Computes (and on subsequent calls re-computes) the [`StatisticsResult-class`] for the pairwise metric tables. R has no built-in `cached_property` equivalent for S4 slots, so this is recomputed each call. Cache externally via `stats <- statistics(result)` if you need to access it many times.
Examples
ds <- methods::new("VennDataset",
set_names = c("A", "B"),
items = list(A = c("x", "y"), B = c("y", "z")),
item_order = c("x", "y", "z"),
universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
stats <- statistics(result)
stats@jaccard["A", "B"]
#> [1] 0.3333333
# \donttest{
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
stats <- statistics(result)
stats@jaccard
#> Vogelstein COSMIC_CGC OncoKB IntOGen
#> Vogelstein 1.0000000 0.2124789 0.1058158 0.1898148
#> COSMIC_CGC 0.2124789 1.0000000 0.4719740 0.4697337
#> OncoKB 0.1058158 0.4719740 1.0000000 0.3439077
#> IntOGen 0.1898148 0.4697337 0.3439077 1.0000000
stats@hypergeometric
#> set_a set_b intersection expected p_value p_adjusted
#> 1 COSMIC_CGC OncoKB 581 35.76055 0.000000e+00 0.000000e+00
#> 2 COSMIC_CGC IntOGen 388 18.38865 0.000000e+00 0.000000e+00
#> 3 OncoKB IntOGen 477 38.96115 0.000000e+00 0.000000e+00
#> 4 Vogelstein COSMIC_CGC 126 4.00890 6.751534e-184 1.012730e-183
#> 5 Vogelstein IntOGen 123 4.36770 4.613517e-171 5.536220e-171
#> 6 Vogelstein OncoKB 131 8.49390 3.131045e-151 3.131045e-151
#> significant highly_significant
#> 1 TRUE TRUE
#> 2 TRUE TRUE
#> 3 TRUE TRUE
#> 4 TRUE TRUE
#> 5 TRUE TRUE
#> 6 TRUE TRUE
# }