Package 'fpc' - open source solution for an Internet free, intelligent ...

22 cluster.stats
Usage
cluster.stats(d = NULL, clustering, alt.clustering = NULL,
noisecluster=FALSE,
silhouette = TRUE, G2 = FALSE, G3 = FALSE,
wgap=TRUE, sepindex=TRUE, sepprob=0.1,
sepwithnoise=TRUE,
compareonly = FALSE,
aggregateonly = FALSE)
Arguments
d
clustering
a distance object (as generated by dist) or a distance matrix between cases.
an integer vector of length of the number of cases, which indicates a clustering.
The clusters have to be numbered from 1 to the number of clusters.
alt.clustering an integer vector such as for clustering, indicating an alternative clustering.
If provided, the corrected Rand index and Meila’s VI for clustering vs.
alt.clustering are computed.
noisecluster
silhouette
logical. If TRUE, it is assumed that the largest cluster number in clustering
denotes a ’noise class’, i.e. points that do not belong to any cluster. These points
are not taken into account for the computation of all functions of within and
between cluster distances including the validation indexes.
logical. If TRUE, the silhouette statistics are computed, which requires package
cluster.
G2 logical. If TRUE, Goodman and Kruskal’s index G2 (cf. Gordon (1999), p. 62)
is computed. This executes lots of sorting algorithms and can be very slow (it
has been improved by R. Francois - thanks!)
G3
wgap
sepindex
sepprob
sepwithnoise
compareonly
aggregateonly
logical. If TRUE, the index G3 (cf. Gordon (1999), p. 62) is computed. This
executes sort on all distances and can be extremely slow.
logical. If TRUE, the widest within-cluster gaps (largest link in within-cluster
minimum spanning tree) are computed.
logical. If TRUE, a separation index is computed, defined based on the distances
for every point to the closest point not in the same cluster. The separation index
is then the mean of the smallest proportion sepprob of these. This allows to
formalise separation less sensitive to a single or a few ambiguous points. The
output component corresponding to this is sindex, not separation!
numerical between 0 and 1, see sepindex.
logical. If TRUE and sepindex and noisecluster are both TRUE, the noise
points are incorporated as cluster in the separation index computation. Also they
are taken into account for the computation for the minimum cluster separation
regardless of sepindex.
logical. If TRUE, only the corrected Rand index and Meila’s VI are computed
and given out (this requires alt.clustering to be specified).
logical. If TRUE (and not compareonly), no clusterwise but only aggregated
information is given out (this cuts the size of the output down a bit).