eval.bagg: Returns a bootstrap aggregation of adaptive histograms
In delt: Estimation of Multivariate Densities Using Adaptive Partitions

Description Usage Arguments Value Author(s) See Also Examples

Returns a bootstrap aggregation of CART-histograms or greedy histograms.

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eval.bagg(dendat, B, leaf, minobs = NULL, seed = 1, sample = "bagg", 
prune = "off", splitscan = 0, seedf = 1, scatter = 0, src = "c", 
method = "loglik")

`dendat`	n*d data matrix
`B`	positive integer; the number of aggregated histograms
`leaf`	the cardinality of the partitions of the aggregated histograms
`minobs`	non-negative integer; a property of aggregated histograms; splitting of a bin will be continued if the bin containes "minobs" or more observations
`seed`	the seed for the random number generation of the random selection of the bootstrap sample
`sample`	"bagg" or "worpl"; the bootstrapping method; "worpl" for the n/2-out-of-n without replacement; "bagg" for n-out-of-n with replacement
`prune`	"on" or "off"; if "on", then CART-histograms will be aggregated; if "off", then greedy histograms will be aggregated
`splitscan`	internal (how many splits will be used for random split selection)
`seedf`	internal (seed for random split selection)
`scatter`	internal (random perturbation of observations)
`src`	internal ("c" or "R" code)
`method`	"loglik" or "projec"; the empirical risk is either the log-likelihood or the L2 empirical risk

An evaluation tree

Jussi Klemela

lstseq.bagg, eval.cart, eval.greedy

library(denpro)
dendat<-sim.data(n=600,seed=5,type="mulmodII")

leaf<-7     # number of leaves in the histograms
seed<-1     # seed for choosing bootstrap samples
sample="worpl" # without-replacement bootstrap
prune="on"  # we use CART-histograms
B<-5        # the number of histograms in the average

eva<-eval.bagg(dendat,B,leaf,seed=seed,sample=sample,prune=prune)

dp<-draw.pcf(eva,pnum=c(60,60))
persp(dp$x,dp$y,dp$z,theta=-20,phi=30)