These functions combine an appropriate _census()
function
together with methods for calculating the hierarchical clusters
provided by a certain distance calculation.
A plot()
method exists for investigating the dendrogram
of the hierarchical cluster and showing the returned cluster
assignment.
node_equivalence(
.data,
census,
k = c("silhouette", "elbow", "strict"),
cluster = c("hierarchical", "concor"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
node_structural_equivalence(
.data,
k = c("silhouette", "elbow", "strict"),
cluster = c("hierarchical", "concor"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
node_regular_equivalence(
.data,
k = c("silhouette", "elbow", "strict"),
cluster = c("hierarchical", "concor"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
node_automorphic_equivalence(
.data,
k = c("silhouette", "elbow", "strict"),
cluster = c("hierarchical", "concor"),
distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
range = 8L
)
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
A matrix returned by a node_*_census()
function.
Typically a character string indicating which method
should be used to select the number of clusters to return.
By default "silhouette"
, other options include "elbow"
and "strict"
.
"strict"
returns classes with members only when strictly equivalent.
"silhouette"
and "elbow"
select classes based on the distance between
clusters or between nodes within a cluster.
Fewer, identifiable letters, e.g. "e"
for elbow, is sufficient.
Alternatively, if k
is passed an integer, e.g. k = 3
,
then all selection routines are skipped in favour of this number of clusters.
Character string indicating whether clusters should be
clustered hierarchically ("hierarchical"
) or
through convergence of correlations ("concor"
).
Fewer, identifiable letters, e.g. "c"
for CONCOR, is sufficient.
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.
Integer indicating the maximum number of (k) clusters
to evaluate.
Ignored when k = "strict"
or a discrete number is given for k
.
node_equivalence()
: Returns nodes' membership in
according to their equivalence with respective to some census/class
node_structural_equivalence()
: Returns nodes' membership in
structurally equivalent classes
node_regular_equivalence()
: Returns nodes' membership in
regularly equivalent classes
node_automorphic_equivalence()
: Returns nodes' membership in
automorphically equivalent classes
Other memberships:
community
,
components()
,
core
# \donttest{
(nse <- node_structural_equivalence(mpn_elite_usa_advice))
#> Albright Argyros Armitage Curry Fukuyama Gray Greenberg Hills Kissinger
#> 1 1 2 2 1 1 1 2 2 2
#> # ... with 5 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
#> ACUS AEI ASPEN CATO CFR CGD CNAS CNI CSIS EWI FPRI GMFUS HOOVER
#> 1 3 4 5 6 5 6 5 3 3 4 3 3 3
#> # ... with 7 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
plot(nse)
# }
# \donttest{
(nre <- node_regular_equivalence(mpn_elite_usa_advice,
cluster = "concor"))
#> Albright Argyros Armitage Curry Fukuyama Gray Greenberg Hills Kissinger
#> 1 1 2 2 1 1 3 1 4 2
#> # ... with 5 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
#> ACUS AEI ASPEN CATO CFR CGD CNAS CNI CSIS EWI FPRI GMFUS HOOVER
#> 1 5 6 6 6 1 6 7 5 8 6 6 6 6
#> # ... with 7 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
plot(nre)
# }
# \donttest{
(nae <- node_automorphic_equivalence(mpn_elite_usa_advice,
k = "elbow"))
#> Albright Argyros Armitage Curry Fukuyama Gray Greenberg Hills Kissinger
#> 1 1 1 2 3 3 4 3 1 2
#> # ... with 5 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
#> ACUS AEI ASPEN CATO CFR CGD CNAS CNI CSIS EWI FPRI GMFUS HOOVER
#> 1 4 3 1 4 1 4 2 1 1 4 2 2 1
#> # ... with 7 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
plot(nae)
# }