Cluster-Level Network, With One Cluster Expanded

Returns the macro (cluster-level) network of an mcml, optionally with one or more clusters expanded back into their member states. Every other cluster stays collapsed to a single node, so the result is a network at mixed resolution: the cluster of interest in detail, its context in summary.

Usage

macro_network(x, expand = NULL, method = "relative", ...)

Arguments

x

An mcml built from sequence data, or an mcml_pc from build_mcml_pc. A matrix-derived mcml carries no node-level data and cannot be expanded.

expand

Names of clusters to expand into their member states. NULL (default) collapses every cluster, reproducing the macro layer; "all" or TRUE expands every cluster, so each state is its own node.

method

Estimator passed to build_network. Default "relative" (row-normalised transitions). Not used for an mcml_pc.

...

Further arguments passed to build_network. Not used for an mcml_pc.

Value

For an mcml_pc: its cluster-level network, the netobject estimated by build_mcml_pc, unchanged. Its estimator is set when the fit is built, so method or ... raise an error, and expand errors with class nestimate_no_expand (there are no sequences to re-count).

For an mcml: a netobject (also a cograph_network) whose nodes are the collapsed clusters plus the member states of any expanded cluster, with weights re-counted from the sequence data by build_network. $node_groups is a two-column data frame (node, group) mapping every node to its cluster, and the same labels are a factor in $nodes$groups, so the result plots grouped; an expanded cluster's states each map to that cluster, a collapsed cluster maps to itself. $expanded records the cluster names that were expanded (NULL when none were).

See also

build_mcml, as_tna

Examples

seqs <- data.frame(
  t1 = c("A", "C", "A", "B"), t2 = c("B", "D", "C", "A"),
  t3 = c("C", "A", "D", "C"), stringsAsFactors = FALSE
)
mc <- build_mcml(seqs, clusters = list(G1 = c("A", "B"), G2 = c("C", "D")))
macro_network(mc)                    # every cluster collapsed
#> Transition Network (relative probabilities) [directed]
#>   Weights: [0.333, 0.667]  |  mean: 0.500
#> 
#>   Weight matrix:
#>         G1    G2
#>   G1 0.400 0.600
#>   G2 0.333 0.667 
#> 
#>   Initial probabilities:
#>   G1            0.750  ████████████████████████████████████████
#>   G2            0.250  █████████████
macro_network(mc, expand = "G2")     # G2 shown as C and D
#> Transition Network (relative probabilities) [directed]
#>   Weights: [0.400, 1.000]  |  mean: 0.750
#> 
#>   Weight matrix:
#>        C D  G1
#>   C  0.0 1 0.0
#>   D  0.0 0 1.0
#>   G1 0.6 0 0.4 
#> 
#>   Initial probabilities:
#>   G1            0.750  ████████████████████████████████████████
#>   C             0.250  █████████████
#>   D             0.000