directory.Rdata' of mode 'function' was
> not
> >> > found”), which probably has to do with the fact that
> I
> >> did
> >> > not include priors (?!).
> >> >
> >> > Thank you so much in advance for help and/ or
> comments,
> >> > pointers or the like!
> >> >
> >> > Best, Anne
> >> > ____________________________________
> >> >
> >> > Anne Krause
> >> > Research Training Group GRK DFG 1624
> >> > "Frequency Effects in Language"
> >> > University of Freiburg
> >> > Belfortstraße 18
> >> > 79098 Freiburg
> >> >
> >> > Phone: 0761/203-97670
> >> > frequenz.uni-freiburg.de/krause
> >> >
> >> > _______________________________________________
> >> > R-sig-mixed-models at r-project.org mailing list
> >> >
> >>
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >> >
> >> >
> >>
> >>
> >> --
> >> The University of Edinburgh is a charitable body,
> >> registered in
> >> Scotland, with registration number SC005336.
> >>
> >>
> >
> > ____________________________________
> >
> > Anne Krause
> > Research Training Group GRK DFG 1624
> > "Frequency Effects in Language"
> > University of Freiburg
> > Belfortstraße 18
> > 79098 Freiburg
> >
> > Phone: 0761/203-97670
> > frequenz.uni-freiburg.de/krause
> >
> 
> 
> 
> -- 
> The University of Edinburgh is a charitable body,
> registered in
> Scotland, with registration number SC005336.
> 
> 

____________________________________

Anne Krause
Research Training Group GRK DFG 1624
"Frequency Effects in Language"
University of Freiburg
Belfortstraße 18
79098 Freiburg

Phone: 0761/203-97670
frequenz.uni-freiburg.de/krause