beta.loglik {MasterBayes} | R Documentation |
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
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), ...)
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 |
log-likelihood of beta given the pedigree and X.
Intended to be used within MLE.beta
Jarrod Hadfield j.hadfield@ed.ac.uk
Hadfield J.D. et al (2006) Molecular Ecology 15 3715-31 Smouse P.E. et al (1999) Journal of Evolutionary Biology 12 1069-1077
MLE.beta
, MCMCped
, varPed
, getXlist
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="==")) 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")