gbh: gbh: Grouped Benjamini Hochberg

Description Usage Arguments Value Functions References Examples

View source: R/stratified_methods.R

Description

gbh: Grouped Benjamini Hochberg

tst_gbh: wrapper for gbh with method="TST" lsl_gbh: wrapper for gbh with method="LSL"

Usage

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gbh(unadj_p, groups, alpha, method = "TST", pi0_global = "weighted_average")

tst_gbh(unadj_p, groups, alpha, ...)

lsl_gbh(unadj_p, groups, alpha, ...)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

groups

Factor to which different hypotheses belong

alpha

Significance level at which to apply method

method

What pi0 estimator should be used (available "TST","LSL")

pi0_global

GBH requires also a pi0 estimate for the marginal p-value distribution. Can either apply pi0 estimation method to all p-values (pi0_global="global") or use a weigted averarage (pi0_global="weighted_average") of the pi0 estimates within each stratum. This is not explicitly stated in the paper, but based on a reproduction of their paper figures it seems to be the weighted_average.

...

Additional arguments passed from tst_gbh/lsl_gbh to gbh

Value

GBH multiple testing object

Functions

References

Hu, James X., Hongyu Zhao, and Harrison H. Zhou. "False discovery rate control with groups." Journal of the American Statistical Association 105.491 (2010).

Examples

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     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     sim_df$group <- groups_by_filter(sim_df$filterstat, 20)
     obj <- tst_gbh(sim_df$pvalue, sim_df$group, .1)
     sum(rejected_hypotheses(obj))

nignatiadis/ihwPaper documentation built on Jan. 18, 2021, 3:13 p.m.