[R-sig-ME] design matrices in MCMCglmm

[Jarrod Hadfield]

Hi,

Have columns mate_1, mate_2 ... mate_n where n is group size. In each  
column have the identity of each cage mate (order does not matter).  
Make sure each column has the same factor levels even if they don't  
appear. For example,

factor(mate_1, levels=all.ids)

where all.ids are all possible cage mates. Then fit:

random=~mm(mate_1+mate_2+...mate_n):animal

where animal is linked to the pedigree through the ginverse argument.

Cheers,

Jarrod



Quoting Alexandre Martin <alexandre.m.martin at gmail.com> on Tue, 31 Mar  
2015 10:07:34 -0400:

> 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
>>>
>>
>>
>
>


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