[R-sig-ME] design matrices in MCMCglmm

[Alexandre Martin]

Hi Jarrod,

Thank you for your help.
My question is now extended to the subject of associative indirect 
genetic effects.

For example in this data set :
id    cage
a    1
b    2
c    1
d    1
e    2
f    2
g    2

cage is a grouping variable describing the composition of cages. For 
instance, individuals a,c,d live in cage 1.

Design matrix Z_cage typically produced by MCMCglmm should be:
      c1   c2
a    1    0
b    0    1
c    1    0
d    1    0
e    0    1
f    0    1
g    0    1
where phenotype of individuals {a, b, ..., g} are linked to cages 1 and 2.

Design matrix Z_mates, however, linking the phenotype of individual i to 
its cage' mates is:
      a    b    c    d    e    f    g
a    0    0    1    1    0    0    0
b    0    0    0    0    1    1    1
c    1    0    0    1    0    0    0
d    1    0    1    0    0    0    0
e    0    1    0    0    0    1    1
f    0    1    0    0    1    0    1
g    0    1    0    0    1    1    0

It is Z_cage that is given by default, whereas it is matrix Z_mates that 
should be used to predict associative effects.

Is it possible to force MCMCglmm to work with Z_mates instead of Z_cage?

Thanks again!

Alexandre

Le 2015-03-28 04:01, Jarrod Hadfield a écrit :
> Hi Alexandre,
>
> The design matrices should be identical for both effects (z_{ij}=1 if
> the jth individual is the mother of individual i). The difference is in
> the correlation structure of the random effects. For environmental
> maternal effects they are assumed iid (i.e. an identity matrix) but for
> the maternal genetic effects they are assumed to be proportional to the
> A matrix. inverseA will return the inverse of A if you pass it the
> pedigree. It is this inverse that is required for forming the MME.
>
> Cheers,
>
> Jarrod
>
>
>
>
>
>
> Quoting Alexandre Martin <alexandre.m.martin at gmail.com> on Fri, 27 Mar
> 2015 16:39:40 -0400:
>
>> Dear all,
>>
>> I am working on estimating maternal effects (genetic and environmental)
>> with MCMCglmm that is new for me.
>>
>> I am trying to apply to MCMCglmm what is shown in online Muir's course
>> notes made for SAS. Leanning on Henderson’s Mixed Model Equation, these
>> notes explain how to solve MME to predict random effects “by hand”.
>>
>> Here is my concern:
>>
>> I do not know how to extract the design matrices for a MCMCglmm model,
>> e.g. the relatedness matrix or the one for maternal genetic effects. I
>> want that to understand how the design matrices are constructed by
>> comparing them to what they are supposed to look like.  For instance,
>> the design matrix for maternal genetic effects should relate offspring
>> to all the individuals that are in the pedigree, whereas the design
>> matrix for maternal environmental effects should just relate offspring
>> to their mothers. Does such a difference exist when MCMCglmm constructs
>> its design matrices? If not, how to include such different matrices in
>> models?
>>
>>
>> Any help will be greatly appreciated. Thank you!
>>
>>
>> Alexandre
>>
>>     [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>