Classical distancebased test for homogeneity against clustering. Test
statistics is number of isolated vertices in the graph of smallest
distances. The homogeneity model is a random graph model where ne
edges are drawn from all possible edges.
1  homogen.test(distmat, ne = ncol(distmat), testdist = "erdos")

distmat 
numeric symmetric distance matrix. 
ne 
integer. Number of edges in the data graph, corresponding to smallest distances. 
testdist 
string. If 
The "ling"test is onesided (rejection if the number of isolated vertices is too large), the "erdos"test computes a onesided as well as a twosided pvalue.
A list with components
p 
pvalue for onesided test. 
p.twoside 
pvalue for twosided test, only if 
iv 
number of isolated vertices in the data. 
lambda 
parameter of the Poisson test distribution, only if

distcut 
largest distance value for which an edge has been drawn. 
ne 
see above. 
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche
Erdos, P. and Renyi, A. (1960) On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5, 1761.
Godehardt, E. and Horsch, A. (1995) GraphTheoretic Models for Testing the Homogeneity of Data. In Gaul, W. and Pfeifer, D. (Eds.) From Data to Knowledge, Springer, Berlin, 167176.
Ling, R. F. (1973) A probability theory of cluster analysis. Journal of the American Statistical Association 68, 159164.
1 2 3 4 5  options(digits=4)
data(kykladspecreg)
j < jaccard(t(kykladspecreg))
homogen.test(j, testdist="erdos")
homogen.test(j, testdist="ling")

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