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48 distcritmulti
distcritmulti
Distance based validity criteria for large data sets
Description
Usage
Approximates average silhouette width or the Pearson version of Hubert’s gamma criterion by hacking
the dataset into pieces and averaging the subset-wise values, see Hennig and Liao (2010).
distcritmulti(x,clustering,part=NULL,ns=10,criterion="asw",
fun="dist",metric="euclidean",
count=FALSE,seed=NULL,...)
Arguments
x
clustering
part
ns
Value
criterion
fun
metric
count
seed
cases times variables data matrix.
vector of integers indicating the clustering.
vector of integer subset sizes; sum should be smaller or equal to the number of
cases of x. If NULL, subset sizes are chosen approximately equal.
integer. Number of subsets, only used if part==NULL.
"asw" or "pearsongamma", specifies whether the average silhouette width or
the Pearson version of Hubert’s gamma is computed.
"dist" or "daisy", specifies which function is used for computing dissimilarities.
passed on to dist (as argument method) or daisy to determine which dissimilarity
is used.
logical. if TRUE, the subset number just processed is printed.
integer, random seed. (If NULL, result depends on random numbers.)
... further arguments to be passed on to dist or daisy.
A list with components crit.overall,crit.sub,crit.sd,part.
crit.overall
crit.sub
crit.sd
subsets
value of criterion.
vector of subset-wise criterion values.
standard deviation of crit.sub, can be used to assess stability.
list of case indexes in subsets.
Author(s)
Christian Hennig http://www.homepages.ucl.ac.uk/~ucakche