transform glmer model into MCMCglmm: how to define the priors?

Hi,

This works fine on my computer and with old versions.  I
think the problem is not due to MCMCglmm:

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