Hi Jarrod,

Thanks for your response! I made the change you suggested and the model
stopped after 3000 iterations and asked for a stronger prior, I used n=1,
and it seemed to run ok after that.

I was wondering if I might impose on you to ask a follow-up question? My
response variable "grad.lin" are linear selection gradients and therefore
have directionality, but we are also interested in the magnitude. So we are
essentially investigating the potential effect of population size on
selection. I have read a number of previous articles and syntheses
regarding selection and understand that this requires the use of a folded
normal distribution. In fact, one of those articles was one that you
collaborated on with Michael Morrissey. I contacted Dr. Morrissey, and he
was kind enough to provide an example of functions that could be used to
obtain the corresponding values for the magnitude of selection once the
posterior mode and variance were obtained from an MCMCglmm model using the
selection gradients "straight up" as it were. The functions were as follows:

mu.fnorm<-function(mu,sigma){dnorm(mu,0,sigma)*2*sigma^2+mu*(2*pnorm(mu,0,sigma)-1)}
var.fnorm<-function(mu,sigma){mu^2+sigma^2-(sigma*sqrt(2/pi)*exp((-1*mu^2)/(2*sigma^2))+mu*(1-2*pnorm(-1*mu/sigma,0,1)))^2}

I assumed on my own that the value for the posterior mode of selection that
is plugged into the functions above is given for each level of "pop.bin" in
the object:

posterior.mode(model$Sol)

and that the posterior variance of selection to use in the functions comes
from the units variance (after the random effects "study" and "pop"  are
accounted for) given in:

posterior.mode(model$VCV)

In any case, to make a long story short, the reason why I was attempting to
use the prior with "R=list(V=diag(12),n=0.002)" is to obtain estimates of
units variance for the different levels of the interaction term for use in
the folded normal functions to obtain values for the magnitude of
selection. So the change to the model you provided works, but only one
overall estimate of units variance is given.

Anyway, perhaps this is not a problem if I am totally incorrect in the
assumption that units variance is what I'm looking for.

I checked the data and all three of the groups (fish, plants, and birds)
are represented in each of the pop.size bins though it's admittedly not an
even spread. For example, there is more data for birds than for fish or
plants in each of the bins.

Thanks again,
Jackie


On Thu, Feb 13, 2014 at 5:15 AM, Jarrod Hadfield <j.hadfield@ed.ac.uk>wrote:

> Hi Jackie,
>
> Unless you have a very large amount of data I would be careful about
> fitting 3 12x12 covariance matrices. However, that in itself should not
> generate the error.
>
> Does
>
> prior=list(R=list(V=diag(1),n=0.002),G=list(G1=list(V=diag(
> 12),n=0.002),G2=list(V=diag(12),n=0.002)))
>
> model=MCMCglmm(grad.lin~pop.bin*group,mev=mev,data=data,
> prior=prior,random=~idh(pop.bin*group):study+idh(pop.bin*
> group):pop,rcov=~units,nitt=300000,burnin=100000,thin=50)
>
> run? If so can you let me know. If not can you make sure that both pop.bin
> and group are coded as factors.
>
> Cheers,
>
> Jarrod
>
>
>
>
> Quoting Jackie Wood <jackiewood7@gmail.com> on Wed, 12 Feb 2014 18:04:22
> -0500:
>
>  Hello all,
>>
>> I am using MCMCglmm and attempting to create a model which allows
>> heterogeneous variances for different levels of a moderator variable where
>> the moderator is an interaction term. I attempted the run the following
>> model:
>>
>> prior=list(R=list(V=diag(12),n=0.002),G=list(G1=list(V=
>> diag(12),n=0.002),G2=list(V=diag(12),n=0.002)))
>>
>> model=MCMCglmm(grad.lin~pop.bin*group,mev=mev,data=data,
>> prior=prior,random=~idh(pop.bin*group):study
>> +idh(pop.bin*group):pop,rcov=~idh(pop.bin*group):units,nitt=
>> 300000,burnin=100000,thin=50)
>>
>> where "pop.bin" corresponds to bins of population size and has 4 levels,
>> and "group" has 3 levels (Fish, Birds, Plants).
>>
>> When I run the above model I receive the error:
>> Error in priorformat(if (NOpriorG) { :V is the wrong dimension for some
>> prior$G/prior$R elements4
>>
>> I tried running a simpler model with just "pop.bin" instead of the
>> interaction term and "V=diag(4)" in the prior, and it seems to run just
>> fine.
>>
>> I'm unsure whether the problem is simply too many levels and I perhaps
>> don't have enough data or statistical power to run this model or whether I
>> made a simple (or huge) error in the model or prior specification. If
>> anyone could shed some light on this it would be a huge help.
>>
>> Cheers,
>> Jackie
>>
>>
>> --
>> Jacquelyn L.A. Wood, MSc.
>> PhD Candidate
>> Biology Dept.
>> Concordia University
>> 7141 Sherbrooke St. West
>> Montreal, QC
>> H4B 1R6
>> Phone: (514) 293-7255
>> Fax: (514) 848-2881
>>
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>>
>> _______________________________________________
>> R-sig-mixed-models@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.
>
>
>


-- 
Jacquelyn L.A. Wood, MSc.
PhD Candidate
Biology Dept.
Concordia University
7141 Sherbrooke St. West
Montreal, QC
H4B 1R6
Phone: (514) 293-7255
Fax: (514) 848-2881

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