tna (version 0.4.0)

summary.group_tna: Calculate Summary of Network Metrics for a grouped Transition Network

Description

This function calculates a variety of network metrics for a tna object. It computes key metrics such as node and edge counts, network density, mean distance, strength measures, degree centrality, and reciprocity.

Usage

# S3 method for group_tna
summary(object, combined = TRUE, ...)

Value

A summary.group_tna object which is a list of lists or a combined data.frame containing the following network metrics:

• node_count: The total number of nodes.

• edge_count: The total number of edges.

• network_Density: The density of the network.

• mean_distance: The mean shortest path length.

• mean_out_strength: The mean out-strength of nodes.

• sd_out_strength: The standard deviation of out-strength.

• mean_in_strength: The mean in-strength of nodes.

• sd_in_strength: The standard deviation of in-strength.

• mean_out_degree: The mean out-degree of nodes.

• sd_out_degree: The standard deviation of out-degree.

• centralization_out_degree: The centralization of out-degree.

• centralization_in_degree: The centralization of in-degree.

• reciprocity: The reciprocity of the network.

Arguments

object

A group_tna object.

combined

A logical indicating whether the summary results should be combined into a single data frame for all clusters (defaults to TRUE)

...

Ignored

Details

The function extracts the igraph network for each cluster and computes the following network metrics:

• Node count: Total number of nodes in the network.

• Edge count: Total number of edges in the network.

• Network density: Proportion of possible edges that are present in the network.

• Mean distance: The average shortest path length between nodes.

• Mean and standard deviation of out-strength and in-strength: Measures of the total weight of outgoing and incoming edges for each node.

• Mean and standard deviation of out-degree: The number of outgoing edges from each node.

• Centralization of out-degree and in-degree: Measures of how centralized the network is based on the degrees of nodes.

• Reciprocity: The proportion of edges that are reciprocated (i.e., mutual edges between nodes).

See Also

Cluster-related functions bootstrap(), centralities(), cliques(), communities(), deprune(), estimate_cs(), group_model(), hist.group_tna(), mmm_stats(), plot.group_tna(), plot.group_tna_centralities(), plot.group_tna_cliques(), plot.group_tna_communities(), plot.group_tna_stability(), plot_compare.group_tna(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.group_tna_bootstrap(), print.group_tna_centralities(), print.group_tna_cliques(), print.group_tna_communities(), print.group_tna_stability(), print.summary.group_tna(), print.summary.group_tna_bootstrap(), prune(), pruning_details(), rename_groups(), reprune(), summary.group_tna_bootstrap()

Examples

group <- c(rep("High", 1000), rep("Low", 1000))
model <- group_model(group_regulation, group = group)
summary(model)