DiscreteFDR (version 1.3-4)

DBR: [HBR-\(\lambda\)] procedure

Description

Apply the [HBR-\(\lambda\)] 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).

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

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.

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

# NOT RUN {
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)

# }