node_level_CI {aniSNA}

To obtain confidence intervals for node-level network metrics

Description

To obtain confidence intervals for node-level network metrics

Usage

node_level_CI(
  network,
  n_versions = 100,
  network_metrics_functions_list = c(degree = igraph::degree, strength =
    igraph::strength, betweenness = igraph::betweenness, clustering_coefficient =
    function(x) {
     trans <- igraph::transitivity(x, type = "local", vids =
    igraph::V(x), isolates = "zero")
     names(trans) <- igraph::V(x)$name
    
    return(trans)
 }, eigenvector_centrality = function(x)
    igraph::eigen_centrality(x)$vector),
  n_cores = 1,
  CI_size = 0.95
)

Arguments

network

An igraph graph object consisting of observed network.

n_versions

Number of bootstrapped versions to be used. (default = 100)

network_metrics_functions_list

A list consisting of function definitions of the network metrics that the user wants to evaluate. Each element in the list should have an assigned name. Each function definition should include two parameters, one for the main network and another one for the subnetwork. See default example. network_metrics_functions_list = c("degree" = igraph::degree, "strength" = igraph::strength , "betweenness" = igraph::betweenness, "clustering_coefficient" = function(x) { trans <- igraph::transitivity(x, type = "local", isolates = "zero") names(trans) <- igraph::V(x)$name;return(trans) }, "eigenvector_centrality" = function(x) igraph::eigen_centrality(x)$vector )

n_cores

Number of cores for parallel processing with default 1.

CI_size

Size of confidence interval. Default is 0.95 that generates a 95% confidence interval.

Value

A list of dataframes of class list_node_level_CI. Each element of list is a dataframe having five columns and having number of rows equal to number of nodes in the network. The five columns correspond to node_number, node_name, metric_value, lower_CI, upper_CI. correspond to subsampling_proportion and rows correspond to n_simulations. The entries of the matrix provide value of correlation between the nodes in full network and the sub-sampled network for the corresponding metric.

Examples

 
data(elk_network_2010)
elk_node_level_CI <- node_level_CI(elk_network_2010)
plot(elk_node_level_CI)