lstseq.bagg: Calculates a scale of bootstrap aggregated histograms
In delt: Estimation of Multivariate Densities Using Adaptive Partitions

Description Usage Arguments Value Author(s) See Also Examples

Calculates a scale of bootstrap aggregated histograms. The estimates in the sequence are calculated with function "eval.bagg".

lstseq.bagg(dendat, B, lstree=NULL, level = NULL, 
maxleaf = NULL, leafseq = NULL, 
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
`maxleaf`	the maximal cardinality of the partitions of the histograms in the sequence
`lstree`	if NULL, then level set trees are not calculated
`level`	if NULL, then shape trees are not calculated; if positive number, then it is the level of the level sets for which the shape trees are calculated
`leafseq`	a vector giving the cardinalities 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

A list with components

`lstseq`	a list of level set trees
`pcfseq`	a list of piecewise constant functions
`stseq`	a list of shape trees
`hseq`	a vector of smoothing parameters corresponding to the estimates in the sequence; the smoothing parameter is the cardinality of the partitions of the aggregated histograms

Jussi Klemela

eval.bagg

library(denpro)
dendat<-sim.data(n=100,seed=1,type="mulmodII")

seed<-1        # seed for choosing bootstrap samples
sample="worpl" # without-replacement bootstrap
prune="on"     # we use CART-histograms
B<-2           # the number of histograms in the average

estiseq<-lstseq.bagg(dendat,B,maxleaf=10,lstree=TRUE,
         seed=seed,sample=sample,prune=prune)

mt<-modegraph(estiseq)

plotmodet(mt)

#scaletable(estiseq)