beta.loglik | R Documentation |
Log-likelihood of beta given a pedigree and phenotypic data. Beta is the parameter 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), shrink=NULL)
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 |
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 |
merge |
optional vector that indicates columns of for which the parameter is transformed using the argument |
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 |
nUS |
vector of the number of unsampled females and males, respectively. Only required if unsampled individuals have known phenotype. |
shrink |
optional scalar for the variance defining the ridge-regression likelihood penalisation. |
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
## Not run: 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)$P # get ML pedigree from genetic data alone X<-lapply(X.list$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 corresponding 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") ## End(Not run)
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