Dear list users,

I'm attempting to run a bivariate model in MCMCglmm where I would like the
residual variance of one trait (lifetime reproductive success) to be zero,
while I would like the the other to vary across four quartiles of a
covariate (the individual variance component contains a random slope term).
This requires an 8x8 R structure, using rcov=idh, so that the residual
covariances are zero, and where 'odd' diagonals (1,1), (3,3) etc vary
freely (residual variances in trait 1, across four quartiles), and where
'even' diagonals (2,2), (4,4) etc (residual variances in LRS) are equal to
zero (or at least close to zero, because zero is not possible). I can fix
the residual covariances in LRS to one using:

(R = list (V = diag(8), n = 0.002, fix = c(0, 1, 0, 1, 0, 1, 0, 1)),

However, using a similar logic:

(R = list (V = diag(8), n = 0.002, fix = c(0, 0.0001, 0, 0.0001, 0, 0.0001,
0, 0.0001)),

does not result in these variance being fixed to 0.0001, but results in
them freely varying (although they are very small). I wonder if anyone,
through past experience, or through superior understanding of the general
logic of R, has any advice on how to achieve my desired model? Any thoughts
would be gratefully received.

With best wishes,
Adam



-- 
Adam Hayward
Post-Doctoral Research Associate
Department of Animal and Plant Sciences
Alfred Denny Building
University of Sheffield
Western Bank
Sheffield S10 2TN
UK
http://www.huli.group.shef.ac.uk/adam-personal.html
http://adhayward.wordpress.com/
https://twitter.com/adhayward18

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