qplot {qvalue}R Documentation

Graphical display of qvalue objects

Description

Graphical display of qvalue objects

Usage

qplot(qobj, rng = c(0, 0.1), smooth.df = 3, smooth.log.pi0 = FALSE, ...)
## S3 method for class 'qvalue'
plot(x, ...)

Arguments

qobj, x

Qvalue object.

rng

Range of q-values to consider. 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.

...

Any other arguments.

Details

The function qplot allows one to view several plots:

  1. The estimated pi_0 versus the tuning parameter lambda.

  2. The q-values versus the p-values

  3. The number of significant tests versus each q-value cutoff

  4. The number of expected false positives versus the number of significant tests

This function makes fours plots. The first is a plot of the estimate of pi_0 versus its tuning parameter lambda. In most cases, as lambda gets larger, the bias of the estimate decreases, yet the variance increases. Various methods exist for balancing this bias-variance trade-off (Storey 2002, Storey & Tibshirani 2003, Storey, Taylor & Siegmund 2004). Comparing your estimate of pi_0 to this plot allows one to guage its quality. The remaining three plots show how many tests are significant, as well as how many false positives to expect for each q-value cut-off. A thorough discussion of these plots can be found in Storey & Tibshirani (2003).

Value

Nothing of interest.

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

qvalue, qwrite, qsummary, qvalue.gui

Examples

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

# view plots for q-values between 0 and 0.3:
plot(qobj, rng=c(0.0, 0.3))

## End(Not run)
[Package qvalue version 1.18.0 Index]