Greets,
I'm trying to use nlminb() to estimate the parameters of a bivariate normal
sample and during one of the iterations it passes a parameter vector to the
likelihood function resulting in an invalid covariance matrix that causes
dmvnorm() to throw an error. Thus, it seems I need to somehow communicate to
nlminb() that the final three parameters in my parameter vector are used to
construct a positive semi-definite matrix, but I can't see how to achieve this
using the constraint mechanism provided. Additional details are provided in the
code below.
Suggestions?
Best Regards,
Steven
Generate the data set I'm using:
mu<-c(1,5)
sigma<-c(1,2,2,6)
dim(sigma)<-c(2,2)
set.seed(83165026)
sample.full<-sample.deleted<-mvrnorm(50,mu,sigma)
delete.one<-c(1,5,13,20)
delete.two<-c(3,11,17,31,40,41)
sample.deleted[delete.one,1]<-NA
sample.deleted[delete.two,2]<-NA
missing<-c(delete.one,delete.two)
complete<-sample.deleted[!(is.na(sample.deleted[,1]) |
is.na(sample.deleted[,2])),]
deleted<-sample.deleted[missing,]
Try to estimate the parameters of the data set less the deleted values:
exact<-function(theta,complete,deleted){
one.only<-deleted[!(is.na(deleted[,1])),1]
two.only<-deleted[!(is.na(deleted[,2])),2]
u<-c(theta[1],theta[2])
sigma<-c(theta[3],theta[5],theta[5],theta[4])
dim(sigma)<-c(2,2)
-sum(log(dmvnorm(x=complete,mean=u,sigma=sigma)))-
sum(log(dnorm(one.only,u[1],sigma[1,1])))-
sum(log(dnorm(two.only,u[2],sigma[2,2])))
}
nlminb(start=c(0,0,1,1,0),objective=exact,complete=complete,deleted=deleted,control=list(trace=1))
Escape and it stops at Iteration 9 on my machine.
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