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Binarize Edge Weights

Source: R/wrangle-weights.R
binarize.Rd

Replaces every surviving weight with 1 and drops edges whose weight is at or below the threshold. The operation corresponds to sna::event2dichot() with an absolute threshold.

Usage

binarize(
  x,
  threshold = 0,
  absolute = TRUE,
  signed = FALSE,
  keep_isolates = TRUE,
  keep_format = FALSE,
  directed = NULL
)

Arguments

x

Network input.

threshold

Numeric. Edges whose weight exceeds this value are kept and set to 1. Default 0, which keeps every existing edge.

absolute

Logical. Compare abs(weight). Default TRUE, so a correlation network keeps its strong negative edges.

signed

Logical. If TRUE, negative edges become -1, which preserves the sign of the association. Default FALSE.

keep_isolates

Logical. Keep nodes that end up with no edges? Default TRUE.

keep_format

Logical. Return the input format when TRUE.

directed

Logical or NULL. If NULL (default), auto-detect.

Value

A cograph_network whose weights are all 1 (or, when signed = TRUE, 1 for a positive edge and -1 for a negative one), or the input format when keep_format = TRUE. Nodes left without edges are kept and reported in a cograph_isolates_created warning, unless keep_isolates = FALSE.

References

Butts, C. T. (2008). Social network analysis with sna. Journal of Statistical Software, 24(6), 1–51.

See also

threshold_edges, normalize_weights

Examples

binarize(regulation_net, threshold = 0.1)
#> Cograph network: 10 nodes, 27 edges ( directed )
#> Source: matrix 
#>   Nodes (10): Explore, Plan, Monitor, Adapt, Reflect, Discuss, ... +4 more
#>   Edges: 27 / 90 (density: 30.0%)
#>   Weights: [1.000, 1.000]  |  mean: 1.000
#>   Strongest edges:
#>     Adapt -> Explore  1.000
#>     Discuss -> Explore  1.000
#>     Create -> Explore  1.000
#>     Synthesize -> Plan  1.000
#>     Share -> Plan  1.000
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.