Package index • catgraph

All functions

as_ggraph(): Coerce a catgraph to a ggraph-compatible tbl_graph
as_igraph(): Extract the underlying igraph object from a catgraph
assoc_matrix(): Extract pairwise association weights as a matrix or tidy data frame
assoc_similarity(): Dense pairwise similarity matrix of categorical variables
bayesian_cramers_v(): Bayesian Cramér's V for a pair of categorical variables
bipartite_modality_graph(): Construct a bipartite respondent-modality graph
bootstrap_ci(): Bootstrap confidence intervals for phi or Cramer's V
build_conditional_modality_graph(): Build a modality graph for an observed subgroup
build_graph(): Build the underlying igraph association network
build_modality_graph(): Build a modality-level association graph
catgraph() print(<catgraph>) summary(<catgraph>): Construct a categorical association network
catgraph_ci(): Add bootstrap confidence intervals to all edges of a catgraph
cluster_modalities(): Detect communities of co-associated modalities
clustering_coef(): Compute weighted clustering coefficients for all variables in a catgraph
compare_catgraphs(): Compare multiple catgraph networks on one panel
compare_clustering(): Compare all weighted clustering coefficients side by side
compare_gravity(): Computes modality_gravity on two conditional modality graphs and returns a side-by-side comparison of dMGI, OS, and role for every modality present in either graph. Optionally plots a dot-chart of dMGI differences.
compare_modality_graphs(): Compare multiple modality networks on one panel
compute_assoc(): Compute chi-square association between two categorical variables
detect_clusters(): Detect variable communities in a catgraph using graph clustering algorithms
detect_type(): Detect contingency table type for a pair of categorical variables
effect_size(): Compute phi or Cramer's V effect size for a pair of categorical variables
expand_table(): Expand a contingency table or frequency data frame to observation-level format
joint_balance() print(<jointbalance>) summary(<jointbalance>): Joint categorical distribution diagnostic across groups
modality_gravity(): Modality Gravity Index for catmodgraph objects
nmi_assoc(): Normalised Mutual Information for a pair of categorical variables
node_centrality(): Compute weighted centrality indices for all variables in a catgraph
node_centrality(<catmodgraph>): node_centrality method for catmodgraph objects
plot(<catbipartite>): Plot a bipartite respondent-modality graph
plot(<catgraph>): Plot a catgraph object
plot(<catmodgraph>): Plot a modality graph
plot(<jointbalance>): Plot a jointbalance diagnostic
plot_centrality(): Plot weighted centrality indices for a catgraph
plot_clustering(): Plot weighted clustering coefficients for a catgraph
plot_gravity(): Plot gravity indices alongside traditional centrality for a catmodgraph
plot_gravity_scatter(): Scatter plot of eigenvector centrality vs dMGI
plot_heatmap(): Plot a heatmap of pairwise association weights
plot_modality_difference(): Plot modality-network differences on a single graph
print(<modality_gravity>): Print method for modality_gravity output
prune_edges(): Prune edges from a catgraph by effect size or adjusted p-value
prune_modality_edges(): Prune edges from a modality graph
summarise_modality_communities(): Summarise modality communities in a catmodgraph
summary(<modality_gravity>): Summary method for modality_gravity output
survey_health: Synthetic health survey data (categorical variables)
test_modality_edge_differences(): Edge-wise post-hoc test for modality-network differences
test_modality_graph_equality(): Permutation test for equality of modality-graph structure