[R-sig-ME] New version of MCMCglmm

[Jarrod Hadfield]

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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