fast.Discrete {DiscreteFDR} R Documentation

## Fast application of discrete procedures

### Description

Apply the [HSU], [HSD], [AHSU] or [AHSD] procedure, without computing the critical constants, to a data set of 2 x 2 contingency tables using Fisher's exact tests.

### Usage

fast.Discrete(
counts,
alternative = "greater",
input = "noassoc",
alpha = 0.05,
direction = "su",
)


### Arguments

 counts a data frame of 2 or 4 columns and any number of lines, each line representing a 2 x 2 contingency table to test. The number of columns and what they must contain depend on the value of the input argument, see Details of fisher.pvalues.support. alternative same argument as in fisher.test. The three possible values are "greater" (default), "two.sided" or "less" and you can specify just the initial letter. input the format of the input data frame, see Details of fisher.pvalues.support. The three possible values are "noassoc" (default), "marginal" or "HG2011" and you can specify just the initial letter. alpha the target FDR level, a number strictly between 0 and 1. For *.fast kernels, it is only necessary, if stepUp = TRUE. direction a character string specifying whether to conduct a step-up (direction="su", by default) or step-down procedure (direction="sd"). adaptive a boolean specifying whether to conduct an adaptive procedure or not.

### Details

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 (only for step-down direction). Method Character string describing the used algorithm, e.g. 'Discrete Benjamini-Hochberg procedure (step-up)' Signif.level Significance level alpha Data$raw.pvalues The values of raw.pvalues Data$pCDFlist The values of pCDFlist Data$data.name The variable name of the counts dataset ### Examples  X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) Y1 <- N1 - X1 Y2 <- N2 - X2 df <- data.frame(X1, Y1, X2, Y2) df DBH.su <- fast.Discrete(counts = df, input = "noassoc", direction = "su") summary(DBH.su) DBH.sd <- fast.Discrete(counts = df, input = "noassoc", direction = "sd") DBH.sd$Adjusted
summary(DBH.sd)

ADBH.su <- fast.Discrete(counts = df, input = "noassoc", direction = "su", adaptive = TRUE)