[R-sig-ME] incorporating a kinship matrix

[Pelle Ingvarsson]

Hi,

I'm trying to replicate the behavior of the lmekin function from the 
kinship package with lmer. So far I have arrived at the pedigreemm 
function, but I'm still not at the point where I want to go.

My basic problem is as follows:

With lmekin I can incorporate an arbitrary kinship matrix into the 
calculations (ie. not based on a known pedigree, but estimates using 
genetic markers) , which is useful if you're working with species where 
you don't have pedigree information (which I happen to do).

The "problem" right now is that I have multiple observations from the 
same (cloned) genotype that I would like to include into my modeling 
effort. I have previously handled this by calculating BLUPs for each 
genotype and have used these BLUPs in the lmekin analyses. However, 
treating the BLUPs observed variables seems a little like cheating since 
  a potentially substantial source of variation (variation among 
replicate measures of each clone) just gets "eliminated" in the process 
of calculating the BLUPs.

However, including multiple observations of the same genotype is not 
possible in lmekin, where only a single observation is allowed per entry 
in the kinship matrix. Running a model with multiple observations per 
genotype results in:

lmekin(budset~year+site+pos,data=bs,random=~1|clone,varlist=list(2*K),rescale=TRUE)
Error in lmekin(budset ~ year + site + pos, data = bs, random = ~1 | 
clone,  :  The random effect must be 1 per subject

(note: where K is the nxn kinship matrix, where n in the number of 
observed genotypes):

I have tried to fit such a model using lmer/pedigreemm, which I hoped 
would be able to do that, but I have not succeeded yet. It appears that 
including a kinship matrix directly is not an option in pedigreemm, have 
I understood that correctly? I know I can fit a model like this using 
ASREML, but I would prefer to stick to open source software (and R) if 
possible.

Any ideas or helpful pointers would be very much appreciated.

Sincerely,

-Pelle