beta.loglik {MasterBayes} | R Documentation

## Log-Likelihood of Beta

### Description

Log-likelihood of beta given a pedigree and phenotypic data. Beta is the paramater vector for the multinomial log-linear model. Intended to be used within the function `MLE.beta`

### Usage

    beta.loglik(X, dam_pos=NULL, sire_pos=NULL, par_pos=NULL, beta=NULL,
       beta_map=NULL, merge=NULL, mergeN=NULL, nUS=c(0,0), ...)

### Arguments

`X` | list of design matrices for each offspring. Each element should either have dam (D) and/or sire (S) matrices, or a composite Dam/Sire (DS) matrix. See `varPed` for model types
`dam_pos` | position of each offspring's mother in the dam design matrix
`sire_pos` | position of each offspring's mother in the sire design matrix
`par_pos` | position of each offspring's parents in the composite dam/sire matrix
`beta` | parameter vector
`beta_map` | vector that maps `beta` onto the design matrices (see `getXlist`)
`merge` | optional vector that indicates columns of for which the parameter is transfomed using the argument `merge` in varPed
`mergeN` | optional list of matrices for each offspring the columns of which refer to merged variables and the rows to the number of individuals that fall into each category defined by `merge`)
`nUS` | vector of the number of unsampled females and males, respectively. Only required if unsampled individuals have known phenotype.
`...` | further arguments to be passed

### Value

log-likelihood of beta given the pedigree and X.

### Note

Intended to be used within `MLE.beta`

### Author(s)

Jarrod Hadfield j.hadfield@sheffield.ac.uk

### References

Hadfield J.D. et al (2006) Molecular Ecology 15 3715-31 Smouse P.E. et al (1999) Journal of Evolutionary Biology 12 1069-1077

### See Also

`MLE.beta`, `MCMCped`, `varPed`, `getXlist`

### Examples

    data(WarblerP)
    data(WarblerG)

    GdP<-GdataPed(WarblerG)

    res1<-expression(varPed("offspring", relational=FALSE, restrict=0))
    var1<-expression(varPed(c("lat", "long"), gender="Male",
      relational="OFFSPRING"))
    res2<-expression(varPed("terr", gender="Female", relational="OFFSPRING",
      restrict=TRUE))

    PdP<-PdataPed(formula=list(var1,res1,res2), data=WarblerP)

    # probability of paternity is modelled as a function of  distance

    X.list<-getXlist(PdP=PdP, GdP=GdP)

    ped<-MLE.ped(X.list)

    # get ML pedigree from genetic data alone

    X<-lapply(X.list$X, function(x){list(S=x$XSs)})

    # Extract Design matrices for Sires

    sire_pos<-match(ped[,3][as.numeric(names(X))], X.list$id)
    sire_pos<-mapply(function(x,y){match(x, y$sire.id)}, sire_pos, X.list$X)

    # row number of each design matrix correspoding to the ML sire.

    beta<-seq(-0.065,-0.0325, length=100)
    beta_Loglik<-1:100
      for(i in 1:100){
         beta_Loglik[i]<-beta.loglik(X, sire_pos=sire_pos, beta=beta[i],
         beta_map=X.list$beta_map)
      }

    plot(beta_Loglik~beta, type="l", main="Profile Log-likelihood for beta")

[Package MasterBayes version 2.0 Index]