Packages kinship, lmm and pan were ported from S-PLUS packages by Beth Atkinson, Terry Therneau and Joseph L Schafer, while tdthap was based on a version of R package by David Clayton; you can always send your questions directly to these authors.
- Q. Is there anything wrong with the code in gap, that gc.em and hap.em give me codes hap1code and hap2code that do not make sense to me?
- A. Apparently no, they are the unique haplotype identifiers, mixed-radixed numbers based on alleles at each marker locus. However, if you do want to find out how they are formed, there is an internal function revhap(loci,id) to unfold the alleles. For instance, cat(revhap(c(2,2,3),5)) gives values 1,2,2, for with two biallelic and a triallelic loci, the natural sequence of haplotypes are 111,112,113,121,122, with1,2,2 being the fifth.
- Q. When I run coxme in kinship package, there is an error message saying function "coxph.wtest not found"; can I fix this?
- A. This is to do with survival package, which contains the
function in R 1.8.1 but not R 1.9.x. Ideally this will be fixed in
the next version of survival. In case this problem remains, you
can try the following tricks if you use Windows:
- Start R 1.8.1, issue command library(survival), sink("c:/coxph.wtest.R"), coxph.wtest, sink()
- Start R 1.9.x, issue command coxph.wtest <- source("c:/coxph.wtest.R")
- Q. Are there any examples of using pan and **kinship**?
A. The examples are distributed with the source packages (inst/tests), and tests directory for the installed packages. More references about pan are as follows,
Schafer JL (2001). Multiple imputation with PAN. In Sayer AG, Collins LM (Eds.), New methods for the analysis of change (pp. 357–377). American Psychological Association, Washington, DC.
Schafer JL, Graham JW (2002). Missing data: our view of the state of the art. Psychological Methods. 7:147-177
Schafer J L, Yucel RM (2002). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics. 11:437-457
Demirtas H (2004). Simulation driven inference for multiply imputed longitudinal datasets. Statistica Neerlandica 58:466-482
- Q. I am using IBD information from SOLAR with kinship, but coxme stops due to nonpositive definite matrices.
- A. There is a message from Terry Therneau attached below.
Date: Sat, 13 Nov 2004 08:13:58 -0600 (CST)
From: Terry Therneau <firstname.lastname@example.org>
Subject: Re: kinship
We also ran into the problem of non-positive-definite matrices from SOLAR.
And it is true-- the IBD matrices that it produces are not postitive definite.
We had 2 solutions:
1. Realize that coxme will be happy as long as the overall variance matrix
for the random effects is positive-definite. This can be guarrantteed with
just a little bit on the diagonal:
> kmat <- makekinship( ) however it was created
> smat <- bdsmatrix.ibd(.… data from SOLAR
> imat <- bdsI(dimnames(kmat)[], kmat$blocksize) #identity matrix
> coxme( ....…, varlist=list(imat, kmat, smat), variance=c(.01,0,0) )
This adds .01 to the diagonal of the overall variance matrix, and keeps the
matrix positive definite.
2. Get our IBD matrices from simwalk, which produces correct ones.
Note that the error in SOLAR is small roundoff ones. As long as the
variance component of the kinship matrix is moderate, it overcomes this.
But if the program ever, sometime in searching for the solution, tried one
that had the variance for kmat approx 0 (less than the size of this round
off error, about .001), then the Cholesky decomp of the overall variance fails
and the program dies.
We found that adding a very little bit of diagonal worked, but didn't ever
get around to "proving" that it should, or how much you really need. In
linear models this problem doesn't occur, because the error variance adds
enough to the diagonal.
Date last changed 26/1/2014