DBR {DiscreteFDR} R Documentation

## [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).

### 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)



[Package DiscreteFDR version 1.3.6 Index]