concor_hca: Hierarchical clustering using CONCOR.
In aslez/concoR: CONvergence of iterated CORrelations

Description Usage Arguments Value References Examples

Paritions relational data using a CONvergence of iterated CORrelations (CONCOR) algorithm.

concor_hca(m0, cutoff = 0.999, max.iter = 25, p = 1)

concor(m0, cutoff = 0.999, max.iter = 50)

block_names(p_list)

`m0`	A list of n x n matrices, each of which refers to a relation on a set of vertices. Only one-mode data are supported at present.
`cutoff`	A value between 0 and 1 used to determine convergence.
`max.iter`	An integer representing the maximum number of iterations.
`p`	An integer representing the desired number of partitions.

A data.frame depicting the block assignment for each vertex. Rows are sorted to match the order in which vertices appear in the original data. The column vector indicating block membership can be passed directly to the blockmodel command in sna.

Breiger, R.L., Boorman, S.A., and Arabie, P. 1975. An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling. Journal of Mathematical Psychology, 12: 328–383.

data(bank_wiring)
library(sna)
b <- concor_hca(bank_wiring, p = 2)
g <- as.sociomatrix.sna(bank_wiring)
bm <- blockmodel(g, ec = b$block, glabels = names(bank_wiring))
plot(bm)