eval.bagg {delt}R Documentation

Returns a bootstrap aggregation of adaptive histograms


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


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

See Also

lstseq.bagg, eval.cart, eval.greedy



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<-50       # the number of histograms in the average



[Package delt version 0.8.0 Index]