eval.bagg {delt} | R Documentation |

## Returns a bootstrap aggregation of adaptive histograms

### Description

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

### Usage

eval.bagg(dendat, B, leaf, 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 |

`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

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<-50 # 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)

[Package

*delt* version 0.8.0

Index]