# padjustw: Adjust P-values for Multiple Comparisons In someMTP: Some Multiple Testing Procedures

## Description

Given a set of p-values, returns p-values adjusted using one of several (weighted) methods. It extends the method of `p.adjust{stats}`

## Usage

 `1` ```p.adjust.w(p, method = c("bonferroni","holm","BHfwe","BH","BY"), n = length(p),w=NULL) ```

## Arguments

 `p` vector of p-values (possibly with NAs) `method` correction method `n` number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing! `w` weigths to be used. `p.adjust.w(..., rep(1,length(p)))` produces the same results as in `p.adjust(...)` (i.e. the unweighted counterpart).

## Value

A vector of corrected p-values (same length as p) having two attributes: `attributes(...)\$w` is the vecotr of used weights and `attributes(...)\$method` is the method used.

Livio Finos

## References

Benjamini, Hochberg (1997). Multiple hypotheses testing with weights. Scand. J. Statist. 24, 407-418.

Finos, Salmaso (2007). FDR- and FWE-controlling methods using data-driven weights. Journal of Statistical Planning and Inference, 137,12, 3859-3870.

## See Also

`p.adjust`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```set.seed(13) y <- matrix(rnorm(5000),5,1000) #create toy data y[,1:100] <- y[,1:100]+3 #create toy data p <- apply(y,2,function(y) t.test(y)\$p.value) #compute p-values M2 <- apply(y^2,2,mean) #compute ordering criterion fdr <- p.adjust(p,method="BH") #(unweighted) procedure, fdr control sum(fdr<.05) fdr.w <- p.adjust.w(p,method="BH",w=M2) #weighted procedure, weighted fdr control sum(fdr.w<.05) fwer <- p.adjust(p,method="holm") #(unweighted) procedure, fwer control sum(fwer<.05) fwer.w <- p.adjust.w(p,method="BHfwe",w=M2) #weighted procedure, weighted fwer (=fwer) control sum(fwer.w<.05) plot(M2,-log10(p)) ```

someMTP documentation built on May 29, 2017, 3:47 p.m.