Cluster data and build per-cluster networks in one step

Combines sequence clustering and network estimation into a single call. Clusters the data using the specified algorithm, then calls build_network on each cluster subset.

Usage

cluster_network(data, k, cluster_by = "pam", dissimilarity = "hamming", ...)

Arguments

data: Sequence data. Accepts a data frame, matrix, or netobject. See build_clusters for supported formats.
k: Integer. Number of clusters.
cluster_by: Character. Clustering algorithm passed to build_clusters's method parameter ("pam", "ward.D2", "ward.D", "complete", "average", "single", "mcquitty", "median", "centroid"), or "mmm" for Mixed Markov Model clustering. Default: "pam".
dissimilarity: Character. Distance metric for sequence clustering. Only valid when cluster_by != "mmm". Default: "hamming".
...: Routed to two stages. For distance clustering (cluster_by != "mmm"), build_clusters arguments na_syms, weighted, lambda, seed, q, p, and covariates are intercepted and forwarded to the clusterer; recognised network-estimation arguments flow to build_network. Unknown arguments error before either stage is run. When cluster_by = "mmm", the recognised build_mmm arguments (n_starts, max_iter, tol, smooth, seed, covariates) are intercepted instead, and the rest flows to build_network. In both modes, when data is a netobject, its build_args are merged into the build_network side (caller's explicit values take precedence).

Value

A netobject_group.

Details

If data is a netobject and method is not provided in ..., the original network method is inherited automatically so the per-cluster networks match the type of the input network.

Examples

seqs <- data.frame(V1 = c("A","B","C","A","B"), V2 = c("B","C","A","B","A"),
                   V3 = c("C","A","B","C","B"))
grp <- cluster_network(seqs, k = 2)
grp
#> Group Networks (2 clusters via pam / hamming)
#> 
#>   Group      Nodes  Edges  Weights         N
#>   Cluster 1  3      2      [1.000, 1.000]  2 (40.0%)
#>   Cluster 2  3      4      [0.500, 1.000]  3 (60.0%)
# \donttest{
set.seed(1)
seqs <- data.frame(
  V1 = sample(LETTERS[1:4], 50, TRUE), V2 = sample(LETTERS[1:4], 50, TRUE),
  V3 = sample(LETTERS[1:4], 50, TRUE), V4 = sample(LETTERS[1:4], 50, TRUE)
)
# Default: PAM clustering, relative (transition) networks
grp <- cluster_network(seqs, k = 3)

# Specify network method (cor requires numeric panel data)
if (FALSE) { # \dontrun{
panel <- as.data.frame(matrix(rnorm(1500), nrow = 300, ncol = 5))
grp <- cluster_network(panel, k = 2, method = "cor")
} # }

# MMM-based clustering
grp <- cluster_network(seqs, k = 2, cluster_by = "mmm")
# }