Returns a 1-row tibble summarizing the analysis: number of sets, number of non-empty regions, total unique items, hypergeometric universe size, resolved model name, whether the layout is approximate (proportional 3-set), and the count of statistically significant / highly significant pairs (FDR-adjusted q < 0.05 / < 0.001).
Value
A 1-row tibble (or data.frame fallback) with columns: `n_sets`, `n_regions`, `n_items`, `universe_size`, `model`, `is_approximate`, `n_significant`, `n_highly_significant`.
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)
if (requireNamespace("broom", quietly = TRUE)) broom::glance(result)
#> # A tibble: 1 × 8
#> n_sets n_regions n_items universe_size model is_approximate n_significant
#> <int> <int> <int> <int> <chr> <lgl> <int>
#> 1 2 3 3 10 venn-2-set FALSE 0
#> # ℹ 1 more variable: n_highly_significant <int>
# \donttest{
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
broom::glance(result)
#> # A tibble: 1 × 8
#> n_sets n_regions n_items universe_size model is_approximate n_significant
#> <int> <int> <int> <int> <chr> <lgl> <int>
#> 1 4 15 20000 20000 venn-4-set FALSE 6
#> # ℹ 1 more variable: n_highly_significant <int>
# }