lstseq.bagg {delt} | R Documentation |

## 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 |

### Author(s)

Jussi Klemela

### See Also

`eval.bagg`

### Examples

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)

[Package

*delt* version 0.8.0

Index]