delt (version 0.8.2)

lstseq.bagg: Calculates a scale of bootstrap aggregated histograms

Description

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

Usage

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")

Arguments

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

Value

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

See Also

eval.bagg

Examples

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)