Package 'aniSNA'

Title: Statistical Network Analysis of Animal Social Networks
Description: Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.
Authors: Prabhleen Kaur [aut, cre], Michael Salter-Townshend [ctb]
Maintainer: Prabhleen Kaur <[email protected]>
License: MIT + file LICENSE
Version: 1.1.1
Built: 2025-02-08 06:08:54 UTC
Source: https://github.com/prabhleenkaur19/anisna

Help Index

aniSNA

Description

Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global network metrics, and correlation and regression analysis of the local network metrics.

Author(s)

Prabhleen Kaur

To obtain two non-overlapping bootstrapped versions and obtain p-values for the significance of difference between them

Description

To obtain two non-overlapping bootstrapped versions and obtain p-values for the significance of difference between them

Usage

bootstrapped_difference_pvalues(
  network,
  n_versions = 1000,
  seed = 12345,
  n.iter = 10,
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x))
)

Arguments

network

An igraph object
n_versions

Number of bootstrapped versions to be used (default = 1000)
seed

seed number
n.iter

Number of iterations at each level
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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))

Value

A matrix of p-values whose rows correspond to the sub-sample size and columns correspond to the chosen network metric. The sub-sample size values (corresponding to rows) occur in multiples of 5 and range from 5 to a maximum of half the number of nodes in the network

Examples

data(elk_network_2010)
mean_pvalue_matrix <- bootstrapped_difference_pvalues(elk_network_2010, n_versions = 100)
plot(mean_pvalue_matrix)

To perform correlation analysis for node-level network metrics

Description

To perform correlation analysis for node-level network metrics

Usage

correlation_analyze(
  network,
  n_simulations = 10,
  subsampling_proportion = c(0.1, 0.3, 0.5, 0.7, 0.9),
  network_metrics_functions_list = c(degree = function(net, sub_net) igraph::degree(net,
    v = igraph::V(sub_net)$name), strength = function(net, sub_net) igraph::strength(net,
    v = igraph::V(sub_net)$name), betweenness = function(net, sub_net)
    igraph::betweenness(net, v = igraph::V(sub_net)$name), clustering_coefficient =
    function(net, sub_net) igraph::transitivity(net, type = "local", vids =
    igraph::V(sub_net)$name), eigenvector_centrality = function(net, sub_net)
    igraph::eigen_centrality(net)$vector[igraph::V(sub_net)$name])
)

Arguments

network

An igraph graph object consisting of observed network
n_simulations

Number of sub-samples to be obtained at each level
subsampling_proportion

A vector depicting proportions of sub-sampled nodes
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. Default = c("degree" = function(net, sub_net) igraph::degree(net, v = igraph::V(sub_net)$name), "strength" = function(net, sub_net) igraph::strength(net, v = igraph::V(sub_net)$name), "betweenness" = function(net, sub_net) igraph::betweenness(net, v = igraph::V(sub_net)$name), "clustering_coefficient" = function(net, sub_net) igraph::transitivity(net, type = "local", vids = igraph::V(sub_net)$name), "eigenvector_centrality" = function(net, sub_net) igraph::eigen_centrality(net)$vector[igraph::V(sub_net)$name])

Value

A list of network metrics of class list_correlation_matrices. Each element of list is a matrix whose columns 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_correlation_analysis <- correlation_analyze(elk_network_2010)
plot(elk_correlation_analysis)

Calculate distance between two pairs of radian coordinates

Description

Calculate distance between two pairs of radian coordinates

Usage

distance_radian_coordinates(latf, lonf, latt, lont)

Arguments

latf

latitude from
lonf

longitude from
latt

latitude to
lont

longitude to

Value

distance value in meters

A list of 100 igraph objects obtained by permuting the raw elk_data_2010 and obtaining network from those

Description

A list of 100 igraph objects obtained by permuting the raw elk_data_2010 and obtaining network from those

Usage

elk_2010_permutations

Format

A list of 100 igraph objects

Examples

data(elk_2010_permutations)

Dataset of all possible interactions from elk_data_2010

Description

Dataset of all possible interactions from elk_data_2010

Usage

elk_all_interactions_2010

Format

A dataframe with 7615 rows and 5 variables

Animal_A

First animal ID

Animal_B

Second animal ID

Timestamp_A

Observation timestamp of first animal

Timestamp_B

Observation timestamp of second animal

distance

Distance in metres between the two animals

Examples

data(elk_all_interactions_2010)

Data to showcase functions in our package

Description

Contains GPS telemetry observations of the species elk in year 2010

Usage

elk_data_2010

Format

A dataframe with 123568 rows and 4 variables:

animal_id

Unique ID of individuals in the observed sample

datetime

Date and timestamp of the observation

latitude_rad

Latitude of individual observation in radians

longitude_rad

Longitude of individual observation in radians

Examples

data(elk_data_2010)

Dataset of interactions from elk_data_2010 using first mode as the spatial threshold

Description

Dataset of interactions from elk_data_2010 using first mode as the spatial threshold

Usage

elk_interactions_2010

Format

A dataframe with 2393 rows and 5 variables

Animal_A

First animal ID

Animal_B

Second animal ID

Timestamp_A

Observation timestamp of first animal

Timestamp_B

Observation timestamp of second animal

distance

Distance in metres between the two animals

Examples

data(elk_interactions_2010)

An igraph object depicting the network obtained from elk_interactions_2010

Description

An igraph object depicting the network obtained from elk_interactions_2010

Usage

elk_network_2010

Format

An igraph object with 57 nodes and 114 edges

Examples

igraph::E(elk_network_2010)

To convert latitude and longitude values from degrees to radians

Description

To convert latitude and longitude values from degrees to radians

Usage

get_coordinates_in_radian(species_raw)

Arguments

species_raw

A DataFrame consisting of GPS observations. The DataFrame must have a "latitude" column and a "longitude" column whose values are specified in degrees.

Value

The same DataFrame that has been passed as the argument with two additional columns namely "latitude_rad" and "longitude_rad"

To obtain interactions from raw GPS observations

Description

To obtain interactions from raw GPS observations

Usage

get_interactions(species_raw, temporal_thresh = 7, spatial_thresh, n_cores = 1)

Arguments

species_raw

A DataFrame consisting of GPS observations. It should have at least four columns namely "animal_id", "datetime", "latitude|_rad", and "longitude_rad". "latitude|_rad", and "longitude_rad" are latitude and longitude values in radians respectively. See function "get_coordinates_in_radian" to get these values.
temporal_thresh

Temporal threshold in minutes with default 7 minutes
spatial_thresh

The maximum distance in meters within which two animals are considered interacting
n_cores

Number of cores for parallel processing with default 1

Value

A dataframe consisting of five columns. The first two columns contain animal ids, third and fourth column contain timestamp of their observations and the final column contains the distance between the two individuals.

Examples

data(elk_data_2010)
get_interactions(elk_data_2010, temporal_thresh = 7, spatial_thresh = 15)

Calculates and prints network summary statistics

Description

Calculates and prints network summary statistics

Usage

get_network_summary(network)

Arguments

network

An undirected network with nodes representing animal IDs and edges representing associations between them.

Value

No return value, called for side effects. The function prints values of network metrics to the console.

Examples

data(elk_network_2010)
get_network_summary(elk_network_2010)

To obtain spatial threshold for calculating interactions from raw GPS observations. The threshold is obtained as the distance interval that captures maximum number of inter-individual interactions.

Description

To obtain spatial threshold for calculating interactions from raw GPS observations. The threshold is obtained as the distance interval that captures maximum number of inter-individual interactions.

Usage

get_spatial_threshold(species_interactions, interval_size)

Arguments

species_interactions

A dataframe consisting of individual interactions within maximum possible distance
interval_size

Minimum interval size within which the number of interactions should be calculated

Value

Spatial threshold in meters

Examples

data(elk_all_interactions_2010)
get_spatial_threshold(elk_all_interactions_2010, interval_size = 2)

To obtain confidence intervals around the observed global network statistics

Description

To obtain confidence intervals around the observed global network statistics

Usage

global_CI(
  network,
  n_versions = 100,
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x)),
  CI_size = 0.95
)

Arguments

network

An igraph 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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))
CI_size

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

Value

A DataFrame consisting of three columns. The first column contains the value of observed network metric, the second and third column represent the lower and upper limit of 95

Examples

data(elk_network_2010)
global_CI(elk_network_2010, n_versions = 100, 
network_metrics_functions_list = c("edge_density" = function(x) igraph::edge_density(x),
"diameter" = function(x) igraph::diameter(x, weights = NA),
"transitivity" = function(x) igraph::transitivity(x)))

To obtain width of confidence intervals for global network metrics using bootstrapped versions at each level of sub-sampling

Description

To obtain width of confidence intervals for global network metrics using bootstrapped versions at each level of sub-sampling

Usage

global_width_CI(
  network,
  n_versions = 100,
  seed = 12345,
  n.iter = 10,
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x)),
  scaled_metrics = NULL,
  CI_size = 0.95
)

Arguments

network

An igraph object consisting of observed network.
n_versions

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

seed number
n.iter

Number of iterations at each level. (default = 10)
network_metrics_functions_list

A list consisting of function definitions of the global network metrics that the user wants to evaluate. Each element in the list should have an assigned name. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))
scaled_metrics

Optional. A vector subset of the names of functions in network_metrics_functions_list with the metrics that should be scaled. For example scaled_metrics = c("diameter")
CI_size

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

Value

A matrix of class Width_CI_matrix containing width of Confidence Intervals where each row corresponds to the sub-sample size and columns correspond to the chosen network metric. Sub-sample size values occur in multiples of 10 and range from 10 to maximum multiple of 10 less than or equal to the number of nodes in the network.

Examples

data(elk_network_2010)
width_CI_elk <- global_width_CI(elk_network_2010, n_versions = 100)
plot(width_CI_elk)

Function to obtain pairs of interacting animals

Description

Function to obtain pairs of interacting animals

Usage

interacting_pairs(
  i,
  datetime,
  latitude,
  longitude,
  temporal_thresh,
  spatial_thresh
)

Arguments

i

Index of the animal
datetime

DateTime vector
latitude

latitude vector
longitude

longitude vector
temporal_thresh

time threshold in minutes
spatial_thresh

spatial threshold in meters

Value

A matrix consisting of two row. The first row corresponds to the interacting indices and the second row to the respective distances.

Function to obtain a network structure from interactions dataframe

Description

Function to obtain a network structure from interactions dataframe

Usage

network_from_interactions(species_raw, interactions, n_cores = 1)

Arguments

species_raw

A dataframe consisting of raw GPS observations. It should have at least four columns namely "animal_id", "datetime", "latitude|_rad", and "longitude_rad". "latitude|_rad", and "longitude_rad" are latitude and longitude values in radians respectively. See function "get_coordinates_in_radian" to get these values.
interactions

A dataframe of interactions obtained from raw GPS observations using the function "get_interactions"
n_cores

Number of cores for parallel processing, default is 1

Value

An object of class igraph

Examples

data(elk_data_2010, elk_interactions_2010)
network_from_interactions(elk_data_2010, elk_interactions_2010)

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)

To obtain bootstrapped versions of a network's adjacency matrix

Description

To obtain bootstrapped versions of a network's adjacency matrix

Usage

obtain_bootstrapped_samples(
  network,
  n_nodes = igraph::gorder(network),
  n_versions = 1000,
  seed = 12345
)

Arguments

network

An igraph object
n_nodes

Number of nodes to be selected in bootstrapped versions (default : All nodes)
n_versions

Number of bootstrapped versions required
seed

seed number

Value

A list of class bootstrapped_pvalue_matrix consisting of two elements. The first element contains the adjacency matrix of the original network and the second element contains bootstrapped versions of the adjacency matrices.

Examples

data(elk_network_2010)
obtain_bootstrapped_samples(elk_network_2010, n_versions = 100)

To obtain sub-networks of the observed network

Description

To obtain sub-networks of the observed network

Usage

obtain_network_subsamples(
  network,
  n_subsamples = 1,
  subsampling_proportion = 0.5
)

Arguments

network

An igraph object
n_subsamples

Number of sub-networks to be obtained. (default = 1)
subsampling_proportion

A value depicting the level (in proportion) at which sub-samples to be taken. (default = 0.5). This value should lie between 0 and 1 depicting the proportion of observed nodes to be included in the sub-network.

Value

A list of size n_subsamples, where each element of the list is an igraph object representing a sub-network of the observed network.

Examples

data(elk_network_2010)
obtain_network_subsamples(elk_network_2010, 1, 0.5)

Function to obtain permuted networks from raw datastream

Description

Function to obtain permuted networks from raw datastream

Usage

obtain_permuted_network_versions(
  species_raw,
  temporal_thresh,
  spatial_thresh,
  n_permutations,
  n_cores = 1
)

Arguments

species_raw

A dataframe consisting of raw GPS observations
temporal_thresh

Temporal threshold in minutes
spatial_thresh

Spatial threshold
n_permutations

Number of permuted versions to obtain
n_cores

Number of cores for parallel processing with default 1

Value

An object of class "list_permuted_networks" of size n_permutations where each element is a network of class igraph obtained by permuting raw datastream

Examples

data(elk_data_2010)
permuted_versions <- obtain_permuted_network_versions(elk_data_2010, 
temporal_thresh = 7, spatial_thresh = 15, n_permutations = 10, n_cores = 2)

Visualize Animal Network

Description

Visualize Animal Network

Usage

plot_network(species_network, seed = 1)

Arguments

species_network

An igraph graph object consisting of observed network.
seed

Seed to be set for layout.

Value

No return value, called for side effects. The plots depict a visualisation of network structure.

Examples

data(elk_network_2010)
plot_network(elk_network_2010)

To plot the results obtained from bootstrapped_difference_pvalues function

Description

To plot the results obtained from bootstrapped_difference_pvalues function

Usage

## S3 method for class 'bootstrapped_pvalue_matrix'
plot(x, ...)

Arguments

x

A matrix of p-values obtained from bootstrapped_difference_pvalues function
...

Further arguments are ignored.

Value

No return value, called for side effects. The plot shows p-values between 0 and 1 corresponding to each sample size.

Examples

data(elk_network_2010)
mean_pvalue_matrix <- bootstrapped_difference_pvalues(elk_network_2010, n_versions = 100)
plot(mean_pvalue_matrix)

To plot correlation analysis results

Description

To plot correlation analysis results

Usage

## S3 method for class 'list_correlation_matrices'
plot(x, ...)

Arguments

x

A list of matrices obtained from correlation_analyze function.
...

Further arguments are ignored.

Value

No return value, called for side effects. The plots show mean and standard deviation of correlation coefficients obtained over multiple iterations.

Examples

data(elk_network_2010)
elk_correlation_analysis <- correlation_analyze(elk_network_2010)
plot(elk_correlation_analysis)

To plot the results for node-level confidence intervals

Description

To plot the results for node-level confidence intervals

Usage

## S3 method for class 'list_node_level_CI'
plot(x, ...)

Arguments

x

A list of dataframes obtained from node_level_CI function.
...

Further arguments are ignored.

Value

No return value, called for side effects. The plots show confidence intervals along with the observed metric value for each of the nodes in the network.

Examples

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

Function to plot the network metrics distribution of permuted networks

Description

Function to plot the network metrics distribution of permuted networks

Usage

## S3 method for class 'list_permuted_networks'
plot(
  x,
  species_original_network,
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x)),
  ...
)

Arguments

x

A list of igraph objects obtained obtained using the function obtain_permuted_network_versions
species_original_network

An igraph object which is the original network
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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))
...

Further arguments are ignored.

Value

No return value, called for side effects.

Examples

data(elk_data_2010, elk_network_2010)
permuted_versions <- obtain_permuted_network_versions(elk_data_2010, 
temporal_thresh = 7, spatial_thresh = 15, n_permutations = 10, n_cores = 2)
plot(permuted_versions, elk_network_2010)

To plot regression analysis results

Description

To plot regression analysis results

Usage

## S3 method for class 'list_regression_matrices'
plot(x, ...)

Arguments

x

A list of matrices obtained from regression_slope_analyze function
...

Further arguments are ignored

Value

No return value, called for side effects. The plots show regression slope values corresponding to proportion of individuals in the sample.

Examples

data(elk_network_2010)
elk_regression_analysis <- regression_slope_analyze(elk_network_2010)
plot(elk_regression_analysis)

To plot sub-sampling results

Description

To plot sub-sampling results

Usage

## S3 method for class 'Subsampled_Network_Metrics'
plot(
  x,
  network,
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x)),
  ...
)

Arguments

x

A list of matrices belonging to class "Subsampled_Network_Metrics" and is obtained from subsampled_network_metrics function
network

An igraph graph object consisting of the observed network
network_metrics_functions_list

This is the same argument that is passed for obtaining the results from the function subsampled_network_metrics. 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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))
...

Further arguments are ignored

Value

No return value, called for side effects. The boxplots depict range of values, network metrics take when multiple subsamples are chosen from the observed sample.

Examples

data(elk_network_2010)
elk_subsamples <- subsampled_network_metrics(elk_network_2010)
plot(elk_subsamples, elk_network_2010, 
network_metrics_functions_list = c("edge_density" = function(x) igraph::edge_density(x),
"diameter" = function(x) igraph::diameter(x, weights = NA),
"transitivity" = function(x) igraph::transitivity(x)))

To plot sub-sampling results of the original network and permuted networks

Description

To plot sub-sampling results of the original network and permuted networks

Usage

## S3 method for class 'Subsampled_Permuted_Network_Metrics'
plot(
  x,
  network,
  n_simulations = 100,
  subsampling_proportion = c(0.1, 0.3, 0.5, 0.7, 0.9),
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x)),
  ...
)

Arguments

x

A list of matrices obtained from subsampled_permuted_network_metrics function of class "Subsampled_Permuted_Network_Metrics"
network

An igraph graph object consisting of observed network
n_simulations

For subsampling results of original network, this determines the number of sub-samples to be obtained at each level
subsampling_proportion

A vector depicting the levels (in proportion) at which subsamples to be taken. This parameter should be the same as the subsampling_proportion parameter passed for the function subsampled_permuted_network_metrics to obtain x.
network_metrics_functions_list

This is the same argument that is passed for obtaining the results from the function subsampled_permuted_network_metrics. 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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))
...

Further arguments are ignored

Value

No return value, called for side effects. The boxplots show side-by-side comparison of network metrics distribution from subsamples of observed network and subsamples from permuted networks.

Examples

data(elk_2010_permutations, elk_network_2010)
elk_subsamples_permuted_networks <- subsampled_permuted_network_metrics(elk_2010_permutations)
plot(elk_subsamples_permuted_networks, elk_network_2010)

To plot the results obtained from width_CI function

Description

To plot the results obtained from width_CI function

Usage

## S3 method for class 'Width_CI_matrix'
plot(x, ...)

Arguments

x

A matrix of width of Confidence Intervals obtained from global_width_CI function
...

Further arguments are ignored.

Value

No return value, called for side effects. Plots show width of confidence intervals corresponding to number of individuals in the sub-sample.

Examples

data(elk_network_2010)
width_CI_elk <- global_width_CI(elk_network_2010, n_versions = 100)
plot(width_CI_elk)

To perform regression analysis for local network metrics

Description

To perform regression analysis for local network metrics

Usage

regression_slope_analyze(
  network,
  n_simulations = 10,
  subsampling_proportion = c(0.1, 0.3, 0.5, 0.7, 0.9),
  network_metrics_functions_list = c(degree = function(net, sub_net) igraph::degree(net,
    v = igraph::V(sub_net)$name), strength = function(net, sub_net) igraph::strength(net,
    v = igraph::V(sub_net)$name), betweenness = function(net, sub_net)
    igraph::betweenness(net, v = igraph::V(sub_net)$name), clustering_coefficient =
    function(net, sub_net) igraph::transitivity(net, type = "local", vids =
    igraph::V(sub_net)$name), eigenvector_centrality = function(net, sub_net)
    igraph::eigen_centrality(net)$vector[igraph::V(sub_net)$name])
)

Arguments

network

An igraph graph object consisting of observed network
n_simulations

Number of sub-samples to be obtained at each level
subsampling_proportion

A vector depicting proportions of sub-sampled nodes
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. Default = c("degree" = function(net, sub_net) igraph::degree(net, v = igraph::V(sub_net)$name), "strength" = function(net, sub_net) igraph::strength(net, v = igraph::V(sub_net)$name), "betweenness" = function(net, sub_net) igraph::betweenness(net, v = igraph::V(sub_net)$name), "clustering_coefficient" = function(net, sub_net) igraph::transitivity(net, type = "local", vids = igraph::V(sub_net)$name), "eigenvector_centrality" = function(net, sub_net) igraph::eigen_centrality(net)$vector[igraph::V(sub_net)$name])

Value

A list of network metrics of class list_regression_matrices. Each element of list is a matrix whose columns correspond to subsampling_proportion and rows correspond to n_simulations. The entries of the matrix provide value of the slope of regression when the nodal values in sub-sampled network are regressed upon the values of the same nodes in the full network for the corresponding metric.

Examples

data(elk_network_2010)
elk_regression_analysis <- regression_slope_analyze(elk_network_2010)
plot(elk_regression_analysis)

To generate subsamples and obtain network metrics of the subsamples

Description

To generate subsamples and obtain network metrics of the subsamples

Usage

subsampled_network_metrics(
  network,
  n_simulations = 100,
  subsampling_proportion = c(0.1, 0.3, 0.5, 0.7, 0.9),
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x))
)

Arguments

network

An igraph graph object consisting of observed network
n_simulations

Number of sub-samples to be obtained at each level
subsampling_proportion

A vector depicting the levels (in proportion) at which subsamples to be taken
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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))

Value

A list of network metrics of class "Subsampled_Network_Metrics". Each element of list is a matrix whose columns correspond to subsampling_proportion and rows correspond to n_simulations. The entries of the matrix provide values of the corresponding metric.

Examples

data(elk_network_2010)
elk_subsamples <- subsampled_network_metrics(elk_network_2010)
plot(elk_subsamples, elk_network_2010, 
network_metrics_functions_list = c("edge_density" = function(x) igraph::edge_density(x),
"diameter" = function(x) igraph::diameter(x, weights = NA),
"transitivity" = function(x) igraph::transitivity(x)))

To generate subsamples of the permuted networks and obtain network metrics of those subsamples

Description

To generate subsamples of the permuted networks and obtain network metrics of those subsamples

Usage

subsampled_permuted_network_metrics(
  networks_list,
  subsampling_proportion = c(0.1, 0.3, 0.5, 0.7, 0.9),
  network_metrics_functions_list = c(edge_density = function(x) igraph::edge_density(x),
    diameter = function(x) igraph::diameter(x, weights = NA), transitivity = function(x)
    igraph::transitivity(x))
)

Arguments

networks_list

A list of igraph objects obtained by permuting the observed network
subsampling_proportion

A vector depicting the levels (in proportion) at which subsamples to be taken
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. Default = c("edge_density" = function(x) igraph::edge_density(x), "diameter" = function(x) igraph::diameter(x, weights = NA), "transitivity" = function(x) igraph::transitivity(x))

Value

A list of network metrics of class "Subsampled_Permuted_Network_Metrics". Each element of list is a matrix whose columns correspond to subsampling_proportion and rows correspond to the number of networks in networks_list. The entries of the matrix provide values of the corresponding metric.

Examples

data(elk_2010_permutations)
elk_subsamples_permuted_networks <- subsampled_permuted_network_metrics(elk_2010_permutations)
plot(elk_subsamples_permuted_networks, elk_network_2010)