Classical distance-based test for homogeneity against clustering
Classical distance-based 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.
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 one-sided (rejection if the number of isolated vertices is too large), the "erdos"-test computes a one-sided as well as a two-sided p-value.
A list with components
p |
p-value for one-sided test. |
p.twoside |
p-value for two-sided 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. |
Erdos, P. and Renyi, A. (1960) On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5, 17-61.
Godehardt, E. and Horsch, A. (1995) Graph-Theoretic Models for Testing the Homogeneity of Data. In Gaul, W. and Pfeifer, D. (Eds.) From Data to Knowledge, Springer, Berlin, 167-176.
Ling, R. F. (1973) A probability theory of cluster analysis. Journal of the American Statistical Association 68, 159-164.
options(digits=4) data(kykladspecreg) j <- jaccard(t(kykladspecreg)) homogen.test(j, testdist="erdos") homogen.test(j, testdist="ling")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.