Skip to contents

Estimate Centrality Stability

Source: R/centralities.R
estimate_centrality_stability.Rd

Estimates the stability of centrality measures in a network using subset sampling without replacement. It allows for dropping varying proportions of cases and calculates correlations between the original centralities and those computed using sampled subsets.

Usage

estimate_cs(
  x,
  loops,
  normalize,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar
)

estimate_centrality_stability(
  x,
  loops,
  normalize,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar
)

# S3 method for class 'tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE
)

# S3 method for class 'tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE
)

# S3 method for class 'group_tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE
)

# S3 method for class 'group_tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE
)

Arguments

x

A tna or a group_tna object representing the temporal network analysis data. The object should be created from a sequence data object.

loops

A logical value indicating whether to include loops in the network when computing the centrality measures (default is FALSE).

normalize

A logical value indicating whether to normalize the centrality measures. The default is FALSE.

measures

A character vector of centrality measures to estimate. The default measures are "InStrength", "OutStrength", and "Betweenness". See centralities() for a list of available centrality measures.

iter

An integer specifying the number of resamples to draw. The default is 1000.

method

A character string indicating the correlation coefficient type. The default is "pearson". See stats::cor() for details.

drop_prop

A numeric vector specifying the proportions of cases to drop in each sampling iteration. Default is a sequence from 0.1 to 0.9 in increments of 0.1.

threshold

A numeric value specifying the correlation threshold for calculating the CS-coefficient. The default is 0.7.

certainty

A numeric value specifying the desired level of certainty for the CS-coefficient. Default is 0.95.

progressbar

A logical value. If TRUE, a progress bar is displayed Defaults to FALSE

Value

A tna_stability object which is a list with an element for each measure with the following elements:

  • cs_coefficient: The centrality stability (CS) coefficient of the measure.

  • correlations: A matrix of correlations between the original centrality and the resampled centralities for each drop proportion.

If x is a group_tna object, a group_tna_stability object is returned instead, which is a list of tna_stability objects.

Details

The function works by repeatedly resampling the data, dropping varying proportions of cases, and calculating centrality measures on the subsets. The correlation between the original centralities and the resampled centralities is calculated for each drop proportion. The stability of each centrality measure is then summarized using a centrality stability (CS) coefficient, which represents the proportion of dropped cases at which the correlations drop below a given threshold (default 0.7).

The results can be visualized by plotting the output object showing the stability of the centrality measures across different drop proportions, along with confidence intervals. The CS-coefficients are displayed in the subtitle.

See also

Validation functions bootstrap(), deprune(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
# Small number of iterations and drop proportions for CRAN
estimate_cs(
  model,
  drop_prop = seq(0.3, 0.9, by = 0.2),
  measures = c("InStrength", "OutStrength"),
  iter = 10
)
#> Centrality Stability Coefficients
#> 
#>  InStrength OutStrength 
#>         0.9         0.9