Hello,

thank you Jarrod for clarifying things, I obviously made a mistake with the
nu value. I was also wondering - what is the interpretation of idh(1+X):Y
in case X is a dichotomous variable?

Cheers,
sz.


On 14 November 2013 18:23, Jarrod Hadfield <j.hadfield@ed.ac.uk> wrote:

> Hi Maria,
>
> random = ~us(1):subj_ID + us(fin_B):subj_ID  is the same as
> idh(1+finB):subj_ID and is not the same as (1 + fin_B|subj_ID) in lmer
> because the idh fixes the covariance between and intercepts and slopes to
> zero.  us(1+finB):subj_ID is equivalent to the lmer code.
>
> Also, a prior of nu=2 (or nu=2.002) could have quite an influence on your
> posterior. V=diag(2) and nu=1.002 for the 2x2 case gives a marginal prior
> on the variances that is equivalent to an inverse gamma with shape and
> scale equal to 0.001. This prior used to be used a lot, but has some bad
> properties when values close to zero have support. I often use the
> parameter expanded prior:
>
> list(V=diag(2), nu=2, alpha.mu=c(0,0), alpha.V=diag(2)*a)
>
> where a is something large.
>
> There is no difference between nu and n.
>
> Cheers,
>
> Jarrod
>
>
> I meant (1 + fin_B|subj_ID), as indicated in glmer() (lme4 package).
>
>> And this should be indicated in MCMCglmm() as random = ~us(1):subj_ID +
>> us(fin_B):subj_ID.
>>
>
>
>  Quoting Maria Paola Bissiri <Maria_Paola.Bissiri@tu-dresden.de> on Thu,
> 14 Nov 2013 15:06:26 +0000:
>
>  Dear Szymek,
>> thank you very much for your answer.
>>
>> Yes, the random effects were indicated wrongly in MCMCglmm! My intention
>> is of course to look at variance associated with subjects (subj_ID).
>> I meant (1 + fin_B|subj_ID), as indicated in glmer() (lme4 package).
>> And this should be indicated in MCMCglmm() as random = ~us(1):subj_ID +
>> us(fin_B):subj_ID.
>> Please, correct me if I am wrong.
>>
>> So the model runs with:
>> k <- length(levels(fallmid$resp_X))
>> I <- diag(k-1)
>> J <- matrix(rep(1, (k-1)^2), c(k-1, k-1))
>> prior <- list(R = list(fix = 1, V = 0.5 * (I+J), n = 2),
>>                  G = list(G1 = list(V = diag(1), n = 2), G2 = list(V =
>> diag(2), n = 2)))
>>
>> fallmid.MCMCglmm <- MCMCglmm(resp_X ~ lang * ini_pch + lang * manner +
>> lang * fin_B,
>>                              random = ~us(1):subj_ID + us(fin_B):subj_ID,
>>                              family="categorical", data=fallmid,
>>                              prior=prior
>>                              )
>>
>> In your suggestion you indicate nu=2.002. What does "nu" mean? What is
>> the difference between nu and n? In the MCMCglmm manual and in the tutorial
>> they are both defined as "degrees of belief". What does this mean?
>>
>> Kind regards,
>> Maria Paola
>>
>>
>>
>>
>>
>>
>>
>> Zitat von Szymek Drobniak <geralttee@gmail.com>:
>>
>>  Dear Maria,
>>>
>>> I'm not sure what exactly you're trying to test with your model, but to
>>> start with - your prior specification assumes 2 random effects, and your
>>> model has only one (a structured covariance matrix with fin_B defined as
>>> a
>>> random effect). This specification you've provided is similar to a random
>>> intercept/slope model - but I can't see why you would like to fit it
>>> (most
>>> importantly, you assumed that fin_B is both a fixed and random effect).
>>> If
>>> your intention was to look at variance associated with subjects
>>> (subj_ID),
>>> and you'd like to see if this variance is heterogeneous for different
>>> levels of fin_B - you could fit:
>>>
>>> MCMCglmm(your_fixed_formula_here, random=~us(fin_B):subj_ID, ...)
>>>
>>> and the prior would be (assuming fin_B has 2 levels as you've said)
>>>
>>> list(R=list(V=1, fix=1), G=list(G1=list(V=diag(2),nu=2.002)))
>>>
>>> that's for start, then have a look at mcmc-series plots to see if it
>>> mixes
>>> well and tweak your model further if necessary.
>>>
>>> Cheer
>>> szymek
>>>
>>
>> --
>> Dr. Maria Paola Bissiri
>>
>> TU Dresden
>> Fakultät Elektrotechnik und Informationstechnik
>> Institut für Akustik und Sprachkommunikation
>> 01062 Dresden
>>
>> Barkhausen-Bau, Raum S54
>> Helmholtzstraße 18
>>
>> Tel: +49 (0)351 463-34283
>> Fax: +49 (0)351 463-37781
>> E-Mail: Maria_Paola.Bissiri@tu-dresden.de
>> http://wwwpub.zih.tu-dresden.de/~bissiri/index.htm
>>
>> _______________________________________________
>> 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.
>
>
>


-- 
Szymon Drobniak, PhD. || Population Ecology Group
Institute of Environmental Sciences, Jagiellonian University
ul. Gronostajowa 7, 30-387 Kraków, POLAND
tel.: +48 12 664 51 79 fax: +48 12 664 69 12
szymek.drobniak@uj.edu.pl
www.eko.uj.edu.pl/drobniak

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