DLR: Wrapper Functions for the Discrete Guo-Romano Procedure

In FDX: False Discovery Exceedance Controlling Multiple Testing Procedures

Wrapper Functions for the Discrete Guo-Romano Procedure

Description

DLR() is a wrapper function of discrete.LR() for computing [DLR]. It simply passes its arguments to discrete.LR() with fixed adaptive = TRUE.

Usage

DLR(test.results, ...)

## Default S3 method:
DLR(
  test.results,
  pCDFlist,
  alpha = 0.05,
  zeta = 0.5,
  direction = "sd",
  critical.values = FALSE,
  select.threshold = 1,
  pCDFlist.indices = NULL,
  ...
)

## S3 method for class 'DiscreteTestResults'
DLR(
  test.results,
  alpha = 0.05,
  zeta = 0.5,
  direction = "sd",
  critical.values = 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 specifying the target FDP.
zeta	single real number strictly between 0 and 1 specifying the target probability of not exceeding the desired FDP. If zeta = NULL (the default), then zeta is chosen equal to alpha.
direction	single character string specifying whether to perform the step-up (⁠"su⁠) or step-down ("sd"; the default) version of the Lehmann-Romano procedure.
critical.values	single boolean indicating 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 (critical.values = 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 area in a plot or other theoretical reasons.

Value

A FDX S3 class object whose elements are:

Rejected	rejected raw p-values.
Indices	indices of rejected p-values.
Num.rejected	number of rejections.
Adjusted	adjusted p-values (only for step-down direction).
Critical.values	critical values (only exists if computations where performed with critical.values = TRUE).
Select	list with data related to p-value selection; only exists if select.threshold < 1.
Select$Threshold	p-value selection threshold.
Select$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.
Data	list with input data.
Data$Method	character string describing the used algorithm, e.g. 'Discrete Lehmann-Romano procedure (step-up)'.
Data$Raw.pvalues	all observed raw p-values.
Data$pCDFlist	list of the p-value supports.
Data$FDP.threshold	FDP threshold alpha.
Data$Exceedance.probability	probability zeta of FDP exceeding alpha; thus, FDP is being controlled at level alpha with confidence 1 - zeta.
Data$Adaptive	boolean indicating whether an adaptive procedure was conducted or not.
Data$Data.name	the respective variable name(s) of the input data.

References

Döhler, S. & Roquain, E. (2020). Controlling False Discovery Exceedance for Heterogeneous Tests. Electronic Journal of Statistics, 14(2), pp. 4244-4272. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/20-EJS1771")}

See Also

discrete.LR(), NDLR(), discrete.GR(), DGR(), NDGR(), discrete.PB(), DPB(), NDPB()

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

# Construction of the p-values and their supports with Fisher's exact test
library(DiscreteTests)  # for Fisher's exact test
test.results <- fisher_test_pv(df)
raw.pvalues <- test.results$get_pvalues()
pCDFlist <- test.results$get_pvalue_supports()

# DLR without critical values; using results object
DLR.sd.fast <- DLR(test.results)
summary(DLR.sd.fast)

# DLR with critical values; using extracted p-values and supports
DLR.sd.crit <- DLR(raw.pvalues, pCDFlist, critical.values = TRUE)
summary(DLR.sd.crit)

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

# DLR (step-up) with critical values; using test results object
DLR.su.crit <- DLR(test.results, direction = "su", critical.values = TRUE)
summary(DLR.su.crit)

FDX documentation built on April 4, 2025, 4:08 a.m.