Promote the Layers of an mcml to Networks

Converts an mcml object into a netobject_group: one netobject for the cluster-level (macro) layer, and one per cluster for the within-cluster layers. The stored weights are carried over as they are – nothing is re-normalised here, so the aggregation chosen when the mcml was built is what the networks hold.

Usage

as_tna(x, ...)

# S3 method for class 'mcml'
as_tna(x, expand = NULL, ...)

# Default S3 method
as_tna(x, ...)

Arguments

x: An mcml object created by cluster_summary or build_mcml.
...: Passed to methods.
expand: For the mcml method, names of clusters whose member states replace the collapsed cluster node in the macro layer (see macro_network). NULL (default) keeps the macro fully collapsed. The per-cluster layers are unaffected.

Value

A netobject_group: a named list whose first element is macro (the k x k cluster-level network) followed by one element per cluster, each a netobject/cograph_network carrying $weights, $inits, $nodes, $edges and the recorded $method ("relative" for an mcml whose weights are already row-normalised, "frequency" otherwise).

The mcml method returns that netobject_group, each layer keeping the data the corresponding mcml layer carried. With expand, its macro element is the mixed-resolution network of macro_network rather than the fully collapsed one.

The default method returns the input unchanged when it already inherits from tna, and otherwise raises an error.

Details

This is the step that lets an MCML result flow into the verbs that take a group of networks (printing, network-metric summaries, rendering with cograph).

Workflow


# Full MCML workflow
net <- build_network(data, method = "relative")
cs   <- cluster_summary(net, clusters = group_assignments)
nets <- as_tna(cs)

# Every layer is an ordinary netobject
print(nets)      # one line per layer
summary(nets)    # network metrics per layer

Zero-out-degree (sink) nodes

Every cluster is returned, regardless of its row sums. A node with zero outgoing weight is a legitimate sink (a terminal state); its row in the wrapped network is left all-zero. This holds for both net_method = "relative" and "frequency" – the stored weights are never re-normalised, so a sink row needs no special handling. Inspect rowSums(x$clusters[[cl]]$weights) to find sink nodes.

Examples

set.seed(1)
mat <- matrix(runif(36), 6, 6)
rownames(mat) <- colnames(mat) <- LETTERS[1:6]
clusters <- list(G1 = c("A", "B"), G2 = c("C", "D"), G3 = c("E", "F"))
cs <- cluster_summary(mat, clusters)
nets <- as_tna(cs)
nets
#> Group Networks (4 groups)
#> 
#>   Group  Nodes  Edges  Weights
#>   macro  3      9      [1.076, 2.706]
#>   G1     2      4      [0.266, 0.945]
#>   G2     2      4      [0.212, 0.935]
#>   G3     2      4      [0.340, 0.870]
summary(nets)
#> Network metrics by group:
#>                       metric  macro     G1      G2     G3
#>                   Node Count      3      2       2      2
#>                   Edge Count      9      4       4      4
#>              Network Density      1      1       1      1
#>                Mean Distance  1.863 0.6584  0.7162 0.5839
#>            Mean Out-Strength  6.181  1.122   1.207  1.353
#>              SD Out-Strength 0.8432 0.6844 0.08534 0.2021
#>             Mean In-Strength  6.181  1.122   1.207  1.353
#>               SD In-Strength 0.5072 0.1253  0.7034 0.4867
#>              Mean Out-Degree      3      2       2      2
#>                SD Out-Degree      0      0       0      0
#>  Centralization (Out-Degree)      0      0       0      0
#>   Centralization (In-Degree)      0      0       0      0
#>                  Reciprocity      1      1       1      1