factrix.multi_factor.partial_conjunction_across_metrics ¶

partial_conjunction_across_metrics(results: list[EvaluationResult], *, metrics: list[str], min_pass: int, q: float = 0.05) -> CrossMetricPartialConjunctionResult

Test whether each factor identity passes at least k of m metrics.

Metric labels are the predeclared condition axis. For each result, the Bonferroni-style partial-conjunction p-value is computed across the fixed m=len(metrics) endpoints, then BHY runs across factor identities. insufficient_* endpoints are conservatively assigned p=1 rather than shrinking m; identities with fewer than min_pass active endpoints remain in the audit output but do not enter the outer BHY family.

Parameters:

Name	Type	Description	Default
`results`	`list[EvaluationResult]`	Unique :class:`EvaluationResult` hypotheses. Horizon and all `params` remain part of each factor identity.	required
`metrics`	`list[str]`	At least two unique, predeclared inferential metric labels.	required
`min_pass`	`int`	`k` in the claim "at least k of m metrics carry signal". Must satisfy `2 <= min_pass <= len(metrics)`.	required
`q`	`float`	Nominal FDR target for BHY across factor identities.	`0.05`

Returns:

Name	Type	Description
`A`	`CrossMetricPartialConjunctionResult`	class:`CrossMetricPartialConjunctionResult` with factor-level
	`CrossMetricPartialConjunctionResult`	survivors and the underlying metric hypotheses retained for audit.

Raises:

Type	Description
`UserInputError`	Inputs, metric p-values, or `min_pass` violate the declared k-of-m contract.

When to use it¶

Use this procedure for a predeclared factor-level claim such as "the factor has signal on at least two of IC, beta, and spread." Metric labels are the fixed condition axis: each factor identity receives one k-of-m partial-conjunction p-value, followed by BHY across identities.

screen = fx.multi_factor.partial_conjunction_across_metrics(
    results,
    metrics=["ic", "beta", "spread"],
    min_pass=2,
    q=0.05,
)

screen.to_frame()
# factor | pc_p | adj_p | survived | active
#        | n_tests | n_active | n_passed_uncorr

This differs from bhy_across_metrics: pooled BHY selects factor × metric cells, while partial conjunction returns factor identities supported by at least k endpoints.

Fixed-m data-shortage rule¶

The declared metric list fixes m. An insufficient_* endpoint is retained in hypotheses and conservatively enters the k-of-m calculation as p=1; it is never deleted in a way that would lower the confirmation bar. If fewer than min_pass endpoints are active, that identity remains visible with active=False and empty PC/adjusted p-values, and does not enter the outer BHY family.

Descriptive endpoints and other invalid p-values fail loudly. The function does not implement min_pass=1 any-metric promotion.

Result fields¶

Field	Meaning
`entries`	Every tested factor identity, in input order
`hypotheses`	Underlying result × metric cells retained for audit
`pc_p_all`	Raw k-of-m p-value per identity; NaN when fewer than `k` endpoints are active
`adj_p_all`	BHY-adjusted PC p-value per identity
`survivors` / `adj_p`	Passing factor identities and their adjusted p-values
`metrics` / `min_pass`	Declared m endpoints and required k
`n_tests`	Fixed condition count m per identity
`n_active`	Computable endpoint count per identity
`n_identities`	Identities entering the outer BHY family

factrix.multi_factor.CrossMetricPartialConjunctionResult `dataclass` ¶

CrossMetricPartialConjunctionResult(entries: list[EvaluationResult], hypotheses: list[MetricHypothesis], adj_p_all: ndarray, pc_p_all: ndarray, q: float, metrics: tuple[str, ...], min_pass: int, n_tests: Mapping[tuple[Any, ...], int], n_active: Mapping[tuple[Any, ...], int], n_identities: int, n_passed_uncorr_all: ndarray)

Bases: _ScreenResultMixin

Factor-level k-of-m metric confirmation followed by BHY.

to_frame ¶

to_frame() -> DataFrame

Return one row per factor identity, including ineligible rows.