modeP {MasterBayes} | R Documentation

## Posterior Mode of Parents

### Description

Finds the mode of the posterior marginal distribution of parents

### Usage

    modeP(postP, threshold=0, ...)

### Arguments

`postP` | posterior distribution of parentage from an `MCMCped` model with argument `write_postP="MARGINAL"`
`threshold` | threshold probability under which ML parents are replaced by NA
`...` | further arguments to be passed

### Details

Individuals that do not have a parent assignment with a posterior probability exceeding the `threshold`, or whose parents belong to the base or unsampled population, have NA in the dan and sire columns. Please bear in mind that the mode of the marginal distribution may be different from the mode of the joint distribution. For example the male that has the highest marginal probability (marginal with respect to potential mothers) may not be the male that is in the parental category (i.e. dam/sire combination) with the highest probability. With multigenerational pedigrees, the mode of the joint distribution of parental combinations may not coincide with the mode of the distribution of pedigrees!

### Value

pedigree with id in the first column, and ML estimates for dam and sire in the second and third columns

### Author(s)

Jarrod Hadfield j.hadfield@sheffield.ac.uk

### References

Hadfield J.D. et al, Molecular Ecology

### See Also

`MCMCped`

### Examples

    data(WarblerP)
    data(WarblerG)
    GdP<-GdataPed(WarblerG)

    var1<-expression(varPed(c("lat", "long"), gender="Male",
      relational="OFFSPRING"))

    # paternity is to be modelled as a function of distance
    # between offspring and male territories

    res1<-expression(varPed("offspring", restrict=0))

    # indivdiuals from the offspring generation are excluded as parents

    res2<-expression(varPed("terr", gender="Female", relational="OFFSPRING",
      restrict=TRUE))

    # mothers not from the offspring territory are excluded

    PdP<-PdataPed(formula=list(var1,res1,res2), data=WarblerP, USsire=FALSE)
    tP<-tunePed(beta=30)

    model1<-MCMCped(PdP=PdP, GdP=GdP, tP=tP, nitt=3000, thin=2, burnin=1000)

    ped<-modeP(model1$P, threshol=0.9)
    ped

[Package MasterBayes version 2.0 Index]