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

## Description

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.

## Usage

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

## Arguments

 `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

## Value

An evaluation tree

Jussi Klemela

## See Also

`eval.greedy`, `eval.stage.gauss`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```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) ```

delt documentation built on May 2, 2019, 3:42 p.m.