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

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