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Fit SansSouci object

Source: R/SansSouci-class.R
fit.SansSouci.Rd

Fit SansSouci object

Usage

# S3 method for SansSouci
fit(
  object,
  alpha,
  B = 1000,
  rowTestFUN = NULL,
  alternative = c("two.sided", "less", "greater"),
  family = c("Simes", "Linear", "Beta", "Oracle"),
  max_steps_down = 10L,
  K = nHyp(object),
  force = FALSE,
  verbose = FALSE,
  ...
)

Arguments

object

An object of class SansSouci

alpha

A numeric value in [0,1], the target (JER) risk

B

An integer value, the number of permutations to be performed. Defaults to 1000

rowTestFUN

A (vectorized) test function. Defaults to rowWelchTests.

alternative

A character string specifying the alternative hypothesis. Must be one of "two.sided" (default), "greater" or "less".

family

A character value, the name of a threshold family. Should be one of "Linear", "Beta" and "Simes", or "Oracle". "Linear" and "Simes" families are identical.

  • Simes/Linear: The classical family of thresholds introduced by Simes (1986). This family yields JER control if the test statistics are positively dependent (PRDS) under H0.

  • Beta: A family of thresholds that achieves marginal kFWER control under independence

  • Oracle A family such that the associated bounds correspond to the true numbers/proportions of true/false positives. "truth" must be available in object$input$truth.

max_steps_down

A numeric value, the maximum number of steps down to perform. Defaults to 10 (but the algorithm generally converges in 1 or 2 steps).

K

An integer value in [1,m], the number of elements in the reference family. Defaults to m

force

A boolean value: should the permutation p-values and pivotal statistics be re-calculated ? Defaults to FALSE

verbose

A boolean value: should extra info be printed? Defaults to FALSE

...

Not used

Value

A 'fitted' object of class 'SansSouci'. It is a list of three elements

  • input: see SansSouci

  • param: the input parameters, given as a list

  • output: the outputs of the calibration, see calibrate

Examples

# Generate Gaussian data and perform multiple tests
obj <- SansSouciSim(m = 502, rho = 0.5, n = 100, pi0 = 0.8, SNR = 3, prob = 0.5)
res <- fit(obj, B = 100, alpha = 0.1)

# confidence curve
plot(res)


# confidence curve for a subset
S <- which(pValues(res) < 0.1 & foldChanges(res) > 0.3)
plot(res, S = S)


# plot two confidence curves
res_beta <- fit(res, B = 100, alpha = 0.1, family = "Beta", K = 20)

resList <- list("Linear" = res, "Beta" = res_beta)
bounds <- lapply(resList, predict, all = TRUE)
plotConfCurve(bounds, xmax = 200)