geneFlowTinv creates gene flow matrix (T)
and its inverse (Tinv), while
gameteFlowM creates gamete flow
mendelianSamplingD creates a mendelian sampling
covariance matrix (D).
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logical, for the computation the pedigree needs to be sorted, but results are sorted back to original sorting (sort=TRUE) or not (sort=FALSE)
logical, should returned matrix have row/colnames; this can be used to get leaner matrix
logical, should returned value be a diagonal matrix or a vector
arguments for other methods
geneFlowT returns a matrix with coefficients that show the flow
of genes from one generation to the next one etc.
simply the inverse of
geneFlowT, but calculated as I - M,
where M is gamete flow matrix with coefficients that represent
parent gamete contribution to their offspring.
is another matrix (D) for construction of relationship additive
matrix via decomposition i.e. A=TDT' (Henderson, 1976). Mrode
(2005) has a very nice introduction to these concepts.
Take care with
sort=FALSE, names=FALSE. It is your own
responsibility to assure proper handling in this case.
Matrices of n * n dimension, with coeficients as described in the
details, where n is number of subjects in
Henderson, C. R. (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1):69-83
Mrode, R. A. (2005) Linear models for the prediction of animal breeding values. 2nd edition. CAB International. ISBN 0-85199-000-2 http://www.amazon.com/gp/product/0851990002
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if(require(gdata)) data(Mrode2.1) Mrode2.1$dtB <- as.Date(Mrode2.1$dtB) x2.1 <- Pedigree(x=Mrode2.1, subject="sub", ascendant=c("fat", "mot"), ascendantSex=c("M", "F"), family="fam", sex="sex", generation="gen", dtBirth="dtB") fractions(geneFlowT(x2.1)) fractions(geneFlowTinv(x2.1)) fractions(gameteFlowM(x2.1)) mendelianSamplingD(x2.1)
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