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 cluster_summary should typically be created with type = "tna" to ensure row-normalized transition probabilities. If created with type = "raw", the raw counts will be passed to tna::tna() which will normalize them.

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, type = "tna")
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)

Excluded Clusters

A within-cluster tna cannot be created when:

• The cluster has only 1 node (no internal transitions possible)

• Some nodes in the cluster have no outgoing edges (row sums to 0)

These clusters are silently excluded from $clusters. The between-cluster model still includes all clusters.

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, type = "tna")
tna_models <- as_tna(cs)
tna_models
tna_models$macro$weights

Nestimate documentation built on April 20, 2026, 5:06 p.m.