qplot {qvalue} | R Documentation |

Graphical display of qvalue objects

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

`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 texttt{pi0.method} = "smoother", pi_0 will be
estimated by applying a smoother to a scatterplot of textit{log} pi_0 estimates
against the tuning parameter lambda. Optional. |

`...` |
Any other arguments. |

The function qplot 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 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).

Nothing of interest.

John D. Storey jstorey@u.washington.edu

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

`qvalue`

, `qwrite`

, `qsummary`

, `qvalue.gui`

## 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 1.1 Index]