[R-sig-ME] design matrices in MCMCglmm

[Jarrod Hadfield]

Hi,

Sorry it should have been just:

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

and it is the mate_1, mate_2, ... mate_n that need to be linked to the  
A inverse.

Cheers,

Jarrod

Quoting Jarrod Hadfield <j.hadfield at ed.ac.uk> on Fri, 03 Apr 2015  
08:16:02 +0100:

> 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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Scotland, with registration number SC005336.