R/pcf.greedy.kernel.R

In delt: Estimation of Multivariate Densities Using Adaptive Partitions

pcf.greedy.kernel<-function(dendat,h,leaf=round(dim(dendat)[1]/2),
minobs=NULL,type="greedy")
{
if (type=="old"){
  eva<-eval.greedy(dendat,leaf)
  pa<-partition(eva)
  fs<-fs.calc.parti(pa,dendat,h)
  pcf<-eva
  pcf$value<-fs
}

if (type=="prune"){
    bt<-densplit(dendat,minobs=minobs)
    treeseq<-prune(bt)
    eva<-eval.pick(treeseq,leaf=treeseq$leafs[1])  
    pa<-partition(eva)
    pcf<-eva
    fs<-fs.calc.parti(pa,dendat,h)
    pcf$value<-fs
}

if (type=="greedy"){
   pa<-densplitter(dendat,minobs=minobs)
   pcf<-list(down=pa$down,high=pa$high,grid=pa$grid,support=pa$support,recs=pa$recs)
   fs<-fs.calc.parti(pa,dendat,h)
   pcf$value<-fs
}

if (type=="dyadic"){
   pa<-densplitter(dendat,minobs=minobs,dyadic=TRUE)
   pcf<-list(down=pa$down,high=pa$high,grid=pa$grid,support=pa$support,recs=pa$recs)
   fs<-fs.calc.parti(pa,dendat,h)
   pcf$value<-fs
}

#if (type=="cpp"){
#   pa<-densplitter2(dendat,minobs=minobs)
#   pcf<-list(down=pa$down,high=pa$high,grid=pa$grid,support=pa$support,
#   recs=pa$recs)
#   fs<-fs.calc.parti(pa,dendat,h)
#   pcf$value<-fs
#}

return(pcf)
}