eval.stage: Returns a stagewise minimization estimate
In delt: Estimation of Multivariate Densities Using Adaptive Partitions

Description Usage Arguments Value Author(s) See Also Examples

Returns a stagewise minimization estimate. A stagewise minimization estimator is a convex combination of greedy histograms. The convex combination is constructed by a stagewise minimization of an empirical risk functional.

1 2	eval.stage(dendat, leaf, M, pis = NULL, mcn = dim(dendat)[1], minobs = NULL, seedi = 1, method = "projec", bound = 0)

`dendat`	n*d data matrix
`leaf`	the (maximal) number of rectangles in the partition of the greedy histograms
`M`	the number of histograms in the convex combination
`pis`	the vector of weights of the convex combination
`mcn`	the size of the Monte Carlo sample used in the numerical integration in calculating the empirical risk functional
`minobs`	non-negative integer; splitting of a bin of a greedy histogram will be continued if the bin containes "minobs" or more observations
`seedi`	the seed for the generation of the Monte Carlo sample
`method`	"loglik" or "projec"; the empirical risk is either the log-likelihood or the L2 empirical risk
`bound`	internal

An evaluation tree

Jussi Klemela

eval.greedy, eval.stage.gauss

library(denpro)
dendat<-sim.data(n=100,seed=5,type="mulmodII")
leaf<-13  # the number of leafs of the greedy histograms
M<-5      # the number of greedy histograms

pcf<-eval.stage(dendat,leaf=leaf,M=M)

dp<-draw.pcf(pcf,pnum=c(120,120))
persp(dp$x,dp$y,dp$z,ticktype="detailed",phi=25,theta=-120)