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 at 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 at 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 at 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
Fakult?t Elektrotechnik und Informationstechnik
Institut f?r Akustik und Sprachkommunikation
01062 Dresden
Barkhausen-Bau, Raum S54
Helmholtzstra?e 18
Tel: +49 (0)351 463-34283
Fax: +49 (0)351 463-37781
E-Mail: Maria_Paola.Bissiri at tu-dresden.de
http://wwwpub.zih.tu-dresden.de/~bissiri/index.htm