DBH: Wrapper Functions for the Discrete Benjamini-Hochberg...

In DISOhda/DiscreteFDR: FDR Based Multiple Testing Procedures with Adaptation for Discrete Tests

Wrapper Functions for the Discrete Benjamini-Hochberg Procedure

Description

DBH() is a wrapper function of discrete.BH() for computing [HSU] and [HSD]. It simply passes its arguments to discrete.BH() with fixed adaptive = FALSE.

Usage

DBH(test.results, ...)

## Default S3 method:
DBH(
  test.results,
  pCDFlist,
  alpha = 0.05,
  direction = "su",
  ret.crit.consts = FALSE,
  select.threshold = 1,
  pCDFlist.indices = NULL,
  ...
)

## S3 method for class 'DiscreteTestResults'
DBH(
  test.results,
  alpha = 0.05,
  direction = "su",
  ret.crit.consts = FALSE,
  select.threshold = 1,
  ...
)

Arguments

test.results	either a numeric vector with p-values or an R6 object of class DiscreteTestResults from package DiscreteTests for which a discrete FDR procedure is to be performed.
...	further arguments to be passed to or from other methods. They are ignored here.
pCDFlist	list of the supports of the CDFs of the p-values; each list item must be a numeric vector, which is sorted in increasing order and whose last element equals 1.
alpha	single real number strictly between 0 and 1 indicating the target FDR level.
direction	single character string specifying whether to perform a step-up ("su"; the default) or step-down procedure ("sd").
ret.crit.consts	single boolean specifying whether critical constants are to be computed.
select.threshold	single real number strictly between 0 and 1 indicating the largest raw p-value to be considered, i.e. only p-values below this threshold are considered and the procedures are adjusted in order to take this selection effect into account; if threshold = 1 (the default), all raw p-values are selected.
pCDFlist.indices	list of numeric vectors containing the test indices that indicate to which raw p-value each unique support in pCDFlist belongs; ignored if the lengths of test.results and pCDFlist are equal.

Details

Computing critical constants (ret.crit.consts = TRUE) requires considerably more execution time, especially if the number of unique supports is large. We recommend that users should only have them calculated when they need them, e.g. for illustrating the rejection set in a plot or other theoretical reasons.

Value

A DiscreteFDR S3 class object whose elements are:

Rejected	rejected raw p-values.
Indices	indices of rejected hypotheses.
Num.rejected	number of rejections.
Adjusted	adjusted p-values (only for step-down direction).
Critical.constants	critical values (only exists if computations where performed with ret.crit.consts = TRUE).
Data	list with input data.
Data$Method	character string describing the used algorithm, e.g. 'Discrete Benjamini-Hochberg procedure (step-up)'
Data$Raw.pvalues	observed p-values.
Data$pCDFlist	list of the p-value supports.
Data$FDR.level	FDR level alpha.
Data$Data.name	the respective variable names of the input data.
Select	list with data related to p-value selection; only exists if select.threshold < 1.
Select$Threshold	p-value selection threshold (select.threshold).
Select$Effective.Thresholds	results of each p-value CDF evaluated at the selection threshold.
Select$Pvalues	selected p-values that are \leq selection threshold.
Select$Indices	indices of p-values \leq selection threshold.
Select$Scaled	scaled selected p-values.
Select$Number	number of selected p-values \leq selection threshold.

References

Döhler, S., Durand, G., & Roquain, E. (2018). New FDR bounds for discrete and heterogeneous tests. Electronic Journal of Statistics, 12(1), pp. 1867-1900. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/18-EJS1441")}

See Also

discrete.BH(), ADBH(), DBR(), DBY()

Examples

X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1)
X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2)
N1 <- rep(148, 9)
N2 <- rep(132, 9)
Y1 <- N1 - X1
Y2 <- N2 - X2
df <- data.frame(X1, Y1, X2, Y2)
df

# Compute p-values and their supports of Fisher's exact test
test.result <- generate.pvalues(df, "fisher")
raw.pvalues <- test.result$get_pvalues()
pCDFlist <- test.result$get_pvalue_supports()

# DBH (step-up) without critical values; using test results object
DBH.su.fast <- DBH(test.result)
summary(DBH.su.fast)

# DBH (step-down) without critical values; using extracted p-values 
# and supports
DBH.sd.fast <- DBH(raw.pvalues, pCDFlist, direction = "sd")
summary(DBH.sd.fast)

# DBH (step-up) with critical values; using extracted p-values and supports
DBH.su.crit <- DBH(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(DBH.su.crit)

# DBH (step-down) with critical values; using test results object
DBH.sd.crit <- DBH(test.result, direction = "sd", ret.crit.consts = TRUE)
summary(DBH.sd.crit)

DISOhda/DiscreteFDR documentation built on April 14, 2025, 8:35 a.m.