Graphical display of the q-value object
A q-value object.
Range of q-values to show. Optional
Additional arguments. Currently unused.
The function plot allows one to view several plots:
The estimated pi_0 versus the tuning parameter lambda.
The q-values versus the p-values.
The number of significant tests versus each q-value cutoff.
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).
Nothing of interest.
John D. Storey, Andrew J. Bass
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,
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.
Storey JD. (2011) False discovery rates. In International Encyclopedia of Statistical Science.
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