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optimizer_numericalintegration <-
function(listdata,fMP,zeroset,starting,K,coreNumber) {
npairs = length(listdata);
## points and weights in 3d for Gauss-Hermite Quadrature
PointsWK = ghpoints3(K=K);
if(coreNumber == 1) {
OutputK = bobyqa(par=starting$initial,fn=likelihood,
lower=starting$lower,upper=starting$upper,control=list(maxfun=3000),
paramnames=starting$paramnames,zeroset=zeroset,listdata=listdata,
fMP=fMP, PointsW=PointsWK);
estimates = OutputK$par;
names(estimates) = starting$paramnames;
loglike = - OutputK$fval;
BIC = -2*loglike + length(estimates)*log(npairs);
hessfit = fdHess(pars=estimates,fun=likelihood,paramnames=starting$paramnames,zeroset=zeroset,
listdata=listdata,fMP=fMP,PointsW=PointsWK);
hessianmatrix = hessfit$Hessian; colnames(hessianmatrix) = starting$paramnames;
gradient = hessfit$gradient;
} ## end of if(coreNumber == 1)
if(coreNumber > 1) {
cluster = makeCluster(coreNumber);
OutputK = bobyqa(par=starting$initial,fn=likelihoodcluster,
lower=starting$lower,upper=starting$upper,control=list(maxfun=3000),
paramnames=starting$paramnames,zeroset=zeroset,listdata=listdata,
fMP=fMP,PointsW=PointsWK,cl=cluster);
estimates = OutputK$par; names(estimates) = starting$paramnames;
loglike = - OutputK$fval;
BIC = -2*loglike + length(estimates)*log(npairs);
hessfit = fdHess(pars=estimates,fun=likelihoodcluster,paramnames=starting$paramnames,zeroset=zeroset,
listdata=listdata,fMP=fMP,PointsW=PointsWK,cl=cluster);
hessianmatrix = hessfit$Hessian; colnames(hessianmatrix) = starting$paramnames;
gradient = hessfit$gradient;
stopCluster(cluster);
} ## end of if(coreNumber > 1)
return(list(loglikelihood=loglike, BIC=BIC, par=estimates, hess=hessianmatrix, gradient=gradient));
}
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