Package 'IHWpaper' reference manual

Title:	Reproduce figures in IHW paper
Description:	This package conveniently wraps all functions needed to reproduce the figures in the IHW paper (https://www.nature.com/articles/nmeth.3885) and the data analysis in https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12411, cf. the arXiv preprint (http://arxiv.org/abs/1701.05179). Thus it is a companion package to the Bioconductor IHW package.
Authors:	Nikos Ignatiadis [aut, cre]
Maintainer:	Nikos Ignatiadis <[email protected]>
License:	Artistic-2.0
Version:	1.41.0
Built:	2026-10-10 10:40:13 UTC
Source:	https://github.com/bioc/IHWpaper

analyze_dataset: Basically performs preprocessing and then returns analyzed RNASeq dataset (diff. expression) , i.e. the DESeq2 result whose p-values and baseMean statistics can then be used with IHW

Description

analyze_dataset: Basically performs preprocessing and then returns analyzed RNASeq dataset (diff. expression) , i.e. the DESeq2 result whose p-values and baseMean statistics can then be used with IHW

Usage

analyze_dataset(dataset = c("airway", "bottomly", "hammer"), res = TRUE)
analyze_dataset(dataset = c("airway", "bottomly", "hammer"), res = TRUE)

Arguments

dataset

Character, name of dataset to be preprocessed, only 3 choices currently available ("airway","bottomly","hammer")

res

(default TRUE): return result table, rather than DESeq2 object

Value

Preprocessed dataset

Examples

bottomly <- analyze_dataset("bottomly")

bottomly <- analyze_dataset("bottomly")

bh: Wrapper for Benjamini Hochberg

Description

bh: Wrapper for Benjamini Hochberg

Usage

bh(unadj_p, alpha)
bh(unadj_p, alpha)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

alpha

Significance level at which to apply method

Value

BH multiple testing object

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- bh(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- bh(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

bonf: Wrapper for Bonferroni

Description

bonf: Wrapper for Bonferroni

Usage

bonf(unadj_p, alpha)
bonf(unadj_p, alpha)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

alpha

Significance level at which to apply method

Value

Bonferroni multiple testing object

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- bonf(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- bonf(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

clfdr: Cai's local fdr based method

Description

clfdr: Cai's local fdr based method

Usage

clfdr(unadj_p, groups, alpha, lfdr_estimation = "fdrtool")
clfdr(unadj_p, groups, alpha, lfdr_estimation = "fdrtool")

Arguments

unadj_p

Numeric vector of unadjusted p-values.

groups

Factor to which different hypotheses belong

alpha

Significance level at which to apply method

lfdr_estimation

Method used to estimate the loca fdr, defaults to "fdrtool"

Value

Clfdr multiple testing object

References

Cai, T. Tony, and Wenguang Sun. "Simultaneous testing of grouped hypotheses: Finding needles in multiple haystacks." Journal of the American Statistical Association 104.488 (2009).

Examples

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

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

Benchmarking wrapper: Given a multiple testing method, convert it so that it takes a simulation object (see simulation function) and a nominal level alpha as inputs

Description

Benchmarking wrapper: Given a multiple testing method, convert it so that it takes a simulation object (see simulation function) and a nominal level alpha as inputs

Usage

continuous_wrap(mt_method, nbins = 20)
continuous_wrap(mt_method, nbins = 20)

Arguments

mt_method

Multiple testing method (e.g. a function such as gbh or ddhf)

nbins

Integer, number of equally sized bins into which to stratify hypotheses

Value

A new multiple testing function which has an interface of the form f(sim_data_frame, alpha)

Examples

     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))

     tst_gbh_continuous <- continuous_wrap(tst_gbh)
     obj2 <- tst_gbh_continuous(sim_df, .1)
     sum(rejected_hypotheses(obj2))

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))

     tst_gbh_continuous <- continuous_wrap(tst_gbh)
     obj2 <- tst_gbh_continuous(sim_df, .1)
     sum(rejected_hypotheses(obj2))

ddhf: Greedy independent filtering

Description

ddhf: Greedy independent filtering

Usage

ddhf(unadj_p, filterstat, alpha)
ddhf(unadj_p, filterstat, alpha)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

filterstat

Factor to which different hypotheses belong

alpha

Significance level at which to apply method

Value

DDHF multiple testing object

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- ddhf(sim_df$pvalue, sim_df$filterstat, .1)
     sum(rejected_hypotheses(obj))

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- ddhf(sim_df$pvalue, sim_df$filterstat, .1)
     sum(rejected_hypotheses(obj))

t-test simulation: Simulate rowwise t-tests

Description

t-test simulation: Simulate rowwise t-tests

Usage

du_ttest_sim(
  m,
  pi0,
  effect_size,
  n_samples = 10,
  uninformative_filter = FALSE,
  seed = NULL
)

du_ttest_sim_fun(
  m,
  pi0,
  effect_size,
  n_samples = 10,
  uninformative_filter = FALSE
)
du_ttest_sim(
  m,
  pi0,
  effect_size,
  n_samples = 10,
  uninformative_filter = FALSE,
  seed = NULL
)

du_ttest_sim_fun(
  m,
  pi0,
  effect_size,
  n_samples = 10,
  uninformative_filter = FALSE
)

Arguments

m

Integer, total number of hypotheses

pi0

Numeric, proportion of null hypotheses

effect_size

Numeric, the alternative hypotheses will be

n_samples

Integer, number of samples for t-test, i.e. the comparison will be n_samples/2 vs n_samples/2

uninformative_filter

Boolean, if TRUE will generate uniformly distributed filter statistic Otherwise will use the pooled standard deviations

seed

Integer, Random seed to be used for simulation (default: NULL, i.e. RNG state will be used as is)

Value

A data frame containing all information about the simulation experiment

Functions

du_ttest_sim_fun: Creates a closure function for a given seed

Examples

sim_df <- du_ttest_sim(20000,0.95, 1.5)

sim_df <- du_ttest_sim(20000,0.95, 1.5)

gbh: Grouped Benjamini Hochberg

Description

gbh: Grouped Benjamini Hochberg

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

Usage

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

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

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

tst_gbh: Wrapper of GBH with TST pi0 estimator
lsl_gbh: Wrapper of GBH with LSL pi0 estimator

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

     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))

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))

IHW wrappers

Description

IHW wrappers

Usage

ihw_naive(unadj_p, filterstat, alpha)

ihw_ecdf_5fold(unadj_p, filterstat, alpha)

ihw_5fold(unadj_p, filterstat, alpha)

ihw_5fold_reg(unadj_p, filterstat, alpha)

ihw_bonf_5fold_reg(unadj_p, filterstat, alpha)

ihw_storey_5fold(unadj_p, filterstat, alpha)
ihw_naive(unadj_p, filterstat, alpha)

ihw_ecdf_5fold(unadj_p, filterstat, alpha)

ihw_5fold(unadj_p, filterstat, alpha)

ihw_5fold_reg(unadj_p, filterstat, alpha)

ihw_bonf_5fold_reg(unadj_p, filterstat, alpha)

ihw_storey_5fold(unadj_p, filterstat, alpha)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

filterstat

Factor to which different hypotheses belong

alpha

Significance level at which to apply method

Details

These are closures, which apply IHW with custom prespecified parameters. These correspond to interesting settings, for which it is convenient to be able to immediately call the corresponding functions, rather than having to specify parameters each time. Thus they make it easier to benchmark. All of these wrappers are defined in 2 lines of code, so the settings pertaining to each one can be inspected by typing the functions name into the console.

Value

ihwResult multiple testing object

Functions

ihw_naive: IHW naive
ihw_ecdf_5fold: IHW (E2) with 5 folds
ihw_5fold: IHW (E1-E2) with 5 folds
ihw_5fold_reg: IHW (E1-E2-E3) with 5 folds
ihw_bonf_5fold_reg: IHW-Bonferroni (E1-E2-E3) with 5 folds
ihw_storey_5fold: IHW (E1-E2) with 5 folds and Storey's pi0 estimator

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- ihw_5fold(sim_df$pvalue, sim_df$filterstat, .1)
     sum(rejected_hypotheses(obj))

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- ihw_5fold(sim_df$pvalue, sim_df$filterstat, .1)
     sum(rejected_hypotheses(obj))

LSL (Least-Slope) pi0 estimator

Description

LSL (Least-Slope) pi0 estimator

Usage

lsl_pi0_est(pvalue)
lsl_pi0_est(pvalue)

Arguments

pvalue

Numeric vector of unadjusted p-values.

Value

estimated proportion of null hypotheses (pi0)

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     lsl_pi0_est(sim_df$pvalue)

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     lsl_pi0_est(sim_df$pvalue)

Null simulation: Generate uniformly distributed p-values and covariates

Description

Null simulation: Generate uniformly distributed p-values and covariates

Usage

null_sim(m, seed = NULL)

null_sim_fun(m)
null_sim(m, seed = NULL)

null_sim_fun(m)

Arguments

m

Integer, total number of hypotheses

seed

Integer, Random seed to be used for simulation (default: NULL, i.e. RNG state will be used as is)

Value

A data frame containing all information about the simulation experiment

Functions

null_sim_fun: Creates a closure function for a given seed

Examples

sim_df <- null_sim(20000)

sim_df <- null_sim(20000)

helper function to create nice legends

Description

helper function to create nice legends

Usage

pretty_legend(gg, last_vals, xmin, fontsize = 13)
pretty_legend(gg, last_vals, xmin, fontsize = 13)

Arguments

gg

ggplot2 object

last_vals

data frame with columns label, colour, last_vals (i.e. place label with colour at y-coordinate last_vals)

xmin

Numeric, x axis position at which labels should be placed

fontsize

Integer, fontsize

Value

Another ggplot2 object

This replaces the default legend of a ggplot2 object. In particular, given a ggplot2 object ,it removes the existing legend and then places new labels based on the annotation data frame 'last_vals' (see parameter description) at a given x-coordinate of the original plot.

This function can be attributed to and is described in more detail in the following blog post: http://www.r-bloggers.com/coloring-and-drawing-outside-the-lines-in-ggplot/

Examples

  library("ggplot2")
  labels  <- c("A","B","C")
  mypoints <- rbind(data.frame(y=1:3, x=1, label=as.factor(labels)),
                     data.frame(y=2:4, x=2, label=as.factor(labels)))
  mycolours <- c("#F8766D","#00BA38","#619CFF")
  gg <- ggplot(mypoints,aes(x=x,y=y,color=label)) + 
               geom_line(size=2) + 
               scale_color_manual(values=mycolours) +
               xlim(c(0,2.2))
  gg
  annotation_df <- data.frame(colour=mycolours, last_vals=2:4, label=labels)
  pretty_legend(gg, annotation_df, 2.1)

library("ggplot2")
  labels  <- c("A","B","C")
  mypoints <- rbind(data.frame(y=1:3, x=1, label=as.factor(labels)),
                     data.frame(y=2:4, x=2, label=as.factor(labels)))
  mycolours <- c("#F8766D","#00BA38","#619CFF")
  gg <- ggplot(mypoints,aes(x=x,y=y,color=label)) + 
               geom_line(size=2) + 
               scale_color_manual(values=mycolours) +
               xlim(c(0,2.2))
  gg
  annotation_df <- data.frame(colour=mycolours, last_vals=2:4, label=labels)
  pretty_legend(gg, annotation_df, 2.1)

run_evals: Main function to benchmark FDR methods on given simulations.

Description

run_evals: Main function to benchmark FDR methods on given simulations.

Usage

run_evals(sim_funs, fdr_methods, nreps, alphas, ...)
run_evals(sim_funs, fdr_methods, nreps, alphas, ...)

Arguments

sim_funs

List of simulation settings

fdr_methods

List of FDR controlling methods to be benchmarked

nreps

Integer, number of Monte Carlo replicates for the simulations

alphas

Numeric, vector of nominal significance levels at which to apply FDR controlling methods

...

Additional arguments passed to sim_fun_eval

Details

This is the main workhorse function which runs all simulation benchmarks for IHWpaper. It receives input as described above, and the output is a data.frame with the following columns:

fdr_method: Multiple testing method which was used
fdr_pars: Custom parameters of the multiple testing method
alpha: Nominal significance level at which the benchmark was run
FDR: False Discovery Rate of benchmarked method on simulated dataset
power: Power of benchmarked method on simulated dataset
rj_ratio: Average rejections divided by total number of hypotheses
FPR: False positive rate of benchmarked method on simulated dataset
FWER: Familywise Error Rate of benchmarked method on simulated dataset
nsuccessful: Number of successful evaluations of the method
sim_method: Simulation scenario under which benchmark was run
m: Total number of hypotheses
sim_pars: Custom parameters of the simulation scenario

Value

data.frame which summarizes results of numerical experiment

Examples

   nreps <- 3 # monte carlo replicates
   ms <- 5000 # number of hypothesis tests
   eff_sizes <- c(2,3)
   sim_funs <- lapply(eff_sizes,
			function(x) du_ttest_sim_fun(ms,0.95,x, uninformative_filter=FALSE))
	  continuous_methods_list <- list(bh,
                              	  lsl_gbh,
 	                          	  clfdr,
                                   ddhf)
  fdr_methods <- lapply(continuous_methods_list, continuous_wrap)
 eval_table <- run_evals(sim_funs, fdr_methods, nreps, 0.1, BiocParallel=FALSE)

nreps <- 3 # monte carlo replicates
   ms <- 5000 # number of hypothesis tests
   eff_sizes <- c(2,3)
   sim_funs <- lapply(eff_sizes,
			function(x) du_ttest_sim_fun(ms,0.95,x, uninformative_filter=FALSE))
	  continuous_methods_list <- list(bh,
                              	  lsl_gbh,
 	                          	  clfdr,
                                   ddhf)
  fdr_methods <- lapply(continuous_methods_list, continuous_wrap)
 eval_table <- run_evals(sim_funs, fdr_methods, nreps, 0.1, BiocParallel=FALSE)

scott_fdrreg: Wrapper for FDR regression (https://github.com/jgscott/FDRreg)

Description

scott_fdrreg: Wrapper for FDR regression (https://github.com/jgscott/FDRreg)

Usage

scott_fdrreg(unadj_p, filterstat, alpha, df = 3, lambda = 0.01)
scott_fdrreg(unadj_p, filterstat, alpha, df = 3, lambda = 0.01)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

filterstat

Factor to which different hypotheses belong

alpha

Significance level at which to apply method

df

Degrees of freedom for B-slines

lambda

Ridge regularization parameter

Value

FDRreg multiple testing object

References

James G. Scott, Ryan C. Kelly, Matthew A. Smith, Pengcheng Zhou, and Robert E. Kass. "False discovery rate regression: application to neural synchrony detection in primary visual cortex." Journal of the American Statistical Association (2015).

storey_qvalue: Wrapper for Storey's qvalue package

Description

storey_qvalue: Wrapper for Storey's qvalue package

Usage

storey_qvalue(unadj_p, alpha)
storey_qvalue(unadj_p, alpha)

Arguments

unadj_p

Numeric vector of unadjusted p-values.

alpha

Significance level at which to apply method

Value

StoreyQValue multiple testing object

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- storey_qvalue(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     obj <- storey_qvalue(sim_df$pvalue, .1)
     sum(rejected_hypotheses(obj))

stratified_bh: Stratified Benjamini Hochberg

Description

stratified_bh: Stratified Benjamini Hochberg

Usage

stratified_bh(unadj_p, groups, alpha)
stratified_bh(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

Value

SBH multiple testing object

References

Sun, Lei, et al. "Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies." Genetic epidemiology 30.6 (2006): 519-530.

Yoo, Yun J., et al. "Were genome-wide linkage studies a waste of time? Exploiting candidate regions within genome-wide association studies." Genetic epidemiology 34.2 (2010): 107-118.

Examples

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

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

TST (Two-Step) pi0 estimator

Description

TST (Two-Step) pi0 estimator

Usage

tst_pi0_est(pvalue, alpha)
tst_pi0_est(pvalue, alpha)

Arguments

pvalue

Numeric vector of unadjusted p-values.

alpha

Nominal level for applying the TST procedure

Value

estimated proportion of null hypotheses (pi0)

Examples

     sim_df <- du_ttest_sim(20000,0.95, 1.5)
     tst_pi0_est(sim_df$pvalue, .1)

sim_df <- du_ttest_sim(20000,0.95, 1.5)
     tst_pi0_est(sim_df$pvalue, .1)

Normal PRDS simulation: Covariate is effect size under alternative, there are latent factors driving PRDS correlations among hypotheses

Description

Normal PRDS simulation: Covariate is effect size under alternative, there are latent factors driving PRDS correlations among hypotheses

Usage

wasserman_normal_prds_sim(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5,
  seed = NULL
)

wasserman_normal_prds_sim_fun(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5
)
wasserman_normal_prds_sim(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5,
  seed = NULL
)

wasserman_normal_prds_sim_fun(
  m,
  pi0,
  rho = 0,
  latent_factors = 1,
  xi_min = 0,
  xi_max = 2.5
)

Arguments

m

Integer, total number of hypotheses

pi0

Numeric, proportion of null hypotheses

rho

Numeric, correlation between z-scores of hypotheses driven by same latent factor

latent_factors

Integer, number of latent factors driving the correlations

xi_min, xi_max

Numeric, covariates are drawn as uniform on xi_min, xi_max

seed

Integer, Random seed to be used for simulation (default: NULL, i.e. RNG state will be used as is)

Value

A data frame containing all information about the simulation experiment

Functions

wasserman_normal_prds_sim_fun: Creates a closure function for a given seed

Examples

sim_df <- wasserman_normal_prds_sim(20000,0.9, rho=0.1)

sim_df <- wasserman_normal_prds_sim(20000,0.9, rho=0.1)

Normal simulation: Covariate is effect size under alternative

Description

Normal simulation: Covariate is effect size under alternative

Usage

wasserman_normal_sim(m, pi0, xi_min, xi_max, seed = NULL)

wasserman_normal_sim_fun(m, pi0, xi_min, xi_max)
wasserman_normal_sim(m, pi0, xi_min, xi_max, seed = NULL)

wasserman_normal_sim_fun(m, pi0, xi_min, xi_max)

Arguments

m

Integer, total number of hypotheses

pi0

Numeric, proportion of null hypotheses

xi_min, xi_max

Numeric, covariates are drawn as uniform on xi_min, xi_max

seed

Integer, Random seed to be used for simulation (default: NULL, i.e. RNG state will be used as is)

Value

A data frame containing all information about the simulation experiment

Functions

wasserman_normal_sim_fun: Creates a closure function for a given seed

Examples

sim_df <- wasserman_normal_sim(20000,0.9, 1, 5)

sim_df <- wasserman_normal_sim(20000,0.9, 1, 5)

Package 'IHWpaper'

Help Index

analyze_dataset: Basically performs preprocessing and then returns analyzed RNASeq dataset (diff. expression) , i.e. the DESeq2 result whose p-values and baseMean statistics can then be used with IHW

Description

Usage

Arguments

Value

Examples

bh: Wrapper for Benjamini Hochberg

Description

Usage

Arguments

Value

Examples

bonf: Wrapper for Bonferroni

Description

Usage

Arguments

Value

Examples

clfdr: Cai's local fdr based method

Description

Usage

Arguments

Value

References

Examples

Benchmarking wrapper: Given a multiple testing method, convert it so that it takes a simulation object (see simulation function) and a nominal level alpha as inputs

Description

Usage

Arguments

Value

Examples

ddhf: Greedy independent filtering

Description

Usage

Arguments

Value

Examples

t-test simulation: Simulate rowwise t-tests

Description

Usage

Arguments

Value

Functions

Examples

gbh: Grouped Benjamini Hochberg

Description

Usage

Arguments

Value

Functions

References

Examples

IHW wrappers

Description

Usage

Arguments

Details

Value

Functions

Examples

LSL (Least-Slope) pi0 estimator

Description

Usage

Arguments

Value

Examples

Null simulation: Generate uniformly distributed p-values and covariates

Description

Usage

Arguments

Value

Functions

Examples

helper function to create nice legends

Description

Usage

Arguments

Value