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 cluster_data for supported formats.
k: Integer. Number of clusters.
cluster_by: Character. Clustering algorithm passed to cluster_data'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 (ignored when cluster_by = "mmm"). Default: "hamming".
...: Passed directly to build_network. Use method to specify the network type; threshold, scaling, and all other build_network arguments are supported.

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

# \donttest{
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")
# }