DiscreteFDR (version 1.3-4)

fast.Discrete: 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",
  adaptive = FALSE
)

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.

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

Details

This version: 2019-06-18.

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

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)
summary(ADBH.su)

ADBH.sd <- fast.Discrete(counts = df, input = "noassoc", direction = "sd", adaptive = TRUE)
ADBH.sd$Adjusted
summary(ADBH.sd)

# }