fillX.G | R Documentation |
This function is primarily intended for use within getXlist
, and fills in the design matrices of the model with the genetic likelihoods.
fillX.G(X.list, A, G, E1=0.005, E2=0.005, marker.type="MSW")
X.list |
list of design matrices for each offspring derived using |
A |
list of allele frequencies |
G |
list of genotype objects; rows must correspond to individuals in the vector |
E1 |
if Wang's (2004) model of genotyping error for co-dominant markers is used this is the probability of an allele dropping out. If CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers is used this parameter is not used. If Hadfield's (2009) model of genotyping error for dominant markers is used this is the probability of a dominant allele being scored as a recessive allele. |
E2 |
if Wang's (2004) or CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers are used this is the probability of an allele being miss-scored. In the CERVUS model errors are not independent for the two alleles within a genotype and so if a genotyping error has occurred at one allele then a genotyping error occurs at the other allele with probability one. Accordingly, |
marker.type |
|
list of design matrices of the form X.list
containing genetic likelihoods for each offspring.
If a GdataPed
object is passed to getXlist
then the genetic likelihoods will be calculated by default.
Jarrod Hadfield j.hadfield@ed.ac.uk
Marshall, T. C. et al (1998) Molecular Ecology 7 5 639-655 Kalinowski S.T. et al (2007) Molecular Ecology 16 5 1099-1106 Hadfield J. D. et al (2009) in prep
getXlist
## Not run: data(WarblerG) A<-extractA(WarblerG) ped<-matrix(NA, 5,3) ped[,1]<-1:5 ped[,2]<-c(rep(NA, 4), 1) ped[,3]<-c(rep(NA, 4), 2) genotypes<-simgenotypes(A, ped=ped) sex<-c("Female", "Male", "Female", "Male","Female") offspring<-c(0,0,0,0,1) data<-data.frame(id=ped[,1], sex, offspring) res1<-expression(varPed(x="offspring", restrict=0)) PdP<-PdataPed(formula=list(res1), data=data) GdP<-GdataPed(G=genotypes$Gobs, id=genotypes$id) X.list<-getXlist(PdP) # creates design matrices for offspring (in this case indivdiual "5") X.list.G<-fillX.G(X.list, A=A, G=genotypes$Gobs, E2=0.005) # genetic likelihoods are arranged sires within dams X.list.G$X$"5"$dam.id X.list.G$X$"5"$sire.id # so for this example we have parental combinations # ("1","2"), ("1","4"), ("3","2"), ("2","4"): X.list.G$X$"5"$G # The true parents have the highest likelihood in this case ## End(Not run)
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