Threshold Edges by Weight, Count, Proportion or Density

Keeps the edges that satisfy every criterion supplied. This is the network equivalent of qgraph's minimum/cut arguments and of tna::prune(), except that it returns a network rather than a plot setting, so the thresholded network can be analysed, not only drawn.

Usage

threshold_edges(
  x,
  minimum = NULL,
  maximum = NULL,
  proportion = NULL,
  density = NULL,
  top = NULL,
  absolute = TRUE,
  keep_isolates = TRUE,
  keep_format = FALSE,
  directed = NULL
)

Arguments

x: Network input: cograph_network, matrix, igraph, network, tna, or an edge-list data frame.
minimum: Numeric. Keep edges whose weight is at least this value.
maximum: Numeric. Keep edges whose weight is at most this value.
proportion: Numeric in (0, 1]. Keep this fraction of the edges, the strongest first.
density: Numeric in (0, 1]. Keep as many of the strongest edges as gives this density (edges as a fraction of the possible edges).
top: Integer. Keep this many edges, the strongest first.
absolute: Logical. Compare abs(weight) rather than the signed weight. Default TRUE, which is what correlation and partial-correlation networks need. minimum/maximum and the ranking used by proportion, density and top both follow this flag.
keep_isolates: Logical. Keep nodes that end up with no edges? Default TRUE. Set FALSE, or call remove_isolates(), to drop them.
keep_format: Logical. Return the input format when TRUE.
directed: Logical or NULL. If NULL (default), auto-detect.

Value

A cograph_network with the surviving edges, or the input format when keep_format = TRUE. Every node is kept unless keep_isolates = FALSE; nodes the threshold stranded are reported in a cograph_isolates_created warning. An out-of-range minimum, maximum, proportion, density or top raises a cograph_bad_selection error.

Details

When several criteria are given they are combined with AND: for example threshold_edges(x, minimum = 0.2, top = 20) keeps the twenty strongest edges among those of weight at least 0.2.

Ties at the cut point are all kept, so top = 10 can return more than ten edges when the tenth and eleventh weights are equal. This is deliberate: breaking ties on edge order would make the result depend on how the network was built.

References

Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18.

Examples

adj <- matrix(c(0, .5, .8, 0,
                .5, 0, .3, .6,
                .8, .3, 0, .4,
                 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")

threshold_edges(adj, minimum = 0.5)
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: matrix 
#>   Nodes (4): A, B, C, D
#>   Edges: 3 / 6 (density: 50.0%)
#>   Weights: [0.500, 0.800]  |  mean: 0.633
#>   Strongest edges:
#>     A -- C  0.800
#>     B -- D  0.600
#>     A -- B  0.500
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.
threshold_edges(adj, top = 2)
#> Cograph network: 4 nodes, 2 edges ( undirected )
#> Source: matrix 
#>   Nodes (4): A, B, C, D
#>   Edges: 2 / 6 (density: 33.3%)
#>   Weights: [0.600, 0.800]  |  mean: 0.700
#>   Strongest edges:
#>     A -- C  0.800
#>     B -- D  0.600
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.
threshold_edges(adj, density = 0.5)
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: matrix 
#>   Nodes (4): A, B, C, D
#>   Edges: 3 / 6 (density: 50.0%)
#>   Weights: [0.500, 0.800]  |  mean: 0.633
#>   Strongest edges:
#>     A -- C  0.800
#>     B -- D  0.600
#>     A -- B  0.500
#> Layout: none 
#>   Use as.data.frame() for the edge table, as.data.frame(what = "nodes") for the nodes.