factrix.multi_factor¶
Collection-level false-discovery-rate (FDR) control across a list of
EvaluationResult objects. Use after evaluate
has produced results for candidate factors (or per factor × params
combinations): the functions in this module adjust traceable
factor/context/metric hypotheses for multiple testing under the dependence
structure that factor pools exhibit by construction.
This page is a module-level index. Each function has its own page covering call shape, parameters, the result containers, and design rationale.
Choosing a function¶
| Question you are asking | Function | Page |
|---|---|---|
| "Which factors in this candidate pool survive FDR ≤ q under arbitrary dependence?" | bhy |
api/bhy |
| "Which factor × metric cells survive one pooled FDR family?" | bhy_across_metrics |
api/bhy-across-metrics |
"Which factors are significant in at least k of m replication conditions?" |
partial_conjunction |
api/partial-conjunction |
"Which factors have signal on at least k of m predeclared metrics?" |
partial_conjunction_across_metrics |
api/partial-conjunction-across-metrics |
| "Which factor families carry signal, and which factors within each surviving family survive?" | bhy_hierarchical |
api/bhy-hierarchical |
Start with bhy when one metric defines the screen. Use a cross-metric function
only when the predeclared selection rule may choose among metric labels or
requires confirmation on at least k endpoints; use the hierarchical function
only for a predeclared group structure.
See also¶
-
Large-scale evaluation
How to structure factor screens using a user-side batched loop with Polars LazyFrames.
-
Statistical methods — multiple testing
Why Benjamini-Hochberg-Yekutieli (BHY) rather than Bayesian or reality-check / SPA bootstraps; positive regression dependence on a subset (PRDS) and the harmonic dependence correction.