fit.SansSouci: Fit SansSouci object

In pneuvial/sanssouci: Post Hoc Multiple Testing Inference

Fit SansSouci object

Description

Fit SansSouci object

Usage

## S3 method for class '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)

pneuvial/sanssouci documentation built on Feb. 12, 2024, 4:18 a.m.