py3plex.algorithms.hedwig.stats package¶
Submodules¶
py3plex.algorithms.hedwig.stats.adjustment module¶
Multiple-testing adjustment methods.
@author: anze.vavpetic@ijs.si
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py3plex.algorithms.hedwig.stats.adjustment.fdr(ruleset, q=0.05)¶ The Benjamini-Hochberg-Yekutieli direct adjustment method to control the FDR.
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py3plex.algorithms.hedwig.stats.adjustment.fwer(ruleset, alpha=0.05)¶ The Holm-Bonferroni direct adjustment method to control the FWER.
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py3plex.algorithms.hedwig.stats.adjustment.none(ruleset)¶
py3plex.algorithms.hedwig.stats.scorefunctions module¶
Score function definitions.
@author: anze.vavpetic@ijs.si
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py3plex.algorithms.hedwig.stats.scorefunctions.chisq(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.enrichment_score(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.interesting(rule)¶ Checks if a given rule is interesting for the given score function
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py3plex.algorithms.hedwig.stats.scorefunctions.kaplan_meier_AUC(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.leverage(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.lift(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.precision(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.t_score(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.wracc(rule)¶
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py3plex.algorithms.hedwig.stats.scorefunctions.z_score(rule)¶
py3plex.algorithms.hedwig.stats.significance module¶
Significance testing methods.
@author: anze.vavpetic@ijs.si
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py3plex.algorithms.hedwig.stats.significance.apply_fisher(ruleset)¶ Fisher’s exact test to test rule significance.
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py3plex.algorithms.hedwig.stats.significance.fisher(rule)¶ Fisher’s p-value for one rule.
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py3plex.algorithms.hedwig.stats.significance.is_redundant(rule, new_rule)¶ Computes the redundancy coefficient of a new rule compared to its immediate generalization.
Rules with a coeff > 1 are deemed non-redundant.
py3plex.algorithms.hedwig.stats.validate module¶
Module for ruleset validation.
@author: anze.vavpetic@ijs.si