iglm.data_generator: Networks with Unit-Level Attributes (R6 Class)
In iglm: Regression under Interference in Connected Populations
iglm.data_generator R Documentation
Networks with Unit-Level Attributes (R6 Class)
Description
The 'iglm.data' class is a container for storing, validating, and analyzing
unit-level attributes (x_attribute, y_attribute) and connections (z_network).
Active bindings
x_attribute('numeric') The vector for the first unit-level attribute.
y_attribute('numeric') The vector for the second unit-level attribute.
z_network('matrix') The primary network structure as a 2-column integer edgelist.
neighborhood('matrix') Read-only. The secondary/neighborhood structure as a 2-column integer edgelist. An empty matrix if not provided.
overlap(‘matrix') Read-only. The calculated overlap relation (dyads with shared neighbors in 'neighborhood') as a 2-column integer edgelist. An empty matrix if overlap hasn’t been computed or is not available.
directed('logical') Indicates if the 'z_network' is treated as directed.
n_actor('integer') The total number of actors (nodes) in the network.
type_x('character') The specified distribution type for the 'x_attribute'.
type_y('character') The specified distribution type for the 'y_attribute'.
scale_x('numeric') The scale parameter associated with the 'x_attribute'.
scale_y('numeric') The scale parameter associated with the 'y_attribute'.
fix_x('logical') Indicates if the 'x_attribute' is fixed during estimation/simulation.
fix_z('logical') RIndicates if the 'z_network' is fixed during estimation/simulation.
descriptives('list')A list storing computed descriptive statistics for the network and attributes.
fix_z_alocal('logical') Flag indicating whether nonoverlap edges are treated as random.
Methods
Public methods
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Method new()
Create a new 'iglm.data' object, that includes data on two attributes and one network.
Usage
iglm.data_generator$new(
x_attribute = NULL,
y_attribute = NULL,
z_network = NULL,
neighborhood = NULL,
directed = NA,
n_actor = NA,
type_x = "binomial",
type_y = "binomial",
scale_x = 1,
scale_y = 1,
fix_x = FALSE,
fix_z = FALSE,
fix_z_alocal = TRUE,
return_neighborhood = TRUE,
file = NULL
)
Arguments
x_attributeA numeric vector for the first unit-level attribute.
y_attributeA numeric vector for the second unit-level attribute.
z_networkA matrix representing the network. Can be a 2-column
edgelist or a square adjacency matrix.
neighborhoodAn optional matrix for the neighborhood representing local dependence.
Can be a 2-column edgelist or a square adjacency matrix.
A tie in 'neighborhood' between actor i and j indicates that j is in the neighborhood of i,
implying dependence between the respective actors.
directedA logical value indicating if 'z_network' is directed.
If 'NA' (default), directedness is inferred from the symmetry of
'z_network'.
n_actorAn integer for the number of actors in the system.
If 'NA' (default), 'n_actor' is inferred from the attributes or
network matrices.
type_xCharacter string for the type of 'x_attribute'.
Must be one of '"binomial"', '"poisson"', or '"normal"'.
Default is '"binomial"'.
type_yCharacter string for the type of 'y_attribute'.
Must be one of '"binomial"', '"poisson"', or '"normal"'.
Default is '"binomial"'.
scale_xA positive numeric value for scaling (e.g., variance
for "normal" type). Default is 1.
scale_yA positive numeric value for scaling (e.g., variance
for "normal" type). Default is 1.
fix_xLogical. If 'TRUE', the 'x_attribute' is treated as fixed
during model estimation and simulation. Default is 'FALSE'.
fix_zLogical. If 'TRUE', the 'z_network' is treated as fixed
during model estimation and simulation. Default is 'FALSE'.
fix_z_alocalLogical. If 'TRUE' (default), alocal dyads in the neighborhood are fixed.
return_neighborhoodLogical. If 'TRUE' (default) and
'neighborhood' is 'NULL', a full neighborhood (all dyads) is
generated implying global dependence. If 'FALSE', no neighborhood is set.
file(character) Optional file path to load a saved 'iglm.data' object state.
Returns
A new 'iglm.data' object.
Method set_z_network()
Sets the 'z_network' of the 'iglm.data' object.
Usage
iglm.data_generator$set_z_network(z_network)
Arguments
z_networkA matrix representing the network. Can be a 2-column
edgelist or a square adjacency matrix.
@return The 'iglm.data' object itself ('self'), invisibly.
Method set_type_x()
Sets the 'type_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_type_x(type_x)
Arguments
type_xA character string for the type of 'x_attribute'.
Must be one of '"binomial"', '"poisson"', or '"normal"'.
@return The 'iglm.data' object itself ('self'), invisibly.
Method set_type_y()
Sets the 'type_y' of the 'iglm.data' object.
Usage
iglm.data_generator$set_type_y(type_y)
Arguments
type_yA character string for the type of 'y_attribute'.
Must be one of '"binomial"', '"poisson"', or '"normal"'.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_scale_x()
Sets the 'scale_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_scale_x(scale_x)
Arguments
scale_xA positive numeric value for scaling (e.g., variance
for "normal" type).
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_scale_y()
Sets the 'scale_y' of the 'iglm.data' object.
Usage
iglm.data_generator$set_scale_y(scale_y)
Arguments
scale_yA positive numeric value for scaling (e.g., variance
for "normal" type).
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_x_attribute()
Sets the 'x_attribute' of the 'iglm.data' object.
Usage
iglm.data_generator$set_x_attribute(x_attribute)
Arguments
x_attributeA numeric vector for the first unit-level attribute.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_y_attribute()
Sets the 'y_attribute' of the 'iglm.data' object.
Usage
iglm.data_generator$set_y_attribute(y_attribute)
Arguments
y_attributeA numeric vector for the first unit-level attribute.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method gather()
Gathers the current state of the 'iglm.data' object into a list.
This includes all attributes, network, and configuration
details necessary to reconstruct the object later.
Usage
iglm.data_generator$gather()
Returns
A list containing the current state of the 'iglm.data' object.
Method set_fix_z_alocal()
Sets the option whether alocal edges are fixed or not.
Usage
iglm.data_generator$set_fix_z_alocal(fix_z_alocal)
Arguments
fix_z_alocalA logical value indicating whether alocal edges should be treated as fixed or not.
Method delete_isolates()
Deletes isolates from the 'z_network' and updates the attributes and neighborhood accordingly.
Isolates are actors that do not have any connections in the 'z_network'. This method identifies such actors, removes them from the attributes and neighborhood, and updates the 'z_network' to reflect the new actor indices.
Usage
iglm.data_generator$delete_isolates()
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method save()
Saves the current state of the 'iglm.data' object to a specified file path
in RDS format. This includes all attributes, network, and configuration
details necessary to reconstruct the object later.
Usage
iglm.data_generator$save(file)
Arguments
file(character) The file where the object state should be saved. Must have a .rds extension.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_fix_x()
Sets the 'fix_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_fix_x(fix_x)
Arguments
fix_xA logical value indicating if 'x_attribute' is fixed or random.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_fix_z()
Sets the 'fix_z' of the 'iglm.data' object.
Usage
iglm.data_generator$set_fix_z(fix_z)
Arguments
fix_zA logical value indicating if 'z_network' is fixed or random.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method mean_z()
Calculates the density of the 'z_network'.
Usage
iglm.data_generator$mean_z()
Returns
A numeric value for the network density.
Method mean_x()
Calculates the mean of the 'x_attribute'.
Usage
iglm.data_generator$mean_x()
Returns
A numeric value for the mean of 'x_attribute'.
Method mean_y()
Calculates the mean of the 'y_attribute'.
Usage
iglm.data_generator$mean_y()
Returns
A numeric value for the mean of 'y_attribute'.
Method x_distribution()
Calculates the distribution of the 'x_attribute'.
Usage
iglm.data_generator$x_distribution(
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
value_range(numeric vector) Optional range of values to consider for the distribution. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns probabilities; if 'FALSE', returns frequencies.
plot(logical) If 'TRUE' (default), plots the distribution using a density plot for continuous data or a bar plot for discrete data.
Returns
A numeric vector representing the distribution of 'x_attribute' (invisible).
Method y_distribution()
Calculates the distribution of the 'y_attribute'.
Usage
iglm.data_generator$y_distribution(
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
value_range(numeric vector) Optional range of values to consider for the distribution. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns probabilities; if 'FALSE', returns frequencies.
plot(logical) If 'TRUE' (default), plots the distribution using a density plot for continuous data or a bar plot for discrete data.
Returns
A numeric vector representing the distribution of 'y_attribute' (invisible).
Method edgewise_shared_partner()
Calculates the matrix of edgewise shared partners.
This is a two-path matrix (e.g., $A A^T$ or $A^T A$).
Usage
iglm.data_generator$edgewise_shared_partner(type = "ALL")
Arguments
type(character) The type of two-path to calculate for directed
networks. Ignored if network is undirected.
Must be one of:
'"OTP"' (Outgoing Two-Path, z_{i,j}\, z_{i,h} \, z_{j,h} ),
'"ISP"' (Ingoing Shared Partner, z_{i,j}\, z_{h,i} \, z_{j,h}),
'"OSP"' (Outgoing Shared Partner, z_{i,j}\, z_{i,h} \, z_{j,h}),
'"ITP"' (Incoming Two-Path, z_{i,j}\, z_{h,i} \, z_{j,h}),
'"ALL"' (Any one of the above).
Default is '"ALL"'.
Returns
A sparse matrix ('dgCMatrix') of shared partner counts.
Method set_neighborhood_overlap()
Sets the neighborhood and overlap matrices.
Usage
iglm.data_generator$set_neighborhood_overlap(neighborhood, overlap)
Arguments
neighborhoodA matrix for a secondary neighborhood.
Can be a 2-column edgelist or a square adjacency matrix.
overlapA matrix for the overlap network.
Can be a 2-column edgelist or a square adjacency matrix.
Returns
None. Updates the internal neighborhood and overlap matrices.
Method dyadwise_shared_partner()
Calculates the matrix of dyadwise shared partners.
Usage
iglm.data_generator$dyadwise_shared_partner(type = "ALL")
Arguments
type(character) The type of two-path to calculate for directed
networks. Ignored if network is undirected.
Must be one of:
'"OTP"' (Outgoing Two-Path, z_{i,h} \, z_{j,h} ),
'"ISP"' (Ingoing Shared Partner, z_{h,i} \, z_{j,h}),
'"OSP"' (Outgoing Shared Partner, z_{i,h} \, z_{j,h}),
'"ITP"' (Incoming Two-Path, z_{h,i} \, z_{j,h}),
'"ALL"' (Any one of the above).
Default is '"ALL"'.
Returns
A sparse matrix ('dgCMatrix') of shared partner counts.
Method geodesic_distances_distribution()
Calculates the geodesic distance distribution of the symmetrized
'z_network'.
Usage
iglm.data_generator$geodesic_distances_distribution(
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
value_range(numeric vector) A vector 'c(min, max)' specifying
the range of distances to tabulate. If 'NULL' (default), the range
is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability
distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of
geodesic distances. Includes 'Inf' for unreachable pairs.
Method geodesic_distances()
Calculates the all-pairs geodesic distance matrix for the
symmetrized 'z_network' using a matrix-based BFS algorithm.
Usage
iglm.data_generator$geodesic_distances()
Returns
A sparse matrix ('dgCMatrix') where 'D[i, j]' is the
shortest path distance from i to j. 'Inf' indicates no path.
Method edgewise_shared_partner_distribution()
Calculates the distribution of edgewise shared partners.
Usage
iglm.data_generator$edgewise_shared_partner_distribution(
type = "ALL",
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
type(character) The type of shared partner matrix to use.
See 'edgewise_shared_partner' for details. Default is '"ALL"'.
value_range(numeric vector) A vector 'c(min, max)' specifying
the range of counts to tabulate. If 'NULL' (default), the range
is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability
distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of
shared partner counts.
Method dyadwise_shared_partner_distribution()
Calculates the distribution of dyadwise shared partners.
Usage
iglm.data_generator$dyadwise_shared_partner_distribution(
type = "ALL",
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
type(character) The type of shared partner matrix to use.
See 'dyadwise_shared_partner' for details. Default is '"ALL"'.
value_range(numeric vector) A vector 'c(min, max)' specifying
the range of counts to tabulate. If 'NULL' (default), the range
is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability
distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of
shared partner counts.
Method degree_distribution()
Calculates the degree distribution of the 'z_network'.
Usage
iglm.data_generator$degree_distribution(
value_range = NULL,
prob = TRUE,
plot = TRUE
)
Arguments
value_range(numeric vector) A vector 'c(min, max)' specifying
the range of degrees to tabulate. If 'NULL' (default), the range
is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability
distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the degree distribution.
Returns
If the network is directed, a list containing two 'table'
objects: 'in_degree' and 'out_degree'. If undirected, a single
'table' object with the degree distribution.
Method degree()
Calculates the degree sequence(s) of the 'z_network'.
Usage
iglm.data_generator$degree()
Returns
If the network is directed, a list containing two vectors:
'in_degree_seq' and 'out_degree_seq'. If undirected, a single
list containing the vector 'degree_seq'.
Method spillover_degree_distribution()
Calculates the spillover degree distribution between actors with
'x_attribute == 1' and actors with 'y_attribute == 1'.
Usage
iglm.data_generator$spillover_degree_distribution(
prob = TRUE,
value_range = NULL,
plot = TRUE
)
Arguments
prob(logical) If 'TRUE' (default), returns a probability
distribution (proportions). If 'FALSE', returns raw counts.
value_range(numeric vector) A vector 'c(min, max)' specifying
the range of degrees to tabulate. If 'NULL' (default), the range
is inferred from the data.
plot(logical) If 'TRUE', plots the distributions.
Returns
A list containing two 'table' objects:
'out_spillover_degree' (from x_i=1 to y_j=1) and
'in_spillover_degree' (from y_i=1 to x_j=1).
Method plot()
Plot the network using 'igraph'.
Visualizes the 'z_network' using the 'igraph' package.
Nodes can be colored by 'x_attribute' and sized by 'y_attribute'.
'neighborhood' edges can be plotted as a background layer.
Usage
iglm.data_generator$plot(
node_color = "x",
node_size = "y",
show_overlap = TRUE,
layout = igraph::layout_with_fr,
network_edges_col = "grey60",
neighborhood_edges_col = "orange",
main = "",
legend_col_n_levels = NULL,
legend_size_n_levels = NULL,
legend_pos = "right",
alpha_neighborhood = 0.2,
edge.width = 1,
edge.arrow.size = 1,
vertex.frame.width = 0.5,
coords = NULL,
legend_size = 0.5,
...
)
Arguments
node_color(character) Attribute to map to node color.
One of '"x"' (default), '"y"', or '"none"'.
node_size(character) Attribute to map to node size.
One of '"y"' (default), '"x"', or '"constant"'.
show_overlap(logical) If 'TRUE' (default), plot the
'neighborhood' edges as a background layer.
layoutAn 'igraph' layout function (e.g., 'igraph::layout_with_fr').
network_edges_col(character) Color for the 'z_network' edges.
neighborhood_edges_col(character) Color for the 'neighborhood' edges.
main(character) The main title for the plot.
legend_col_n_levels(integer) Number of levels for the color legend.
legend_size_n_levels(integer) Number of levels for the size legend.
legend_pos(character) Position of the legend (e.g., '"right"').
alpha_neighborhood(numeric) Alpha transparency for neighborhood edges.
edge.width(numeric) Width of the network edges.
edge.arrow.size(numeric) Size of the arrowheads for directed edges.
vertex.frame.width(numeric) Width of the vertex frame.
coords(matrix) Optional matrix of x-y coordinates for node layout.
legend_size(numeric) Scaling factor for the size legend.
...Additional arguments passed to 'plot.igraph'.
Returns
A list containing the 'igraph' object ('graph') and the
layout coordinates ('coords'), invisibly.
Method print()
Print a summary of the 'iglm.data' object to the console.
Usage
iglm.data_generator$print(digits = 3, ...)
Arguments
digits(integer) Number of digits to round numeric output to.
...Additional arguments (not used).
Returns
The object's private environment, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
iglm.data_generator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
iglm documentation built on April 23, 2026, 5:07 p.m.
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| iglm.data_generator | R Documentation |
Networks with Unit-Level Attributes (R6 Class)
Description
The 'iglm.data' class is a container for storing, validating, and analyzing unit-level attributes (x_attribute, y_attribute) and connections (z_network).
Active bindings
x_attribute('numeric') The vector for the first unit-level attribute.
y_attribute('numeric') The vector for the second unit-level attribute.
z_network('matrix') The primary network structure as a 2-column integer edgelist.
neighborhood('matrix') Read-only. The secondary/neighborhood structure as a 2-column integer edgelist. An empty matrix if not provided.
overlap(‘matrix') Read-only. The calculated overlap relation (dyads with shared neighbors in 'neighborhood') as a 2-column integer edgelist. An empty matrix if overlap hasn’t been computed or is not available.
directed('logical') Indicates if the 'z_network' is treated as directed.
n_actor('integer') The total number of actors (nodes) in the network.
type_x('character') The specified distribution type for the 'x_attribute'.
type_y('character') The specified distribution type for the 'y_attribute'.
scale_x('numeric') The scale parameter associated with the 'x_attribute'.
scale_y('numeric') The scale parameter associated with the 'y_attribute'.
fix_x('logical') Indicates if the 'x_attribute' is fixed during estimation/simulation.
fix_z('logical') RIndicates if the 'z_network' is fixed during estimation/simulation.
descriptives('list')A list storing computed descriptive statistics for the network and attributes.
fix_z_alocal('logical') Flag indicating whether nonoverlap edges are treated as random.
Methods
Public methods
Method new()
Create a new 'iglm.data' object, that includes data on two attributes and one network.
Usage
iglm.data_generator$new( x_attribute = NULL, y_attribute = NULL, z_network = NULL, neighborhood = NULL, directed = NA, n_actor = NA, type_x = "binomial", type_y = "binomial", scale_x = 1, scale_y = 1, fix_x = FALSE, fix_z = FALSE, fix_z_alocal = TRUE, return_neighborhood = TRUE, file = NULL )
Arguments
x_attributeA numeric vector for the first unit-level attribute.
y_attributeA numeric vector for the second unit-level attribute.
z_networkA matrix representing the network. Can be a 2-column edgelist or a square adjacency matrix.
neighborhoodAn optional matrix for the neighborhood representing local dependence. Can be a 2-column edgelist or a square adjacency matrix. A tie in 'neighborhood' between actor i and j indicates that j is in the neighborhood of i, implying dependence between the respective actors.
directedA logical value indicating if 'z_network' is directed. If 'NA' (default), directedness is inferred from the symmetry of 'z_network'.
n_actorAn integer for the number of actors in the system. If 'NA' (default), 'n_actor' is inferred from the attributes or network matrices.
type_xCharacter string for the type of 'x_attribute'. Must be one of '"binomial"', '"poisson"', or '"normal"'. Default is '"binomial"'.
type_yCharacter string for the type of 'y_attribute'. Must be one of '"binomial"', '"poisson"', or '"normal"'. Default is '"binomial"'.
scale_xA positive numeric value for scaling (e.g., variance for "normal" type). Default is 1.
scale_yA positive numeric value for scaling (e.g., variance for "normal" type). Default is 1.
fix_xLogical. If 'TRUE', the 'x_attribute' is treated as fixed during model estimation and simulation. Default is 'FALSE'.
fix_zLogical. If 'TRUE', the 'z_network' is treated as fixed during model estimation and simulation. Default is 'FALSE'.
fix_z_alocalLogical. If 'TRUE' (default), alocal dyads in the neighborhood are fixed.
return_neighborhoodLogical. If 'TRUE' (default) and 'neighborhood' is 'NULL', a full neighborhood (all dyads) is generated implying global dependence. If 'FALSE', no neighborhood is set.
file(character) Optional file path to load a saved 'iglm.data' object state.
Returns
A new 'iglm.data' object.
Method set_z_network()
Sets the 'z_network' of the 'iglm.data' object.
Usage
iglm.data_generator$set_z_network(z_network)
Arguments
z_networkA matrix representing the network. Can be a 2-column edgelist or a square adjacency matrix. @return The 'iglm.data' object itself ('self'), invisibly.
Method set_type_x()
Sets the 'type_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_type_x(type_x)
Arguments
type_xA character string for the type of 'x_attribute'. Must be one of '"binomial"', '"poisson"', or '"normal"'. @return The 'iglm.data' object itself ('self'), invisibly.
Method set_type_y()
Sets the 'type_y' of the 'iglm.data' object.
Usage
iglm.data_generator$set_type_y(type_y)
Arguments
type_yA character string for the type of 'y_attribute'. Must be one of '"binomial"', '"poisson"', or '"normal"'.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_scale_x()
Sets the 'scale_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_scale_x(scale_x)
Arguments
scale_xA positive numeric value for scaling (e.g., variance for "normal" type).
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_scale_y()
Sets the 'scale_y' of the 'iglm.data' object.
Usage
iglm.data_generator$set_scale_y(scale_y)
Arguments
scale_yA positive numeric value for scaling (e.g., variance for "normal" type).
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_x_attribute()
Sets the 'x_attribute' of the 'iglm.data' object.
Usage
iglm.data_generator$set_x_attribute(x_attribute)
Arguments
x_attributeA numeric vector for the first unit-level attribute.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_y_attribute()
Sets the 'y_attribute' of the 'iglm.data' object.
Usage
iglm.data_generator$set_y_attribute(y_attribute)
Arguments
y_attributeA numeric vector for the first unit-level attribute.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method gather()
Gathers the current state of the 'iglm.data' object into a list. This includes all attributes, network, and configuration details necessary to reconstruct the object later.
Usage
iglm.data_generator$gather()
Returns
A list containing the current state of the 'iglm.data' object.
Method set_fix_z_alocal()
Sets the option whether alocal edges are fixed or not.
Usage
iglm.data_generator$set_fix_z_alocal(fix_z_alocal)
Arguments
fix_z_alocalA logical value indicating whether alocal edges should be treated as fixed or not.
Method delete_isolates()
Deletes isolates from the 'z_network' and updates the attributes and neighborhood accordingly. Isolates are actors that do not have any connections in the 'z_network'. This method identifies such actors, removes them from the attributes and neighborhood, and updates the 'z_network' to reflect the new actor indices.
Usage
iglm.data_generator$delete_isolates()
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method save()
Saves the current state of the 'iglm.data' object to a specified file path in RDS format. This includes all attributes, network, and configuration details necessary to reconstruct the object later.
Usage
iglm.data_generator$save(file)
Arguments
file(character) The file where the object state should be saved. Must have a .rds extension.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_fix_x()
Sets the 'fix_x' of the 'iglm.data' object.
Usage
iglm.data_generator$set_fix_x(fix_x)
Arguments
fix_xA logical value indicating if 'x_attribute' is fixed or random.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method set_fix_z()
Sets the 'fix_z' of the 'iglm.data' object.
Usage
iglm.data_generator$set_fix_z(fix_z)
Arguments
fix_zA logical value indicating if 'z_network' is fixed or random.
Returns
The 'iglm.data' object itself ('self'), invisibly.
Method mean_z()
Calculates the density of the 'z_network'.
Usage
iglm.data_generator$mean_z()
Returns
A numeric value for the network density.
Method mean_x()
Calculates the mean of the 'x_attribute'.
Usage
iglm.data_generator$mean_x()
Returns
A numeric value for the mean of 'x_attribute'.
Method mean_y()
Calculates the mean of the 'y_attribute'.
Usage
iglm.data_generator$mean_y()
Returns
A numeric value for the mean of 'y_attribute'.
Method x_distribution()
Calculates the distribution of the 'x_attribute'.
Usage
iglm.data_generator$x_distribution( value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
value_range(numeric vector) Optional range of values to consider for the distribution. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns probabilities; if 'FALSE', returns frequencies.
plot(logical) If 'TRUE' (default), plots the distribution using a density plot for continuous data or a bar plot for discrete data.
Returns
A numeric vector representing the distribution of 'x_attribute' (invisible).
Method y_distribution()
Calculates the distribution of the 'y_attribute'.
Usage
iglm.data_generator$y_distribution( value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
value_range(numeric vector) Optional range of values to consider for the distribution. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns probabilities; if 'FALSE', returns frequencies.
plot(logical) If 'TRUE' (default), plots the distribution using a density plot for continuous data or a bar plot for discrete data.
Returns
A numeric vector representing the distribution of 'y_attribute' (invisible).
Method edgewise_shared_partner()
Calculates the matrix of edgewise shared partners. This is a two-path matrix (e.g., $A A^T$ or $A^T A$).
Usage
iglm.data_generator$edgewise_shared_partner(type = "ALL")
Arguments
type(character) The type of two-path to calculate for directed networks. Ignored if network is undirected. Must be one of: '"OTP"' (Outgoing Two-Path,
z_{i,j}\, z_{i,h} \, z_{j,h}), '"ISP"' (Ingoing Shared Partner,z_{i,j}\, z_{h,i} \, z_{j,h}), '"OSP"' (Outgoing Shared Partner,z_{i,j}\, z_{i,h} \, z_{j,h}), '"ITP"' (Incoming Two-Path,z_{i,j}\, z_{h,i} \, z_{j,h}), '"ALL"' (Any one of the above). Default is '"ALL"'.
Returns
A sparse matrix ('dgCMatrix') of shared partner counts.
Method set_neighborhood_overlap()
Sets the neighborhood and overlap matrices.
Usage
iglm.data_generator$set_neighborhood_overlap(neighborhood, overlap)
Arguments
neighborhoodA matrix for a secondary neighborhood. Can be a 2-column edgelist or a square adjacency matrix.
overlapA matrix for the overlap network. Can be a 2-column edgelist or a square adjacency matrix.
Returns
None. Updates the internal neighborhood and overlap matrices.
Method dyadwise_shared_partner()
Calculates the matrix of dyadwise shared partners.
Usage
iglm.data_generator$dyadwise_shared_partner(type = "ALL")
Arguments
type(character) The type of two-path to calculate for directed networks. Ignored if network is undirected. Must be one of: '"OTP"' (Outgoing Two-Path,
z_{i,h} \, z_{j,h}), '"ISP"' (Ingoing Shared Partner,z_{h,i} \, z_{j,h}), '"OSP"' (Outgoing Shared Partner,z_{i,h} \, z_{j,h}), '"ITP"' (Incoming Two-Path,z_{h,i} \, z_{j,h}), '"ALL"' (Any one of the above). Default is '"ALL"'.
Returns
A sparse matrix ('dgCMatrix') of shared partner counts.
Method geodesic_distances_distribution()
Calculates the geodesic distance distribution of the symmetrized 'z_network'.
Usage
iglm.data_generator$geodesic_distances_distribution( value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
value_range(numeric vector) A vector 'c(min, max)' specifying the range of distances to tabulate. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of geodesic distances. Includes 'Inf' for unreachable pairs.
Method geodesic_distances()
Calculates the all-pairs geodesic distance matrix for the symmetrized 'z_network' using a matrix-based BFS algorithm.
Usage
iglm.data_generator$geodesic_distances()
Returns
A sparse matrix ('dgCMatrix') where 'D[i, j]' is the shortest path distance from i to j. 'Inf' indicates no path.
Method edgewise_shared_partner_distribution()
Calculates the distribution of edgewise shared partners.
Usage
iglm.data_generator$edgewise_shared_partner_distribution( type = "ALL", value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
type(character) The type of shared partner matrix to use. See 'edgewise_shared_partner' for details. Default is '"ALL"'.
value_range(numeric vector) A vector 'c(min, max)' specifying the range of counts to tabulate. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of shared partner counts.
Method dyadwise_shared_partner_distribution()
Calculates the distribution of dyadwise shared partners.
Usage
iglm.data_generator$dyadwise_shared_partner_distribution( type = "ALL", value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
type(character) The type of shared partner matrix to use. See 'dyadwise_shared_partner' for details. Default is '"ALL"'.
value_range(numeric vector) A vector 'c(min, max)' specifying the range of counts to tabulate. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the distribution.
Returns
A named vector (a 'table' object) with the distribution of shared partner counts.
Method degree_distribution()
Calculates the degree distribution of the 'z_network'.
Usage
iglm.data_generator$degree_distribution( value_range = NULL, prob = TRUE, plot = TRUE )
Arguments
value_range(numeric vector) A vector 'c(min, max)' specifying the range of degrees to tabulate. If 'NULL' (default), the range is inferred from the data.
prob(logical) If 'TRUE' (default), returns a probability distribution (proportions). If 'FALSE', returns raw counts.
plot(logical) If 'TRUE', plots the degree distribution.
Returns
If the network is directed, a list containing two 'table' objects: 'in_degree' and 'out_degree'. If undirected, a single 'table' object with the degree distribution.
Method degree()
Calculates the degree sequence(s) of the 'z_network'.
Usage
iglm.data_generator$degree()
Returns
If the network is directed, a list containing two vectors: 'in_degree_seq' and 'out_degree_seq'. If undirected, a single list containing the vector 'degree_seq'.
Method spillover_degree_distribution()
Calculates the spillover degree distribution between actors with 'x_attribute == 1' and actors with 'y_attribute == 1'.
Usage
iglm.data_generator$spillover_degree_distribution( prob = TRUE, value_range = NULL, plot = TRUE )
Arguments
prob(logical) If 'TRUE' (default), returns a probability distribution (proportions). If 'FALSE', returns raw counts.
value_range(numeric vector) A vector 'c(min, max)' specifying the range of degrees to tabulate. If 'NULL' (default), the range is inferred from the data.
plot(logical) If 'TRUE', plots the distributions.
Returns
A list containing two 'table' objects: 'out_spillover_degree' (from x_i=1 to y_j=1) and 'in_spillover_degree' (from y_i=1 to x_j=1).
Method plot()
Plot the network using 'igraph'.
Visualizes the 'z_network' using the 'igraph' package. Nodes can be colored by 'x_attribute' and sized by 'y_attribute'. 'neighborhood' edges can be plotted as a background layer.
Usage
iglm.data_generator$plot( node_color = "x", node_size = "y", show_overlap = TRUE, layout = igraph::layout_with_fr, network_edges_col = "grey60", neighborhood_edges_col = "orange", main = "", legend_col_n_levels = NULL, legend_size_n_levels = NULL, legend_pos = "right", alpha_neighborhood = 0.2, edge.width = 1, edge.arrow.size = 1, vertex.frame.width = 0.5, coords = NULL, legend_size = 0.5, ... )
Arguments
node_color(character) Attribute to map to node color. One of '"x"' (default), '"y"', or '"none"'.
node_size(character) Attribute to map to node size. One of '"y"' (default), '"x"', or '"constant"'.
show_overlap(logical) If 'TRUE' (default), plot the 'neighborhood' edges as a background layer.
layoutAn 'igraph' layout function (e.g., 'igraph::layout_with_fr').
network_edges_col(character) Color for the 'z_network' edges.
neighborhood_edges_col(character) Color for the 'neighborhood' edges.
main(character) The main title for the plot.
legend_col_n_levels(integer) Number of levels for the color legend.
legend_size_n_levels(integer) Number of levels for the size legend.
legend_pos(character) Position of the legend (e.g., '"right"').
alpha_neighborhood(numeric) Alpha transparency for neighborhood edges.
edge.width(numeric) Width of the network edges.
edge.arrow.size(numeric) Size of the arrowheads for directed edges.
vertex.frame.width(numeric) Width of the vertex frame.
coords(matrix) Optional matrix of x-y coordinates for node layout.
legend_size(numeric) Scaling factor for the size legend.
...Additional arguments passed to 'plot.igraph'.
Returns
A list containing the 'igraph' object ('graph') and the layout coordinates ('coords'), invisibly.
Method print()
Print a summary of the 'iglm.data' object to the console.
Usage
iglm.data_generator$print(digits = 3, ...)
Arguments
digits(integer) Number of digits to round numeric output to.
...Additional arguments (not used).
Returns
The object's private environment, invisibly.
Method clone()
The objects of this class are cloneable with this method.
Usage
iglm.data_generator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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