sampleStoppingTimesSaviT: Simulate stopping times for the savi T-test

In safestats: Safe Anytime-Valid Inference

Simulate stopping times for the savi T-test

Description

Simulate stopping times for the savi T-test

Usage

sampleStoppingTimesSaviT(
  deltaTrue,
  alpha = 0.05,
  alternative = c("twoSided", "less", "greater"),
  testType = c("oneSample", "paired", "twoSample"),
  ratio = 1,
  deltaMin = NULL,
  parameter = NULL,
  lowN = 3L,
  nMax = 100000000L,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow", "lai"),
  wantEValuesAtNMax = FALSE,
  nuMin = 2,
  power = NULL,
  wantSamplePaths = TRUE,
  wantSimData = TRUE,
  sigma = 1,
  sigma2 = 1,
  pb = TRUE,
  seed = NULL,
  nSim = 1000L,
  relevanceTest = FALSE,
  relevanceSize = NULL,
  beta = NULL,
  alphaRelevance = NULL,
  ...
)

Arguments

deltaTrue	numeric, data governing effect size used for simulations. Default deltaTrue=deltaMin.
alpha	numeric in (0, 1) that specifies the tolerable type I error and the null rejection rule e >= 1/alpha.
alternative	a character string specifying the alternative hypothesis. Must be one of "twoSided" (default), "greater" or "less".
testType	either one of "oneSample", "paired", "twoSample".
ratio	numeric > 0 representing the randomisation ratio of condition 2 over condition 1. If testType is not equal to "twoSample", or if nPlan is of length(1) then ratio=1.
parameter	numeric, an optional savi test defining parameter. Default set to NULL. and adapts to meanDiffMin and eType, see matchEParameterWith for details.
lowN	integer, smallest sample size (of the first group).
nMax	integer > 0, maximum sample size of the (first) sample in each sample path.
eType	character one of "mom", "grow", "eGauss", and "eCauchy". "mom" is default and uses a non-local moment prior with bump(s) at meanDiffMin, "grow" uses point prior(s) at meanDiffMin, "eGauss" a zero-centred normal prior, "eCauchy" a zero centred Cauchy prior.
wantEValuesAtNMax	logical. If TRUE then compute eValues at nMax. Default FALSE.
wantSamplePaths	logical. If TRUE then output the (stopped) sample paths. Default TRUE.
wantSimData	logical. If TRUE, then output the simulated data.
pb	logical, if TRUE, then show progress bar.
seed	integer, seed number.
nSim	integer > 0, the number of simulations needed to compute power or the number of samples paths for the savi t test under continuous monitoring.
...	further arguments to be passed to or from methods, but mainly to perform do.calls.
deltaMin	numeric that defines the minimal relevant standardised mean difference, the smallest population effect size that we would like to detect (with sufficient power).
nuMin	numeric > 0, the minimum degrees of freedom under which the results are trivial, thus, 1.
power	numeric in (0, 1) that specifies the desired power, that is, the targetted chance to stop in favour of the alternative over the null hypothesis, when the alternative holds true. Note that prior to version 0.8.8 power <- 1-beta. The "beta" argument does not need to be specified anymore.
sigma	numeric > 0 representing the population standard deviation used for the test.
sigma2	numeric > 0 representing the population standard deviation used for the test, for the second group in a two-sample t-test
relevanceTest	logical, if TRUE then impose a rule to stop for minimal efficiency if e <= alphaRelevance. Default FALSE.
relevanceSize	numeric, the minimal clinical relevant mean difference that we do not want to miss under the alternative. Default relevanceSize=NULL implies relevanceSize=abs(meanDiffMin)
beta	numerical in (0,1). Old parameter now replaced by the power parameter
alphaRelevance	numeric, the threshold for relevance test. Taken to be minimum of alpha and 1-power.

Value

a list with stoppingTimes and breakVector. Entries of breakVector are 0, 1. A 1 represents stopping due to exceeding nMax, and 0 due to 1/alpha threshold crossing, which implies that in corresponding stopping time is Inf.

Functions

• sampleStoppingTimesSafeT(): Deprecated version of sampleStoppingTimesSaviT

References

Grünwald, P. D., de Heide, R., & Koolen, W. (2024). Safe testing. Journal of the Royal Statistical Society. Series B (Methodological), 86(5), 1091-1128. (With discussions), https://doi.org/10.1093/jrsssb/qkae011. Ly, A, Boehm, Grünwald, P. D., Ramdas, A., & van Ravenzwaaij, D. (2024). Safe Anytime-Valid Inference: Practical maximally flexible sampling designs for experiments based on e-values. PsyArXiv Preprint, https://doi.org/10.31234/osf.io/h5vae.

Examples

sampleStoppingTimesSaviT(0.7, nSim=10, nMax=20)

safestats documentation built on Sept. 6, 2026, 1:06 a.m.