popsize.loglik {MasterBayes} | R Documentation

## Log-Likelihood of Unsampled Population Size

### Description

Log-likelihood of the number of unsampled individuals given the genotypes of offspring and potential parents

### Usage

    popsize.loglik(X, USdam=FALSE, USsire=FALSE, nUS=NULL, ped=NULL, ...)

### Arguments

`X` | list for each offspring with elements `N` and `G`. `N` is a vector conatining the number of parental combinations in each of 4 classes. `G` is a vector conatining the sum of the Mendelian transition probabilities over parental combinations in each class. The 4 classes are parental combinations where a) both parents are sampled b) only sires are sampled, c) only dams are sampled d) neither parent is sampled.
`USdam` | logical or character; if `TRUE` a single undiferentiated population of unsampled females exists. if `USdam` is a character vector it must have the same length as `id` with factor levels representing sub-populations (in time or space) over which the number of unsampled females vary.
`USsire` | logical or character; if `TRUE` a single undiferentiated population of unsampled males exists. if `USsire` is a character vector it must have the same length as `id` with factor levels representing sub-populations (in time or space) over which the number of unsampled males vary.
`nUS` | vector for the size of the unsampled populations. Parmeters for the unsampled female populations come before the male populations.
`ped` | optional pedigree with id, dam and sire in ech column
`...` | Further arguments to be passed

### Value

log-likelihood of the number of unsampled individuals given the genotype data.

### Note

Intended to be used within `MLE.popsize`

### Author(s)

Jarrod Hadfield j.hadfield@sheffield.ac.uk

### References

Nielsen. R. et.al Genetics (2001) 157 4 1673-1682

### See Also

`MCMCped`, `MLE.popsize`

### Examples

    data(WarblerG)
    A<-extractA(WarblerG)

    sex<-c(rep("Male", 50), rep("Female", 100))
    offspring<-c(rep(0,100), rep(1, 50))
    terr<-as.factor(rep(1:50, 3))
    id<-1:150

    res1<-expression(varPed(x="offspring", restrict=0))
    res2<-expression(varPed(x="terr", gender="Female", relational="OFFSPRING",
      restrict=TRUE))

    test.data<-data.frame(id, sex, offspring, terr)

    PdP<-PdataPed(formula=list(res1, res2), data=test.data)

    simped<-simpedigree(PdP)
    G<-simgenotypes(A, E1=0, E2=0, ped=simped$ped, no_dup=1)

    # remove 25 males at random, leaving 25

    rm.males<-sample(1:50, 25, replace=FALSE)

    data.rm<-test.data[-rm.males,]
    GdPrm<-GdataPed(G=lapply(G$Gobs, function(x){x[-rm.males]}),
      id=G$id[-rm.males])

    # delete genotype and phenotype records

    PdPrm<-PdataPed(formula=list(res1, res2), data=data.rm, USsire=TRUE)

    X.listrm<-getXlist(PdP=PdPrm, GdP=GdPrm, A=A, E2=0)

    X<-lapply(X.listrm$X, function(x){list(N=c(25,0,1,0),
      G=c(sum(x$G[1:25]), 0, x$G[26], 0))})

    # each offspring has 1 mother and 25 sampled fathers so the 4 classes are:
    # a) 1*25 categories with both parents sampled, 0*25 categries with only
    # sires sampled b) 1*1 categories with only dams sired and 0*0 categories
    # with both sexes unsampled.

    nUS<-seq(10,40, length=100)
    nUS_Loglik<-1:100
    for(i in 1:100){
     nUS_Loglik[i]<-popsize.loglik(X, USsire=TRUE, nUS=nUS[i])
    }
    plot(nUS_Loglik~nUS, type="l", main="Profile Log-likelihood
       for number of unsampled males")

[Package MasterBayes version 2.0 Index]