[R-sig-ME] transform glmer model into MCMCglmm: how to define the priors?
Jarrod Hadfield
j.hadfield at ed.ac.uk
Wed Mar 18 15:57:35 CET 2015
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.
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