MCMCglmm update

Hi,

MCMCglmm has been updated to version 2.22. A lot of minor annoying  
bugs have been fixed, but as far as I am aware no major bugs have been  
found. Quite a bit of new functionality has been added:

1) Antedependence structures.

Structured antedependence models can now be fitted using the new  
variance structure ante[]. The suffix [] takes a number, giving the  
order of the antedependence model (e.g ante1 and ante2 give first and  
second order antedependence models), and the number can be prefixed by  
a '€˜c'€™ to hold all regression coefficients of  
the same order equal. The number can also be suffixed by a  
'v'€™ to hold all innovation variances equal. For example,  
antec2v has 3 parameters: a constant innovation variance, and two  
constant regression coefficients (one 1-lagged, and one 2-lagged).

Priors for antedependence structures allow priors to be placed  
directly on the regression parameters via a beta.mu (a vector of prior  
means) and a beta.V (a matrix of prior variances) element to the prior  
list

2) Path analysis.

Path analysis could be performed previously using the sir function,  
but it was cumbersome and did not work if all response variables were  
not Gaussian and completely observed. The path function is less  
flexible than the sir function, but it is easier to use and works with  
non-Gaussian data. Paths are allowed between observations within the  
same residual block, and  path(cause, effect, k) specifies which of  
the k variables affect each other. For example, if a three-response  
model was fitted then

cbind(a,b,c)~trait+path(1,2,3)+path(1,3,3), rcov=~us(trait):units

then states that a[i] determines b[i] and c[i].

3) Simulate

A simulate method now exists and can be used to simulate observations  
from a  model defined by a MCMCglmm object.

4) Predict

The predict method is now more complete and accepts new data

5)  Random effect - residual correlations

Random effect - residual correlations can now be fitted by specifying  
covu=TRUE in the prior specification for the residual structure. The  
set of residuals defined by this structure are allowed to covary with  
the random effects specified by the final random effect structure. If  
the residual (co)variance matrix is of dimension n, and the final  
random effect (co)variance matrix is of dimension m, then the residual  
prior specification must be of dimension n+m. The final random effect  
(co)variance matrix should not have a prior specification.

6) Random effect Bradley-Terry models

Bradley-Terry models without random effects could already be fitted in  
previous versions by simply taking the difference between the two  
opponents predictors (and potentially fixing the intercept at zero if  
no order effects were modelled). Random effects can now be fitted  
using the multimembership model formulation mm(opponent1-opponent2),  
which now allows a `-' as well as the traditional `+'.

Cheers,

Jarrod

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