mutoss (version 0.1-4)

adaptiveBH: Benjamini-Hochberg (2000) adaptive linear step-up procedure

Description

The adaptive Benjamini-Hochberg step-up procedure is applied to pValues. It controls the FDR at level alpha for independent or positive regression dependent test statistics.

Usage

adaptiveBH(pValues, alpha, silent=FALSE)

Arguments

pValues

The used raw pValues.

alpha

The level at which the FDR shall be controlled.

silent

If true any output on the console will be suppressed.

Value

• A list containing:
• adjPValues
  A numeric vector containing the adjusted pValues
• criticalValues
  A numeric vector containing critical values used in the step-up-down test
• rejected
  A logical vector indicating which hypotheses are rejected
• pi0
  An estimate of the proportion of true null hypotheses among all hypotheses (pi0=m0/m).
• errorControl
  A Mutoss S4 class of type errorControl, containing the type of error controlled by the function and the level alpha.

Details

In the adaptive Benjamini-Hochberg step-up procedure the number of true null hypotheses is estimated first as in Hochberg and Benjamini (1990), and this estimate is used in the procedure of Benjamini and Hochberg (1995) with alpha'=alpha*m/m0.

References

Benjamini, Y. and Hochberg, Y. (2000). On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. Journal of Educational and Behavioral Statistics, 25(1): 60-83.$n$

Hochberg, Y. and Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine 9, 811-818.$n$

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to mulitple testing. Journal of the Royal Statistical Society, Series B, 57:289-300.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- adaptiveBH(p, alpha)
result <- adaptiveBH(p, alpha, silent=TRUE)