| lstseq.bagg {delt} | R Documentation |
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
dendat<-sim.data(n=200,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)