Description Usage Arguments Details Value Author(s) See Also

This function produces a list that is used as input to `treeClust`

to determine which items are preserved in the output.

1 2 3 |

`return.trees` |
If TRUE, all the trees that go into the object are returned. This can make the treeClust object very large. Default FALSE. |

`return.mat` |
If TRUE, return a matrix describing leaf membership. Default TRUE. |

`return.dists` |
If TRUE, return an object of class 'dissimilarity' giving all pairwise distances between observations. This can be very large for large datasets. Default FALSE. |

`cluster.only` |
If TRUE, return only the clustering vector, which names the cluster into which each observation is places. Default FALSE. |

`return.newdata` |
If TRUE, return a numeric matrix describing leaf membership and/or inter-point distance (see "Details"). Default FALSE. |

`serule` |
Describes how to prune the rpart trees. By default, each tree is pruned to the minimum error size. With serule > 0, each tree is pruned to the smallest size for which the cross-validated error is less than (min error) + (serule * sds). |

`DevRatThreshold` |
Trees whose deviance ratio is greater than this number are presumed to have arisen from redundant variables. The predictor at the tree's root is dropped, a new tree built, and the new deviance ratio computed. this process is repeated until the resulting tree has deviance ratio less than or equal to the threshold. Default: 1 (do not drop any such trees). |

`parallelnodes` |
Describes whether to use parallel processing by creating a "computing cluster" containing "parallelnodes" nodes. If that number is = 1 no cluster is created. Here "cluster" is referring to a set of nodes operating in parallel, not to the clustering of the data. |

`...` |
Other arguments, passed onto the output. |

The "newdata" item is a numeric matrix that gives inter-point distances whose form depends on the "d.num" argument to treeClust(). When d.num = 1, each tree contributes a set of 0-1 dummy variables that serve as leaf membership indicators, and with d.num = 2, each tree's indicators are multiplied by that tree's "strength." With d.num = 3, a tree with k leaves contributes k-choose-2 columns, with the distances between distinct rows matching the d3 distances, and likewise with d.num = 4, a tree with k leaves produced k-choose-2 columns that have been weighted by tree strength.

list, with all the input arguments and their supplied or default values.

Sam Buttrey, buttrey@nps.edu

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