as_tna: Convert cluster_summary to tna Objects

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Convert cluster_summary to tna Objects

Description

Converts a cluster_summary object to proper tna objects that can be used with all functions from the tna package. Creates both a between-cluster tna model (cluster-level transitions) and within-cluster tna models (internal transitions within each cluster).

Usage

as_tna(x)

## S3 method for class 'mcml'
as_tna(x)

## Default S3 method:
as_tna(x)

Arguments

x	A cluster_summary object created by cluster_summary. The aggregated weights are passed to tna::tna(), which row-normalises them as needed.

Details

This is the final step in the MCML workflow, enabling full integration with the tna package for centrality analysis, bootstrap validation, permutation tests, and visualization.

Requirements

The tna package must be installed. If not available, the function throws an error with installation instructions.

Workflow

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

# Now use tna package functions
plot(tna_models$macro)
tna::centralities(tna_models$macro)
tna::bootstrap(tna_models$macro, iter = 1000)

# Analyze within-cluster patterns
plot(tna_models$clusters$ClusterA)
tna::centralities(tna_models$clusters$ClusterA)

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.

Value

A cluster_tna object (S3 class) containing:

between

A tna object representing cluster-level transitions. Contains $weights (k x k transition matrix), $inits (initial distribution), and $labels (cluster names). Use this for analyzing how learners/entities move between high-level groups or phases.

within

Named list of tna objects, one per cluster. Each tna object represents internal transitions within that cluster. Contains $weights (n_i x n_i matrix), $inits (initial distribution), and $labels (node labels). Clusters with single nodes or zero-row nodes are excluded (tna requires positive row sums).

A netobject_group with data preserved from each sub-network.

A tna object constructed from the input.

See Also

cluster_summary to create the input object, plot() for visualization without conversion, tna::tna for the underlying tna constructor

Examples

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)
tna_models <- as_tna(cs)
tna_models
tna_models$macro$weights

Nestimate documentation built on July 11, 2026, 1:09 a.m.