Networks with Unit-Level Attributes (R6 Class) — iglm.data

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

label_x: (`character`) Label/name for `x_attribute`.
label_y: (`character`) Label/name for `y_attribute`.
label_z: (`character`) Label/name for `z_network`.
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_units: (`integer`) The total number of units (nodes) in the network.
n_actor: (`integer`) Deprecated alias for `n_units`.
family_x: (`character`) The specified distribution family for the `x_attribute`.
family_y: (`character`) The specified distribution family 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`) Indicates 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 (alocal) edges are treated as fixed.

Methods

Public methods

iglm.data$new()
iglm.data$set_z_network()
iglm.data$set_family_x()
iglm.data$set_family_y()
iglm.data$set_scale_x()
iglm.data$set_scale_y()
iglm.data$set_x_attribute()
iglm.data$set_y_attribute()
iglm.data$set_label_x()
iglm.data$set_label_y()
iglm.data$set_label_z()
iglm.data$gather()
iglm.data$set_fix_z_alocal()
iglm.data$delete_isolates()
iglm.data$save()
iglm.data$set_fix_x()
iglm.data$set_fix_z()
iglm.data$mean_z()
iglm.data$mean_x()
iglm.data$mean_y()
iglm.data$x_distribution()
iglm.data$x_dist()
iglm.data$y_distribution()
iglm.data$y_dist()
iglm.data$edgewise_shared_partner()
iglm.data$set_neighborhood_overlap()
iglm.data$dyadwise_shared_partner()
iglm.data$geodesic_distances_distribution()
iglm.data$geodesic_distances()
iglm.data$esp()
iglm.data$esp_dist()
iglm.data$dsp()
iglm.data$dsp_dist()
iglm.data$geo()
iglm.data$geodist()
iglm.data$geo_dist()
iglm.data$edgewise_shared_partner_distribution()
iglm.data$dyadwise_shared_partner_distribution()
iglm.data$degree_distribution()
iglm.data$degree()
iglm.data$deg()
iglm.data$deg_dist()
iglm.data$degree_dist()
iglm.data$plot()
iglm.data$print()
iglm.data$clone()

`iglm.data$new()`

Create a new `iglm.data` object, that includes data on two attributes and one network.

Usage

iglm.data$new(
  x_attribute = NULL,
  y_attribute = NULL,
  z_network = NULL,
  neighborhood = NULL,
  directed = NA,
  n_units = NA,
  family_x = "binomial",
  family_y = "binomial",
  scale_x = 1,
  scale_y = 1,
  fix_x = FALSE,
  fix_z = FALSE,
  fix_z_alocal = TRUE,
  return_neighborhood = TRUE,
  file = NULL,
  label_x = "x",
  label_y = "y",
  label_z = "z",
  n_actor = NULL
)

Arguments

x_attribute: A numeric vector for the first unit-level attribute.
y_attribute: A numeric vector for the second unit-level attribute.
z_network: A matrix representing the network. Can be a 2-column edgelist or a square adjacency matrix.
neighborhood: An optional matrix for the neighborhood representing local dependence. Can be a 2-column edgelist or a square adjacency matrix. A tie in `neighborhood` between unit i and j indicates that j is in the neighborhood of i, implying dependence between the respective units.
directed: A logical value indicating if `z_network` is directed. If `NA` (default), directedness is inferred from the symmetry of `z_network`.
n_units: An integer for the number of units in the system. If `NA` (default), `n_units` is inferred from the attributes or network matrices.
family_x: Character string for the family of `x_attribute`. Must be one of `"binomial"`, `"poisson"`, or `"normal"`. Default is `"binomial"`.
family_y: Character string for the family of `y_attribute`. Must be one of `"binomial"`, `"poisson"`, or `"normal"`. Default is `"binomial"`.
scale_x: A positive numeric value for scaling (e.g., variance for "normal" type). Default is 1.
scale_y: A positive numeric value for scaling (e.g., variance for "normal" type). Default is 1.
fix_x: Logical. If `TRUE`, the `x_attribute` is treated as fixed during model estimation and simulation. Default is `FALSE`.
fix_z: Logical. If `TRUE`, the `z_network` is treated as fixed during model estimation and simulation. Default is `FALSE`.
fix_z_alocal: Logical. If `TRUE` (default), edges outside the overlap region (alocal dyads) are fixed.
return_neighborhood: Logical. 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.
label_x: Character string for the label/name of `x_attribute`. Default is `"x"`.
label_y: Character string for the label/name of `y_attribute`. Default is `"y"`.
label_z: Character string for the label/name of `z_network`. Default is `"z"`.
n_actor: (Deprecated) An integer for the number of units. Please use n_units instead.

Returns

A new `iglm.data` object.

`iglm.data$set_z_network()`

Sets the `z_network` of the `iglm.data` object.

Usage

iglm.data$set_z_network(z_network)

Arguments

z_network: A matrix representing the network. Can be a 2-column edgelist or a square adjacency matrix. @return The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_family_x()`

Sets the `family_x` of the `iglm.data` object.

Usage

iglm.data$set_family_x(family_x)

Arguments

family_x: A character string for the family of `x_attribute`. Must be one of `"binomial"`, `"poisson"`, or `"normal"`. @return The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_family_y()`

Sets the `family_y` of the `iglm.data` object.

Usage

iglm.data$set_family_y(family_y)

Arguments

family_y: A character string for the family of `y_attribute`. Must be one of `"binomial"`, `"poisson"`, or `"normal"`.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_scale_x()`

Sets the `scale_x` of the `iglm.data` object.

Usage

iglm.data$set_scale_x(scale_x)

Arguments

scale_x: A positive numeric value for scaling (e.g., variance for "normal" type).

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_scale_y()`

Sets the `scale_y` of the `iglm.data` object.

Usage

iglm.data$set_scale_y(scale_y)

Arguments

scale_y: A positive numeric value for scaling (e.g., variance for "normal" type).

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_x_attribute()`

Sets the `x_attribute` of the `iglm.data` object.

Usage

iglm.data$set_x_attribute(x_attribute)

Arguments

x_attribute: A numeric vector for the first unit-level attribute.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_y_attribute()`

Sets the `y_attribute` of the `iglm.data` object.

Usage

iglm.data$set_y_attribute(y_attribute)

Arguments

y_attribute: A numeric vector for the first unit-level attribute.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_label_x()`

Sets the label for the `x_attribute`.

Usage

iglm.data$set_label_x(label_x)

Arguments

label_x: A character string for the label of `x_attribute`.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_label_y()`

Sets the label for the `y_attribute`.

Usage

iglm.data$set_label_y(label_y)

Arguments

label_y: A character string for the label of `y_attribute`.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_label_z()`

Sets the label for the `z_network`.

Usage

iglm.data$set_label_z(label_z)

Arguments

label_z: A character string for the label of `z_network`.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$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$gather()

Returns

A list containing the current state of the `iglm.data` object.

`iglm.data$set_fix_z_alocal()`

Sets the option whether alocal edges are fixed or not.

Usage

iglm.data$set_fix_z_alocal(fix_z_alocal)

Arguments

fix_z_alocal: A logical value indicating whether alocal edges should be treated as fixed or not.

`iglm.data$delete_isolates()`

Deletes isolates from the `z_network` and updates the attributes and neighborhood accordingly. Isolates are units that do not have any connections in the `z_network`. This method identifies such units, removes them from the attributes and neighborhood, and updates the `z_network` to reflect the new unit indices.

Usage

iglm.data$delete_isolates()

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$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$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.

`iglm.data$set_fix_x()`

Sets the `fix_x` of the `iglm.data` object.

Usage

iglm.data$set_fix_x(fix_x)

Arguments

fix_x: A logical value indicating if `x_attribute` is fixed or random.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$set_fix_z()`

Sets the `fix_z` of the `iglm.data` object.

Usage

iglm.data$set_fix_z(fix_z)

Arguments

fix_z: A logical value indicating if `z_network` is fixed or random.

Returns

The `iglm.data` object itself (`self`), invisibly.

`iglm.data$mean_z()`

Calculates the density of the `z_network`.

Usage

iglm.data$mean_z()

Returns

A numeric value for the network density.

`iglm.data$mean_x()`

Calculates the mean of the `x_attribute`.

Usage

iglm.data$mean_x()

Returns

A numeric value for the mean of `x_attribute`.

`iglm.data$mean_y()`

Calculates the mean of the `y_attribute`.

Usage

iglm.data$mean_y()

Returns

A numeric value for the mean of `y_attribute`.

`iglm.data$x_distribution()`

Calculates the distribution of the `x_attribute`.

Usage

iglm.data$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 proportions; if `FALSE`, returns counts.
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).

`iglm.data$x_dist()`

Short alias for `x_distribution`.

Usage

iglm.data$x_dist(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.

Returns

A numeric vector representing the distribution of `x_attribute` (invisible).

`iglm.data$y_distribution()`

Calculates the distribution of the `y_attribute`.

Usage

iglm.data$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 proportions; if `FALSE`, returns counts.
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).

`iglm.data$y_dist()`

Short alias for `y_distribution`.

Usage

iglm.data$y_dist(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.

Returns

A numeric vector representing the distribution of `y_attribute` (invisible).

`iglm.data$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$edgewise_shared_partner(
  type = "ALL",
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

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"`.
mode: (character) Either `"global"` (default) to evaluate across all edges, or `"local"` to evaluate only edges with overlapping neighborhoods (from `overlap`).
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A numeric vector of shared partner counts for edges.

`iglm.data$set_neighborhood_overlap()`

Sets the neighborhood and overlap matrices.

Usage

iglm.data$set_neighborhood_overlap(neighborhood, overlap)

Arguments

neighborhood: A matrix for a secondary neighborhood. Can be a 2-column edgelist or a square adjacency matrix.
overlap: A 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.

`iglm.data$dyadwise_shared_partner()`

Calculates the matrix of dyadwise shared partners.

Usage

iglm.data$dyadwise_shared_partner(
  type = "ALL",
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

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"`.
mode: (character) Either `"global"` (default) to evaluate across all dyads, or `"local"` to evaluate only dyads with overlapping neighborhoods.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A sparse matrix (`dgCMatrix`) of shared partner counts.

`iglm.data$geodesic_distances_distribution()`

Calculates the geodesic distance distribution of the symmetrized `z_network`.

Usage

iglm.data$geodesic_distances_distribution(
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

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.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) Either `"global"` (default) to evaluate across all node pairs, or `"local"` to evaluate only pairs with overlapping neighborhoods (from `overlap`).

Returns

A named vector (a `table` object) with the distribution of geodesic distances. Includes `Inf` for unreachable pairs.

`iglm.data$geodesic_distances()`

Calculates the all-pairs geodesic distance matrix for the symmetrized `z_network` using a matrix-based BFS algorithm.

Usage

iglm.data$geodesic_distances(
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

Arguments

mode: (character) Either `"global"` (default) to evaluate across all pairs, or `"local"` to evaluate only pairs with overlapping neighborhoods.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A sparse matrix (`dgCMatrix`) where `D[i, j]` is the shortest path distance from i to j. `Inf` indicates no path.

`iglm.data$esp()`

Short alias for `edgewise_shared_partner`.

Usage

iglm.data$esp(
  type = "ALL",
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

Arguments

type: (character) The type of two-path to calculate. Default is `"ALL"`.
mode: (character) `"global"` (default) or `"local"`.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A numeric vector of shared partner counts for edges.

`iglm.data$esp_dist()`

Short alias for `edgewise_shared_partner_distribution`.

Usage

iglm.data$esp_dist(
  type = "ALL",
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

type: (character) The type of two-path to calculate. Default is `"ALL"`.
value_range: (numeric vector) Range of counts to tabulate.
prob: (logical) If `TRUE` (default), returns proportions.
plot: (logical) If `TRUE`, plots the distribution.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

A named vector with the distribution.

`iglm.data$dsp()`

Short alias for `dyadwise_shared_partner`.

Usage

iglm.data$dsp(
  type = "ALL",
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

Arguments

type: (character) The type of two-path to calculate. Default is `"ALL"`.
mode: (character) `"global"` (default) or `"local"`.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A sparse matrix (`dgCMatrix`) of shared partner counts.

`iglm.data$dsp_dist()`

Short alias for `dyadwise_shared_partner_distribution`.

Usage

iglm.data$dsp_dist(
  type = "ALL",
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

type: (character) The type of two-path to calculate. Default is `"ALL"`.
value_range: (numeric vector) Range of counts to tabulate.
prob: (logical) If `TRUE` (default), returns proportions.
plot: (logical) If `TRUE`, plots the distribution.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

A named vector with the distribution.

`iglm.data$geo()`

Short alias for `geodesic_distances`.

Usage

iglm.data$geo(mode = "global", x_i = NULL, x_j = NULL, y_i = NULL, y_j = NULL)

Arguments

mode: (character) `"global"` (default) or `"local"`.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A sparse matrix (`dgCMatrix`) of geodesic distances.

`iglm.data$geodist()`

Short alias for `geodesic_distances`.

Usage

iglm.data$geodist(
  mode = "global",
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL
)

Arguments

mode: (character) `"global"` (default) or `"local"`.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.

Returns

A sparse matrix (`dgCMatrix`) of geodesic distances.

`iglm.data$geo_dist()`

Short alias for `geodesic_distances_distribution`.

Usage

iglm.data$geo_dist(
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

value_range: (numeric vector) Range of distances to tabulate.
prob: (logical) If `TRUE` (default), returns proportions.
plot: (logical) If `TRUE`, plots the distribution.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

A named vector with the distribution.

`iglm.data$edgewise_shared_partner_distribution()`

Calculates the distribution of edgewise shared partners.

Usage

iglm.data$edgewise_shared_partner_distribution(
  type = "ALL",
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

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.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) Either `"global"` (default) to evaluate across all edges, or `"local"` to evaluate only edges with overlapping neighborhoods.

Returns

A named vector (a `table` object) with the distribution of shared partner counts.

`iglm.data$dyadwise_shared_partner_distribution()`

Calculates the distribution of dyadwise shared partners.

Usage

iglm.data$dyadwise_shared_partner_distribution(
  type = "ALL",
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

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.
x_i: (optional) Sender attribute constraint.
x_j: (optional) Receiver attribute constraint.
y_i: (optional) Sender attribute constraint.
y_j: (optional) Receiver attribute constraint.
mode: (character) Either `"global"` (default) to evaluate across all dyads, or `"local"` to evaluate only dyads with overlapping neighborhoods.

Returns

A named vector (a `table` object) with the distribution of shared partner counts.

`iglm.data$degree_distribution()`

Calculates the degree distribution of the `z_network`.

A flexible, general function for evaluating network connectivity across global networks, local neighborhoods, attribute-defined subgroups, and cross-group spillover pathways.

Topological Scope (`mode`)

"global" (default): Evaluates degree distributions across all dyads in the network.
"local": Evaluates local degree distributions restricted strictly to unit pairs that share an overlapping neighborhood (overlap).

Directionality and Bipartite Subgroups

Directed networks: Always returns both out_degree (ties sent) and in_degree (ties received).
Undirected networks: Returns a single overall degree distribution when unconstrained or single-side constrained. When bilateral constraints are supplied (e.g., sender $i$ and receiver $j$), ties are evaluated directionally ($i \to j$), returning both out_degree and in_degree.

Spillover and Subgroup Conditioning

Any combination of sender attributes (x_i, y_i) and receiver attributes (x_j, y_j) can be specified to measure spillover dynamics:

out_degree: Distribution of ties sent from matching senders $i$ to matching receivers $j$ (spillover sending capacity).
in_degree: Distribution of ties received by matching receivers $j$ from matching senders $i$ (spillover exposure).

Supported Constraint Formats & Internal Handling

Attribute constraints (x_i, x_j, y_i, y_j) accept:

Exact scalar values: For binary attributes, matches units with that exact value (e.g., x_i = 1).
Continuous / Count shortcuts: When an attribute is continuous ("normal") or count ("poisson"), setting 1 internally selects above-mean units ($x_i > \bar{x}$), and 0 selects below-or-equal-to-mean units ($x_i \le \bar{x}$). Any other numeric value $v$ matches units with exact value $v$.
Discrete value sets: Vectors such as x_i = c(1, 2) match units with any value in that set.
Filtering functions: Custom functions (vectorized or scalar), e.g., x_i = function(x) x > 0.5 or y_j = \(y) if (y > 2) TRUE else FALSE.

Plotting and Axis Labels

When plot = TRUE, mathematical expressions are formatted automatically for the x-axis:

Exact values and sets display as $x_i == 1$ or $x_i == \text{c(1, 2)}$.
Continuous shortcuts display with sample mean bars as $x_i > \bar{x}$ or $x_i \le \bar{x}$.
Filtering functions display as $x_i == \text{"fn"}$.

Usage

iglm.data$degree_distribution(
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

value_range: (numeric vector or list) A vector c(min, max) specifying the range of degrees to tabulate, or a list with in_degree and out_degree. If NULL (default), ranges are 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 barplot(s).
x_i: (optional) Exact value, vector, or filtering function for attribute x of sender unit $i$.
x_j: (optional) Exact value, vector, or filtering function for attribute x of receiver unit $j$.
y_i: (optional) Exact value, vector, or filtering function for attribute y of sender unit $i$.
y_j: (optional) Exact value, vector, or filtering function for attribute y of receiver unit $j$.
mode: (character) Either "global" (default) to evaluate across all dyads, or "local" to evaluate only ties within overlapping neighborhoods (overlap).

Returns

If the network is directed or if bilateral constraints are provided, a list containing two table objects: out_degree and in_degree. If undirected without bilateral constraints, a single table object with the degree distribution.

Examples

data(copenhagen)

# Overall degree distribution
copenhagen$degree_distribution(prob = FALSE, plot = FALSE)

# Spillover degree distribution (female to male call connections)
copenhagen$deg_dist(
  x_i = 1, # Sender: female
  x_j = 0, # Receiver: male
  mode = "local",
  plot = FALSE
)

`iglm.data$degree()`

Calculates the degree sequence(s) of the `z_network`.

General function for calculating unit-level degree sequences across global topologies, local neighborhoods, attribute-defined subsets, or directional spillover pathways.

Usage

iglm.data$degree(
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

x_i: (optional) Exact value, vector, or filtering function for attribute x of sender unit $i$.
x_j: (optional) Exact value, vector, or filtering function for attribute x of receiver unit $j$.
y_i: (optional) Exact value, vector, or filtering function for attribute y of sender unit $i$.
y_j: (optional) Exact value, vector, or filtering function for attribute y of receiver unit $j$.
mode: (character) "global" (default) or "local".

Returns

If the network is directed or if asymmetric/bipartite bilateral constraints are given, a list containing two numeric vectors: out_degree_seq and in_degree_seq. If undirected without constraints or with identical bilateral constraints, a list containing the vector degree_seq.

Examples

data(copenhagen)

# Global degree sequence
copenhagen$degree()

# Local spillover degree sequence with filtering functions
copenhagen$deg(
  x_i = function(x) x > 2,
  y_j = function(y) y > 2,
  mode = "local"
)

`iglm.data$deg()`

Short alias for `degree`.

Usage

iglm.data$deg(x_i = NULL, x_j = NULL, y_i = NULL, y_j = NULL, mode = "global")

Arguments

x_i: Optional sender attribute constraint.
x_j: Optional receiver attribute constraint.
y_i: Optional sender attribute constraint.
y_j: Optional receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

Degree sequence(s).

`iglm.data$deg_dist()`

Short alias for `degree_distribution`. Supports standard, local, and attribute-constrained (spillover) degree distributions.

Usage

iglm.data$deg_dist(
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

value_range: Optional range of degrees to tabulate.
prob: (logical) If `TRUE`, returns proportions.
plot: (logical) If `TRUE`, plots the distribution.
x_i: Optional sender attribute constraint.
x_j: Optional receiver attribute constraint.
y_i: Optional sender attribute constraint.
y_j: Optional receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

Degree distribution table(s).

`iglm.data$degree_dist()`

Alias for `deg_dist` / `degree_distribution`.

Usage

iglm.data$degree_dist(
  value_range = NULL,
  prob = TRUE,
  plot = TRUE,
  x_i = NULL,
  x_j = NULL,
  y_i = NULL,
  y_j = NULL,
  mode = "global"
)

Arguments

value_range: Optional range of degrees to tabulate.
prob: (logical) If `TRUE`, returns proportions.
plot: (logical) If `TRUE`, plots the distribution.
x_i: Optional sender attribute constraint.
x_j: Optional receiver attribute constraint.
y_i: Optional sender attribute constraint.
y_j: Optional receiver attribute constraint.
mode: (character) `"global"` (default) or `"local"`.

Returns

Degree distribution table(s).

`iglm.data$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$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.
layout: An `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.

`iglm.data$print()`

Print a summary of the `iglm.data` object to the console.

Usage

iglm.data$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.

`iglm.data$clone()`

The objects of this class are cloneable with this method.

Usage

iglm.data$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `iglm.data$degree_distribution()`
## ------------------------------------------------

data(copenhagen)

# Overall degree distribution
copenhagen$degree_distribution(prob = FALSE, plot = FALSE)

# Spillover degree distribution (female to male call connections)
copenhagen$deg_dist(
  x_i = 1, # Sender: female
  x_j = 0, # Receiver: male
  mode = "local",
  plot = FALSE
)

## ------------------------------------------------
## Method `iglm.data$degree()`
## ------------------------------------------------

data(copenhagen)

# Global degree sequence
copenhagen$degree()
#> $degree_seq
#>   [1] 13 28  4 27  5 11 26  4 14  1 15  5  4 11  2  9 20  4  7  5 56 14 14 13 16
#>  [26] 24 11 11 13  1 18  8 38 24 10  3  6  9 22  5  6  9  6  6  4  9 21  3 26  8
#>  [51] 47 15 15  7 13 14  8 40  5  5 34 14 23 12 12  4 12  4  6  4 13 24  7  5  4
#>  [76]  2 34 12  8 11 44  7  4  6 10  7  7 11 20  8 13  8  8  3  6  2  7  6  4  6
#> [101]  3 10  7  5  9 11  8 22 54  8 15 17 31 13  9 21  7 13  5  7 32 12 13 12 23
#> [126]  1  7 11  5 24 10 16 15 13 45 26 12 10  9  8  3  8 11 50  5  6  3 13 20  9
#> [151]  5  1 11  2  6 36  4 14 10 10  1 13 19 15  7  6 12 14 30 14 19  9  6  4  5
#> [176] 29 29 18 40 20  5 15  5 21 14  4  5  6 10 20 11  3 11  8 10 14  5 21 38 13
#> [201] 15 21 19  5 11 16 15  8  8 11  9 20  3  2  9  2 12  9 12 11  5 25  5 10 17
#> [226]  9  4  7 17  1 14  5 15  5 21  5  3  7 16 10 53  5 18  9  8 15  7 12  5 65
#> [251]  1 30  9 13  8  1 17  5  1  9 33  7 15  8 11  5  8  9 29  6  3  4  9 39 39
#> [276]  6 32  6 10  7 27 11  3 11  7  9 11 21 14  2  3 16  6  9  1 12  6  9  9  7
#> [301] 14  9  5  8 10 11 10 34 13 11  9 21  5  5  7 14 16  3  3  6  5 24 17 12 10
#> [326]  8  3 27 12 11 12  7  7  2  6  7  5 10 13  8  4 13  6  8 11 12 27  4 18 17
#> [351]  4 11  5  7 28  4  3 24  5 10  8 15  5  6  1 19  7  7 28 15 12 11  9  8  9
#> [376] 13 25 25  6  3 12 11  5 10  8 22 47 10  8  5 47  1 12 18 14 18  8  4  7 10
#> [401] 41 11  1  8  2 11 14  8 13
#> 

# Local spillover degree sequence with filtering functions
copenhagen$deg(
  x_i = function(x) x > 2,
  y_j = function(y) y > 2,
  mode = "local"
)
#> $out_degree_seq
#> numeric(0)
#> 
#> $in_degree_seq
#>   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>