DBR: [HBR-lambda] procedure

In DiscreteFDR: Multiple Testing Procedures with Adaptation for Discrete Tests

Description

Apply the [HBR-λ] procedure, with or without computing the critical constants, to a set of p-values and their discrete support.

Usage

DBR(
  raw.pvalues,
  pCDFlist,
  alpha = 0.05,
  lambda = NULL,
  ret.crit.consts = FALSE
)

Arguments

raw.pvalues	vector of the raw observed p-values, as provided by the end user and before matching with their nearest neighbor in the CDFs supports.
pCDFlist	a list of the supports of the CDFs of the p-values. Each support is represented by a vector that must be in increasing order.
alpha	the target FDR level, a number strictly between 0 and 1. For *.fast kernels, it is only necessary, if stepUp = TRUE.
lambda	a number strictly between 0 and 1. If lambda=NULL (by default), then lambda is chosen equal to alpha.
ret.crit.consts	a boolean. If TRUE, critical constants are computed and returned (this is computationally intensive).

Details

[DBR-lambda] is the discrete version of the [Blanchard-Roquain-lambda] procedure (see References), the authors of the latter suggest to take lambda = alpha (see their Proposition 17), which explains the choice of the default value here.

This version: 2019-06-18.

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
Critical.constants	Critical constants (if requested)
Method	Character string describing the used algorithm, e.g. 'Discrete Benjamini-Hochberg procedure (step-up)'
Signif.level	Significance level alpha
Lambda	Value of lambda.
Data$raw.pvalues	The values of raw.pvalues
Data$pCDFlist	The values of pCDFlist
Data$data.name	The respective variable names of raw.pvalues and pCDFlist

References

G. Blanchard and E. Roquain (2009). Adaptive false discovery rate control under independence and dependence. Journal of Machine Learning Research, 10, 2837-2871.

See Also

discrete.BH, DiscreteFDR

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 support
df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc")
raw.pvalues <- df.formatted$raw
pCDFlist <- df.formatted$support

DBR.fast <- DBR(raw.pvalues, pCDFlist)
summary(DBR.fast)
DBR.crit <- DBR(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(DBR.crit)

DiscreteFDR documentation built on Sept. 5, 2021, 5:23 p.m.