# [R-sig-ME] New version of MCMCglmm

Thu Nov 8 12:47:48 CET 2012

```Hi,

I've uploaded a new version of MCMCglmm to CRAN. Main additions are I)
more flexible methods for multi-membership and related models II) bug
fix for the predict function for certain types of random-effect
marginalisation.  III) sir models reinstated  IV) proposal
distribution for MH steps returned.

Cheers,

Jarrod

I) The addition of a linking.function that links different random
effects together. For example imagine two random terms, mother and
grandmother, for which some mothers also appear as grandmothers.
Denoting the associated random effect for the mothers (m) and
grandomothers (g)  we could:

a) fit the simple model ~mother+grandmother which estimates separate
variances (VAR(m) and VAR(g)) and sets the covariance to zero
(COV(m,g)=0)

b) use the linking function "str" to fit the model
~str(mother+grandmother) which estimates separate variances (VAR(m)
and VAR(g)) but also estimates the covariance (COV(m,g))

c) use the linking function "mm" to fit a multimembership model
~mm(mother+grandmother) which forces the variances to be equal
(VAR(m)= VAR(g)) and forces the correlation to be one
(COV(m,g)=VAR(m)= VAR(g)). Multi-membership models can still be fit
using idv(mult.memb(~mother+grandmother))

Terms within mm or str can be linked to a ginverse if the ginverse
list name corresponds to the first term in the linking.function (i.e.
ginverse=list(mother=A) in the models above). They can also be
interacted with variance functions (i.e.
us(sex):str(mother+grandmother) is possible)

II) The predict function did not obtain the correct contribution to
the variance from the marginalised random effects when us(function)
defined the marginalised terms and the function was such that a single
datum was associated with >1 term. For example, in a random regression
us(1+x) we have the variance for datum i as
V[1,1]+2*x[i]*V[1,2]+(x[i]^2)*V[2,2]  where V[1,1] is the variance in
intercept, V[2,2] is the variance in slopes and V[1,2] the covariance
between intercept and slope.  The term 2*x[i]*V[1,2] was omitted in
the older versions.  This may effect confidence intervals and fitted
values on the data scale for non-gaussian data, and prediction
intervals more generally.

III) sir models have gone back to a dense specification. This means
that big data sets may run out of memory when setting up the
equations, but at least it will run if this is not the case.

IV) Tune element in output gives the proposal distribution for the
latent variables that was used (after the adaptive phase).

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