Duda-Hart test for whether a data set should be split into two clusters.
dudahart2(x,clustering,alpha=0.001)
data matrix or data frame.
vector of integers. Clustering into two clusters.
numeric between 0 and 1. Significance level (recommended to be small if this is used for estimating the number of clusters).
A list with components
p-value against null hypothesis of homogemeity.
ratio of within-cluster sum of squares for two clusters and overall sum of squares.
critical value for dh
at level alpha
.
FALSE
if the null hypothesis of homogemeity is
rejected.
see above.
1-alpha
-quantile of a standard Gaussian.
Duda, R. O. and Hart, P. E. (1973) Pattern Classification and Scene Analysis. Wiley, New York.
# NOT RUN {
options(digits=2)
set.seed(98765)
iriss <- iris[sample(150,20),-5]
km <- kmeans(iriss,2)
dudahart2(iriss,km$cluster)
# }
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