Skip to contents

Create a netify matrix from cross-sectional dyadic data

Source: R/get_adjacency.R
get_adjacency.Rd

get_adjacency converts cross-sectional dyadic data into an adjacency matrix of class "netify". This function creates a single network matrix representing relationships at one point in time.

Usage

get_adjacency(
  dyad_data,
  actor1 = NULL,
  actor2 = NULL,
  symmetric = TRUE,
  mode = "unipartite",
  weight = NULL,
  sum_dyads = FALSE,
  diag_to_NA = TRUE,
  missing_to_zero = TRUE
)

Arguments

dyad_data

A data.frame containing dyadic observations. Will be coerced to data.frame if a tibble or data.table is provided.

actor1

Character string specifying the column name for the first actor in each dyad.

actor2

Character string specifying the column name for the second actor in each dyad.

symmetric

Logical. If TRUE (default), treats the network as undirected (i.e., edges have no direction). If FALSE, treats the network as directed.

mode

Character string specifying network structure. Options are:

  • "unipartite": One set of actors (default)

  • "bipartite": Two distinct sets of actors

weight

Character string specifying the column name containing edge weights. If NULL (default), edges are treated as unweighted (binary).

sum_dyads

Logical. If TRUE, sums weight values when multiple edges exist between the same actor pair. If FALSE (default), uses the last observed value.

diag_to_NA

Logical. If TRUE (default), sets diagonal values (self-loops) to NA. Automatically set to FALSE for bipartite networks.

missing_to_zero

Logical. If TRUE (default), treats missing edges as zeros. If FALSE, missing edges remain as NA.

Value

A matrix of class "netify" (a netify matrix) with:

  • Dimensions: [n_actors x n_actors] for unipartite networks or [n_actors1 x n_actors2] for bipartite networks

  • Class: "netify" - this is a full netify object compatible with all netify functions

  • Attributes: Metadata including network properties and processing parameters

The returned object is a netify matrix that can be used with all netify functions such as summary(), plot(), to_igraph(), etc.

Details

Note on usage:

While this function is exported and available for direct use, the primary and recommended way to create netify objects from dyadic data is through the netify() function. The netify() function:

  • Provides a consistent interface for both cross-sectional and longitudinal data

  • Includes additional data validation and preprocessing options

  • Can incorporate nodal and dyadic attributes during creation

  • Offers more comprehensive parameter checking

Use get_adjacency() directly only when you need a simple adjacency matrix creation without additional features.

Author

Ha Eun Choi, Cassy Dorff, Colin Henry, Shahryar Minhas

Examples

# Load example data
data(icews)

# Subset to one year for cross-sectional analysis
icews_2010 <- icews[icews$year == 2010, ]

# Create a directed network with verbal cooperation weights
verbCoop_net <- get_adjacency(
  dyad_data = icews_2010, 
  actor1 = 'i', 
  actor2 = 'j',
  symmetric = FALSE, 
  weight = 'verbCoop'
)

# Create a directed network with material conflict weights
matlConf_net <- get_adjacency(
  dyad_data = icews_2010, 
  actor1 = 'i', 
  actor2 = 'j',
  symmetric = FALSE, 
  weight = 'matlConf'
)

# Verify class
class(verbCoop_net)  # "netify"
#> [1] "netify"

# Check dimensions
dim(verbCoop_net)
#> [1] 152 152