Function reference • sanssouci

All functions

nHyp(<SansSouci>) nObs(<SansSouci>) label(<SansSouci>) print(<SansSouci>) pValues(<SansSouci>) foldChanges(<SansSouci>) thresholds(<SansSouci>): Basic methods for class SansSouci

SansSouci(): Create an object of class 'SansSouci'

SansSouciDyadic(): Create an object of class 'SansSouciStruct' with a dyadic structure

SansSouciSim(): Create an object of class 'SansSouci' from simulations

nHyp(<SansSouciStruct>) nLeaves(<SansSouciStruct>) label(<SansSouciStruct>) pValues(<SansSouciStruct>): Basic methods for class SansSouciStruct

SansSouciStruct(): Create an object of class 'SansSouciStruct'

V.star(): Post hoc bound on the number of false positives

nHyp() nObs() label() pValues() foldChanges() thresholds() nLeaves(): Generic functions for S3 class SansSouci

calibrate() calibrate0(): Perform JER calibration from randomization p-values

confCurveFromFam(): Confidence bounds on the true/false positives among most significant items

curve.V.star.forest.naive() curve.V.star.forest.fast(): Compute a curve of post hoc bounds based on a reference family with forest structure

curveMaxFP(): Upper bound for the number of false discoveries among most significant items

curveMaxFP_BNR2014() curveMaxFP_Mein2006(): Upper bound for the number of false discoveries among most significant items

delete.gaps(): Delete the gaps induced by pruning

dyadic.from.leaf_list() dyadic.from.window.size() dyadic.from.height(): Create a complete dyadic tree structure

fit(<SansSouci>): Fit SansSouci object

fit(<SansSouciStruct>): Fit SansSouciStruct object

forest.completion(): Complete a forest structure

formatBounds(): Table of bounds

gauss_bloc(): Generate a block of deterministic signal of Gaussian shape

gaussianSamples(): Simulate equi-correlated data

gaussianTestStatistics(): Simulate Gaussian test statistics

gen.mu.leaves(): Generate the signal in the leaves of the tree

gen.mu.noleaves(): Generate an unstructured signal

gen.p.values(): Generate one-sided p-values associated to a given signal with equi-correlated noise

get_perm(): Get permutation statistics and p-values

get_pivotal_stat(): Get a vector of pivotal statistics associated to permutation p-values and to a reference family

maxFDP(): Upper bound for the false discovery proportion in a selection

maxFP(): Upper bound for the number of false discoveries in a selection

minTDP(): Lower bound for the true discovery proportion in a selection

minTP(): Lower bound for the number of true discoveries in a selection

nb.elements(): Number of unique regions in a reference family with forest structure

plot(<SansSouci>): Plot confidence bound on the true/false positives among most significant items

plotConfCurve(): Plot confidence bound

posthocBySimes0Rcpp() posthocBySimes(): post hoc bound obtained from Simes' inequality

predict(<SansSouci>): Post hoc confidence bounds on the true/false positives

pruning(): Prune a forest structure to speed up computations

t_inv_linear() t_linear() t_inv_beta() t_beta(): Reference families

rowBinomialTests(): Binomial proportion tests for each row of a matrix

rowPearsonCorrelationTests(): Pearson's correlation test for rows of a matrix

rowWelchTests(): Welch T-tests for rows of a matrix

rowWilcoxonTests(): Wilcoxon rank sum tests for each row of a matrix

rowZTests(): Z tests for rows of a matrix

testByRandomization(): Randomization-based testing

volcanoPlot(): Volcano plot

volcanoPlot(<numeric>): Volcano plot

zeta.HB() zeta.trivial() zeta.DKWM(): Estimate the number of true null hypotheses among a set of p-values

zetas.tree(): Estimate of the proportion of true nulls in each node of a tree