# Returns a bootstrap aggregation of adaptive histograms

### Description

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

### Usage

1 2 3 |

### Arguments

`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 |

### Value

An evaluation tree

### Author(s)

Jussi Klemela

### See Also

`lstseq.bagg`

,
`eval.cart`

,
`eval.greedy`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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)
``` |

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