Description Usage Arguments Value Author(s) References Examples
Selection of number of clusters via modelbased or modelfree, normalized or unnormalized clustering instability.
1 2 
data 
a n x p data matrix of type numeric. 
kseq 
a vector with considered numbers clusters k > 1 
nB 
an integer specifying the number of bootstrap comparisons. 
norm 
logical specifying whether the instability path should be normalized. If TRUE, the instability path is normalized, accounting for a trivial decrease in instability due to a increasing k (see Haslbeck & Wulff, 2016). 
predict 
boolean specifying whether the modelbased or the modelfree variant should be used (see Haslbeck & Wulff, 2016). 
method 
character string specifying the clustering algorithm. 'kmeans' for the kmeans algorithm, 'hierarchical' for hierarchical clustering. 
linkage 
character specifying the linkage criterion, in case

kmIter 
integer specifying the the number of restarts of the kmeans algorithm in order to avoid local minima. 
pbar 
logical 
a list that contains the optimal k selected by the unnormalized and normalized instability method. It also includes a vector containing the averaged instability path (over bootstrap samples) and a matrix containing the instability path of each bootstrap sample for both the normalized and the unnormalized method.
Dirk U. Wulff <dirk.wulff@gmail.com> Jonas M. B. Haslbeck <jonas.haslbeck@gmail.com>
BenHur, A., Elisseeff, A., & Guyon, I. (2001). A stability based method for discovering structure in clustered data. Pacific symposium on biocomputing, 7, 617.
Tibshirani, R., & Walther, G. (2005). Cluster validation by prediction strength. Journal of Computational and Graphical Statistics, 14(3), 511528.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Not run:
# Generate Data from Gaussian Mixture
s < .1
n < 50
data < rbind(cbind(rnorm(n, 0, s), rnorm(n, 0, s)),
cbind(rnorm(n, 1, s), rnorm(n, 1, s)),
cbind(rnorm(n, 0, s), rnorm(n, 1, s)),
cbind(rnorm(n, 1, s), rnorm(n, 0, s)))
plot(data)
# Selection of Number of Clusters using Instabilitybased Measures
stab_obj < cStability(data, kseq=2:10)
print(stab_obj)
## End(Not run)

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