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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