fast.Discrete {DiscreteFDR}R Documentation

Fast application of discrete procedures

Description

[Deprecated]

Apply the [HSU], [HSD], [AHSU] or [AHSD] procedure, without computing the critical constants, to a data set of 2x2 contingency tables using Fisher's exact tests.

Note: In future versions, this function will be removed and replaced by a more flexible one, which will not be limited to Fisher's exact test.

Usage

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

Arguments

counts

a data frame of two or four columns and any number of lines; each line representing a 2x2 contingency table to test. The number of columns and what they must contain depend on the value of the input argument (see Details section of fisher.pvalues.support).

alternative

same argument as in fisher.test. The three possible values are "greater" (default), "two.sided" or "less" (may be abbreviated).

input

the format of the input data frame (see Details section of fisher.pvalues.support. The three possible values are "noassoc" (default), "marginal" or "HG2011" (may be abbreviated).

alpha

the target FDR level, a number strictly between 0 and 1.

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

See Also

fisher.pvalues.support, discrete.BH

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)
summary(ADBH.su)

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


[Package DiscreteFDR version 1.3.7 Index]