These functions are used to cluster some census object:
cluster_hierarchical() returns a hierarchical clustering object
created by stats::hclust().
cluster_concor() returns a hierarchical clustering object
created from a convergence of correlations procedure (CONCOR).
These functions are not intended to be called directly,
but are called within node_equivalence() and related functions.
They are exported and listed here to provide more detailed documentation.
cluster_hierarchical(census, distance)
cluster_concor(.data, census)A matrix returned by a node_*_census() function.
Character string indicating which distance metric
to pass on to stats::dist.
By default "euclidean", but other options include
"maximum", "manhattan", "canberra", "binary", and "minkowski".
Fewer, identifiable letters, e.g. "e" for Euclidean, is sufficient.
An object of a {manynet}-consistent class:
matrix (adjacency or incidence) from {base} R
edgelist, a data frame from {base} R or tibble from {tibble}
igraph, from the {igraph} package
network, from the {network} package
tbl_graph, from the {tidygraph} package
First a matrix of Pearson correlation coefficients between each pair of nodes
profiles in the given census is created.
Then, again, we find the correlations of this square, symmetric matrix,
and continue to do this iteratively until each entry is either 1 or -1.
These values are used to split the data into two partitions,
with members either holding the values 1 or -1.
This procedure from census to convergence is then repeated within each block,
allowing further partitions to be found.
Unlike UCINET, partitions are continued until there are single members in
each partition.
Then a distance matrix is constructed from records of in which partition phase
nodes were separated,
and this is given to stats::hclust() so that dendrograms etc can be returned.
Breiger, Ronald L., Scott A. Boorman, and Phipps Arabie. 1975. "An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling". Journal of Mathematical Psychology, 12: 328-83. doi:10.1016/0022-2496(75)90028-0 .