## S3 method for class 'qvalue':
plot(x, rng = c(0, 0.1), ...)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).
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.
Storey JD. (2011) False discovery rates. In International Encyclopedia of Statistical Science.
qvalue, write.qvalue, summary.qvalue# import data
data(hedenfalk)
p <- hedenfalk$p
qobj <- qvalue(p)
plot(qobj, rng=c(0.0, 0.3))Run the code above in your browser using DataCamp Workspace