plot.qvalue {qvalue} | R Documentation

## Plotting function for q-value object

### Description

Graphical display of the q-value object

### Usage

    ## S3 method for class 'qvalue'
    plot(x, rng = c(0, 0.1), ...)

### Arguments

`x` | A q-value object.
`rng` | Range of q-values to show. Optional
`...` | Additional arguments. Currently unused.

### Details

The function plot 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 four 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 called significant and 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, Andrew J. Bass

### 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.

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

### See Also

`qvalue`, `write.qvalue`, `summary.qvalue`

### Examples

    # import data
    data(hedenfalk)
    p <- hedenfalk$p
    qobj <- qvalue(p)

    plot(qobj, rng=c(0.0, 0.3))

[Package qvalue version 2.16.0 Index]