From: Gordon Smyth <smyth_at_wehi.edu.au>

Date: Sun 16 Jan 2005 - 19:55:35 EST

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R-devel@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-devel Received on Sun Jan 16 19:00:49 2005

Date: Sun 16 Jan 2005 - 19:55:35 EST

I append below a suggested update for p.adjust().

- A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188.
- I've re-named the "fdr" method to "bh" but kept "fdr" as a synonym for backward compatability.
- Upper case values for method "BH" or "YH" are also accepted.
- p.adust() now preserves attributes like names for named vectors (as does cumsum and friends for example).
- p.adjust() now works columnwise on numeric data.frames (as does cumsum and friends).
- method="hommel" now works correctly even for n=2
- The 'n' argument is removed. Setting this argument for any methods other than "none" or "bonferroni" make the p-values indeterminate, and the argument seems to be seldom used. (It isn't used in the R default distribution.) I think trying to combine this argument with NAs would get you into lots of hot water. For example, what does p.adjust(c(NA,NA,0.05),n=2) mean? Which 2 values should be adjusted?
- NAs are treated in na.exclude style. This is the correct approach for most applications. The only other consistent thing you could do would be to treat the NAs as if they all had value=1. But then you would have to explain clearly that the values being returned are not actually the correct adjusted p-values, which are unknown, but are the most conservative possible values assuming the worst-case for the missing values. This would become arbitrarily unreasonable as the number of NAs increases.

Gordon

p.adjust.methods <-

c("holm", "hochberg", "hommel", "bonferroni", "yh", "bh", "fdr", "none")

p.adjust <- function(p, method = p.adjust.methods) {

method <- match.arg(tolower(method),p.adjust.methods) if(method=="fdr") method <- "bh" if(is.data.frame(p)) { if(length(p)) for(i in 1:length(p)) p[[i]] <- Recall(p[[i]],method=method) return(p) } porig <- p notna <- !is.na(p) p <- as.vector(p[notna]) n <- length(p) if (n == 1) return(porig) if (n == 2 && method=="hommel") method <- "hochberg" porig[notna] <- switch(method, holm = { i <- 1:n o <- order(p) ro <- order(o) pmin(1, cummax( (n - i + 1) * p[o] ))[ro]

},

hochberg = { i <- n:1 o <- order(p, decreasing = TRUE) ro <- order(o) pmin(1, cummin( (n - i + 1) * p[o] ))[ro]

},

hommel = { i <- 1:n s <- sort(p, index = TRUE) p <- s$x ro <- order(s$ix) q <- pa <- rep.int( min(n*p/(1:n)), n) for (j in (n-1):2) { q1 <- min(j*p[(n-j+2):n]/(2:j)) q[1:(n-j+1)] <- pmin( j*p[1:(n-j+1)], q1) q[(n-j+2):n] <- q[n-j+1] pa <- pmax(pa,q) } pmax(pa,p)[ro]

},

bh = { i <- n:1 o <- order(p, decreasing = TRUE) ro <- order(o) pmin(1, cummin( n / i * p[o] ))[ro]

},

yh = { i <- n:1 o <- order(p, decreasing = TRUE) ro <- order(o) q <- sum(1/(1:n)) pmin(1, cummin(q * n/i * p[o]))[ro]

},

bonferroni = pmin(n * p, 1), none = p) porig

}

R-devel@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-devel Received on Sun Jan 16 19:00:49 2005

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