qvalue (version 2.4.2)

# qvalue: Estimate the q-values for a given set of p-values

## Description

Estimate the q-values for a given set of p-values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant.

## Usage

qvalue(p, fdr.level = NULL, pfdr = FALSE, ...)

## Arguments

p
A vector of p-values (only necessary input).
fdr.level
A level at which to control the FDR. Must be in (0,1]. Optional; if this is selected, a vector of TRUE and FALSE is returned that specifies whether each q-value is less than fdr.level or not.
pfdr
An indicator of whether it is desired to make the estimate more robust for small p-values and a direct finite sample estimate of pFDR -- optional.
...
Additional arguments passed to pi0est and lfdr.

## Value

• A list of object type "qvalue" containing:
• callFunction call.
• pi0An estimate of the proportion of null p-values.
• qvaluesA vector of the estimated q-values (the main quantity of interest).
• pvaluesA vector of the original p-values.
• lfdrA vector of the estimated local FDR values.
• significantIf fdr.level is specified, and indicator of whether the q-value fell below fdr.level (taking all such q-values to be significant controls FDR at level fdr.level).
• pi0.lambdaAn estimate of the proportion of null p-values at each $\lambda$ value (see vignette).
• lambdaA vector of the $\lambda$ values utilized to obtain pi0.lambda.

## Details

The function pi0est is called internally and calculates the estimate of $\pi_0$, the proportion of true null hypotheses. The function lfdr is also called internally and calculates the estimated local FDR values. Arguments for these functions can be included via ... and will be utilized in the internal calls made in qvalue. See http://genomine.org/papers/Storey_FDR_2011.pdf for a brief introduction to FDRs and q-values.

## References

Storey JD. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64: 479-498. http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00346/abstract Storey JD and Tibshirani R. (2003) Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences, 100: 9440-9445. http://www.pnas.org/content/100/16/9440.full

Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035. http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&id=pdf_1&handle=euclid.aos/1074290335

Storey JD, Taylor JE, and Siegmund D. (2004) Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B, 66: 187-205. http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2004.00439.x/abstract

Storey JD. (2011) False discovery rates. In International Encyclopedia of Statistical Science. http://genomine.org/papers/Storey_FDR_2011.pdf http://www.springer.com/statistics/book/978-3-642-04897-5

pi0est, lfdr, summary.qvalue, plot.qvalue, hist.qvalue, write.qvalue

## Examples

Run this code
# import data
data(hedenfalk)
p <- hedenfalk\$p

# get q-value object
qobj <- qvalue(p)
plot(qobj)
hist(qobj)

# options available
qobj <- qvalue(p, lambda=0.5, pfdr=TRUE)
qobj <- qvalue(p, fdr.level=0.05, pi0.method="bootstrap", adj=1.2)

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