[R-sig-ME] MCMCglmm multivariate meta-analysis with covariance

Jarrod Hadfield j.hadfield at ed.ac.uk
Wed Oct 19 08:45:40 CEST 2016

Hi Jon,

If you have the covariance matrix for your observations, then take its 
inverse and store it in sparse format:

Cinv_sparse<-as(Cinv, "dgCMatrix")

where Cinv is the inverse in dense format. When you say multivariate do 
you mean something like an explicit bivariate response such that the 
fixed formula is of the form cbind(y_1, y_2)~...?  If so you need to 
organise your data in long format and pass a single response vector. You 
can include a variable that denotes whether the observation is y_1 or 
y_2 and use it like "trait", and include a variable that denotes the 
original row for the observation and use it like "units". If we call 
this second variable "row" then having fit "row" as a random effect, and 
pass the argument ginverse=list(row=Cinv_sparse) to MCMCglmm. You will 
also need to fix the "row" variance to one in the prior:

G1=list(V=1, fix=1)

Presumably covariances are only non-zero between observations from the 
same original row? If so make sure the sparse Matrix also represents 
this: numerical issues during inversion may cause zero entries to differ 
slightly from zero and hence not be represented as zero.



Then you can fit the term ~trait:units

On 19/10/2016 05:41, Jon Bischof wrote:
> I'm interested in fitting a multivariate meta-analysis model with
> correlated measurement error. This means fixing the error to a covariance
> matrix per row.
> I saw this post
> <https://stat.ethz.ch/pipermail/r-sig-mixed-models/2013q2/020180.html> on
> the mailing list about non-correlated outcomes, but the noise correlation
> is too large to ignore in my use case. Professor Hadfield implies in the
> post that it is possible but "complicated". Does anyone know how to do it?
> Thanks!
> Jon Bischof
> 	[[alternative HTML version deleted]]
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