a resampled version of the CCCD classifier.
juggle(data, classes, sampled = TRUE, sample.dim = FALSE,
num = 100, sample.proportion = 0.1, k = 2, method = NULL)
juggle.classify(data,J,tdata,indices)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.
class labels.
whether the data are subsampled.
if TRUE, the dimensions (variates) are also sampled.
number of juggles (resamples).
proportion of the data to sample. If 1 or greater, the data are sampled with replacement.
number of variates to sample when sample.dim is TRUE.
the juggled classifier.
the indices of the juggles to use.
the method used for the distance.
See dist
juggle.classify returns a matrix holding the classification
probabilities for each observation in data.
a list consisting of:
the dominating sets.
the radii.
the dimension of the data.
in the case of sample.dim=TRUE, the variables sampled
each time.
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.