juggling {cccd} R Documentation

## Juggling

### Description

a resampled version of the CCCD classifier.

### Usage

```juggle(data, classes, sampled = TRUE, sample.dim = FALSE,
num = 100, sample.proportion = 0.1, k = 2, method = NULL)
juggle.classify(data,J,tdata,indices)
```

### Arguments

 `data,tdata` training data from which to build the classifier. In the case of `juggle.classify`, `tdata` is the training data and `data` is the test data. `classes` class labels. `sampled` whether the data are subsampled. `sample.dim` if TRUE, the dimensions (variates) are also sampled. `num` number of juggles (resamples). `sample.proportion` proportion of the data to sample. If 1 or greater, the data are sampled with replacement. `k` number of variates to sample when `sample.dim` is TRUE. `J` the juggled classifier. `indices` the indices of the juggles to use. `method` the method used for the distance. See `dist`

### Details

The idea of juggling is to sample the data, compute a CCCD classifier, then repeat. The resampling is controled by the two sampling variables, which basically determine whether the data are sampled with replacement, or whether a subsample is used. If `sample.dim` is TRUE, the variates are also sampled, with `k` indicating how many are sampled.

### Value

`juggle.classify` returns a matrix holding the classification probabilities for each observation in `data`. a list consisting of:

 `S ` the dominating sets. `R ` the radii. `dimension ` the dimension of the data. `vars` in the case of `sample.dim`=TRUE, the variables sampled each time.

Only the indicies into the training data are stored in `J`, which is why the classifier requires the original training data in `tdata`.

### Author(s)

David J. Marchette, david.marchette@navy.mil

`cccd`, `dist`