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Analyze correlations between dyadic attributes and network ties

Source: R/dyad_correlation.R
dyad_correlation.Rd

Examines relationships between dyadic (pairwise) attributes and network connections. Calculates correlations between dyadic variables and edge weights/presence, with support for multiple correlation methods and significance testing.

Usage

dyad_correlation(
  netlet,
  dyad_vars = NULL,
  edge_vars = NULL,
  method = "pearson",
  binary_network = FALSE,
  remove_diagonal = TRUE,
  significance_test = TRUE,
  alpha = 0.05,
  partial_correlations = FALSE,
  other_stats = NULL,
  ...
)

Arguments

netlet

A netify object containing network data.

dyad_vars

Character vector of dyadic attribute names to analyze. If NULL, analyzes all available dyadic variables.

edge_vars

Character vector of edge variables to correlate with. If NULL, uses the main network matrix.

method

Character string specifying correlation method:

"pearson"

Pearson product-moment correlation (default)

"spearman"

Spearman rank correlation

"kendall"

Kendall's tau correlation

binary_network

Logical. Whether to convert ties to binary before correlation. Default FALSE.

remove_diagonal

Logical. Whether to exclude diagonal elements. Default TRUE.

significance_test

Logical. Whether to calculate P-values and confidence intervals. Default TRUE.

alpha

Significance level for confidence intervals. Default 0.05.

partial_correlations

Logical. Whether to calculate partial correlations controlling for other dyadic variables. Default FALSE.

other_stats

Named list of custom functions for additional statistics.

...

Additional arguments passed to custom functions.

Value

Data frame with one row per dyadic variable per network/time period:

net

Network/time identifier

layer

Layer name

dyad_var

Name of dyadic variable

edge_var

Name of edge variable

correlation

Correlation coefficient

p_value

P-value for correlation significance

ci_lower, ci_upper

Confidence interval bounds

n_pairs

Number of dyad pairs included

method

Correlation method used

mean_dyad_var

Mean value of dyadic variable

sd_dyad_var

Standard deviation of dyadic variable

mean_edge_var

Mean value of edge variable

sd_edge_var

Standard deviation of edge variable

Details

Extracts dyadic variables from dyad_data attribute and correlates them with network ties. For longitudinal networks, correlations are calculated separately for each time period. Dyadic variables should be stored as matrices with rows and columns corresponding to network actors. Missing values are handled using pairwise complete observations.

Author

Cassy Dorff, Shahryar Minhas