p.adjust
Adjust P-values for Multiple Comparisons

Description

Adjusts the P-values for multiple comparisons.

Usage

p.adjust(p, method = p.adjust.methods, n = length(p))
p.adjust.methods

Arguments

p a numeric vector of probabilities.
method a character string. Must be one of the p.adjust.methods:
  • "holm"
  • "hochberg"
  • "hommel"
  • "bonferroni"
  • "BH"
  • "BY"
  • "fdr"
  • "none"
"holm" is the default method.
n the number of hypothesis tests performed. Must not be less than the length of p.

Details

The adjust methods include the Bonferroni correction "bonferroni", and the less conservative corrections, such as "holm", "hochberg", "hommel", "BH" (or its alias "fdr"), and "BY". A pass-through option ("none") is also included. For more information, see the books listed under References.
Value
returns a vector of adjusted P-values corresponding to the given method, the same length as p.
Note
The "hommel" method is not yet implemented. The "hochberg" method is used in its place.
References
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289--300.
Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165--1188.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65--70.
Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75, 383--386.
Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75, 800--803.
Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561--576. (An excellent review of the area.)
Sarkar, S. (1998). Some probability inequalities for ordered MTP2 random variables: a proof of Simes conjecture. Annals of Statistics, 26, 494--504.
Sarkar, S., and Chang, C. K. (1997). Simes' method for multiple hypothesis testing with positively dependent test statistics. Journal of the American Statistical Association, 92, 1601--1608.
Wright, S. P. (1992). Adjusted P-values for simultaneous inference. Biometrics, 48, 1005--1013. (Explains the adjusted P-value approach.)
See Also
pmax, cummax, order.
Examples
# naively search for a single-factor explanation for
# state differences in murder rates
stateData <- data.frame(check.names=FALSE, Sdatasets::state.x91)
f <- vapply(setdiff(colnames(stateData), "Murder"), function(nm) {
         nm <- as.name(nm)
         summary(lm(bquote(Murder ~ .(nm)), data=stateData))$fstatistic
     }, FUN.VALUE=numeric(3))
rawPValue <- pf(f["value",], f["numdf",], f["dendf",], lower.tail=FALSE)
# account for looking through a list of possible explanations
rbind(rawPValue, adjustedPValue = p.adjust(rawPValue, method="BY"))
Package stats version 4.0.0-28
Package Index