qvalue {qvalue}R Documentation

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=NULL, lambda=seq(0,0.90,0.05), pi0.method="smoother", fdr.level=NULL, robust=FALSE, gui=FALSE, 
  smooth.df=3, smooth.log.pi0=FALSE)

Arguments

p A vector of p-values (only necessary input)
lambda The value of the tuning parameter to estimate pi_0. Must be in [0,1). Optional, see Storey (2002).
pi0.method Either "smoother" or "bootstrap"; the method for automatically choosing tuning parameter in the estimation of pi_0, the proportion of true null hypotheses
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.
robust 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.
gui A flag to indicate to 'qvalue' that it should communicate with the gui. Should not be specified on command line. Optional.
smooth.df Number of degrees-of-freedom to use when estimating pi_0 with a smoother. Optional.
smooth.log.pi0 If TRUE and pi0.method = "smoother", pi_0 will be estimated by applying a smoother to a scatterplot of log pi_0 estimates against the tuning parameter lambda. Optional.

Details

If no options are selected, then the method used to estimate pi_0 is the smoother method described in Storey and Tibshirani (2003). The bootstrap method is described in Storey, Taylor & Siegmund (2004).

Value

A list containing:

call function call
pi0 an estimate of the proportion of null p-values
qvalues a vector of the estimated q-values (the main quantity of interest)
pvalues a vector of the original p-values
significant if 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)

Author(s)

John D. Storey jstorey@u.washington.edu

References

Storey JD. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64: 479-498.

Storey JD and Tibshirani R. (2003) Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences, 100: 9440-9445.

Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.

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.

QVALUE Manual http://faculty.washington.edu/~jstorey/qvalue/manual.pdf

See Also

qplot, qwrite, qsummary, qvalue.gui

Examples

## Not run: 
p <- scan("pvalues.txt")
qobj <- qvalue(p)
qplot(qobj)
qwrite(qobj, filename="myresults.txt")

qobj <- qvalue(p, lambda=0.5, robust=TRUE)
qobj <- qvalue(p, fdr.level=0.05, pi0.method="bootstrap")
## End(Not run)

[Package qvalue version 1.18.0 Index]