Evaluate edge recovery against a known network

Usage

evaluate_edge_recovery(truth, estimate, threshold = 0)

Arguments

truth, estimate: Supported network representations.
threshold: Absolute threshold defining a recovered edge: a dyad counts as present when abs(weight) > threshold.

Value

A tidy simulab_sim base data.frame with one row per dyad in the union of the two edge sets – dyads absent from both networks are not represented, so true negatives are not counted – and columns from, to, truth_weight, estimate_weight, truth_present, estimate_present, recovered, false_positive, and false_negative. A one-row summary table (precision, recall, f1, true_positive, false_positive, false_negative) is available through as.data.frame().

Examples

truth <- simulate_network(nodes = 25, model = "bernoulli", probability = 0.15, seed = 1)
estimate <- simulate_network(nodes = 25, model = "bernoulli", probability = 0.15, seed = 2)
evaluate_edge_recovery(truth, estimate)
#> <simulab_sim:edge_recovery> 171 rows x 9 columns
#>    from to truth_weight estimate_weight truth_present estimate_present
#> 1     1  6            1               0          TRUE            FALSE
#> 2     1  9            0               1         FALSE             TRUE
#> 3     1 11            0               1         FALSE             TRUE
#> 4     1 19            0               1         FALSE             TRUE
#> 5     1 23            1               0          TRUE            FALSE
#> 6     1 25            0               1         FALSE             TRUE
#> 7     2  9            1               0          TRUE            FALSE
#> 8     2 10            1               0          TRUE            FALSE
#> 9     2 18            1               0          TRUE            FALSE
#> 10    2 20            1               1          TRUE             TRUE
#>    recovered false_positive false_negative
#> 1      FALSE          FALSE           TRUE
#> 2      FALSE           TRUE          FALSE
#> 3      FALSE           TRUE          FALSE
#> 4      FALSE           TRUE          FALSE
#> 5      FALSE          FALSE           TRUE
#> 6      FALSE           TRUE          FALSE
#> 7      FALSE          FALSE           TRUE
#> 8      FALSE          FALSE           TRUE
#> 9      FALSE          FALSE           TRUE
#> 10      TRUE          FALSE          FALSE
#> ... 161 more rows
#> 
#> Other tables: summary. Read one with as.data.frame(x, what = "summary").