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Confidence bounds on the true/false positives among most significant items

Source: R/confidenceEnvelope.R
confCurveFromFam.Rd

Confidence bounds on the true/false positives among most significant items

Usage

confCurveFromFam(
  p.values,
  refFamily,
  param,
  K = length(p.values),
  what = c("TP", "FDP")
)

Arguments

p.values

A vector containing the p-values for all m hypotheses, sorted increasingly

refFamily

A character value, the reference family to be used. Should be either "Simes" (or equivalenlty, "Linear"), "Beta", or "Oracle".

param

A numeric value or vector of parameters for the reference family.

K

For JER control over 1:K, ie joint control of all k-FWER, k <= K. Defaults to m.

what

A character vector, the names of the post hoc bounds to be computed, among:

  • FP: Upper bound on the number of false positives in the 'x' most significant items

  • TP: Lower bound on the number of true positives in the 'x' most significant items

  • FDP: Upper bound on the proportion of false positives in the 'x' most significant items

  • TP: Lower bound on the proportion of true positives in the 'x' most significant items.

Defaults to c("TP", "FDP")

Value

A data.frame with m rows and 5 columns:

  • x: Number of most significant items selected

  • family: Matches input argument refFamily

  • param: Matches argument param

  • procedure: Label for the procedure, typically of the form 'refFamily(param)'

  • bound: Value of the post hoc bound

  • stat: Type of post hoc bound, as specified by argument bound

Details

param should be a numeric value unless refFamily == "Oracle". In the latter case, `param`` should be a boolean vector of length m indicating whether each null hypothesis is true or false.

Author

Gilles Blanchard, Pierre Neuvial and Etienne Roquain

Examples


# Generate Gaussian data and perform multiple tests
sim <- gaussianSamples(m = 502, rho = 0.5, n = 100, pi0 = 0.8, SNR = 3, prob = 0.5)
rwt <- rowWelchTests(sim$X, sim$categ, alternative = "greater")

# calculate, print, and plot confidence bound
cb <- confCurveFromFam(rwt$p.value, refFamily = "Simes", param = 0.1)
head(cb)
#>   x      label stat bound
#> 1 1 Simes(0.1)   TP     1
#> 2 2 Simes(0.1)   TP     2
#> 3 3 Simes(0.1)   TP     3
#> 4 4 Simes(0.1)   TP     4
#> 5 5 Simes(0.1)   TP     5
#> 6 6 Simes(0.1)   TP     6
plotConfCurve(cb, xmax = 200)