Description Usage Arguments Value Functions References Examples
View source: R/stratified_methods.R
gbh: Grouped Benjamini Hochberg
tst_gbh: wrapper for gbh with method="TST" lsl_gbh: wrapper for gbh with method="LSL"
1 2 3 4 5 |
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 |
GBH multiple testing object
tst_gbh
: Wrapper of GBH with TST pi0 estimator
lsl_gbh
: Wrapper of GBH with LSL pi0 estimator
Hu, James X., Hongyu Zhao, and Harrison H. Zhou. "False discovery rate control with groups." Journal of the American Statistical Association 105.491 (2010).
1 2 3 4 | 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))
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