[R-sig-ME] MCMCglmm R-structure problem in heteroskedastic model

Paul Johnson Paul.Johnson at glasgow.ac.uk
Thu Feb 3 02:01:41 CET 2011

Thanks very much for this Jarrod.

> The work around is to add the phantom parents to mfs.fam, assigning them
> anything for the fixed effects but having NA for the response.
> It should then run as intended.

Adding 2 phantom parents for every phenotyped parent (the unrelated "founders") with NA response worked perfectly.

Re your other suggestions...

> When the number of missing records is large it can even be more efficient
> (in terms of effective sample size per second) to do away with phantom parents
> and use a denser inverse A (specify nodes="TIPS" in the call to MCMCglmm).

This is very appealing, as the models have to be run for hours to thin out the autocorrelation in the animal variance(s), but I haven't managed to make it work. I don't understand it, or the explanation of the nodes argument in ?MCMCglmm.

The pedigree looks like this:

> pedigree
        ID           DAM          SIRE
   [1,] "Mother0001" NA           NA
[1346,] "Mother1477" NA           NA
[1347,] "Father0001" NA           NA
[2692,] "Father1477" NA           NA
[2693,] "0001"       "Mother0001" "Father0001"
[4038,] "1477"       "Mother1477" "Father1477"

Does doing away with phantom parents imply effectively using na.omit(pedigree) in place of pedigree? I've tried this, with nodes="TIPS", and get the error:
Error in inverseA(pedigree, nodes = nodes, scale = scale) :
  individuals appearing as dams but not in pedigree

> If some individuals in mfs.fam have not been phenotyped you can use prunePed
> to remove useless individuals (make sure make.base=TRUE).

Am I right in thinking that because each family is represented by only 3 rows in the pedigree above (e.g. all the offspring in family 1477 are represented by a single row, the last row), then there are no useless individuals in my pedigree?

Thanks for your help,

The University of Glasgow, charity number SC004401

More information about the R-sig-mixed-models mailing list