computePowerSaviZ: Helper function: Computes the power based on meanDiffTrue and...

In safestats: Safe Anytime-Valid Inference

Helper function: Computes the power based on meanDiffTrue and nPlan

Description

Helper function: Computes the power based on meanDiffTrue and nPlan

Usage

computePowerSaviZ(
  meanDiffTrue,
  nPlan,
  alpha = 0.05,
  alternative = c("twoSided", "greater", "less"),
  sigma = 1,
  kappa = sigma,
  meanDiffMin = meanDiffTrue,
  testType = c("oneSample", "paired", "twoSample"),
  parameter = NULL,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow"),
  wantSamplePaths = TRUE,
  wantSimData = TRUE,
  relevanceTest = FALSE,
  relevanceSize = NULL,
  alphaRelevance = NULL,
  pb = TRUE,
  seed = NULL,
  nSim = 1000L,
  nBoot = nSim,
  ...
)

Arguments

meanDiffTrue	numeric, data governing mean difference used for simulations. Default meanDiffTrue=meanDiffMin.
nPlan	optional numeric vector of length at most 2, see scenario 2 and 3 above.
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".
sigma	numeric > 0 representing the assumed population standard deviation used to scale the data.
kappa	the true population standard deviation. Default kappa=sigma.
testType	either one of "oneSample", "paired", "twoSample".
parameter	numeric, an optional savi test defining parameter. Default set to NULL. and adapts to meanDiffMin and eType, see matchEParameterWith for details.
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.
wantSamplePaths	logical, if TRUE then also outputs the sample paths.
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 z test under continuous monitoring.
nBoot	integer > 0 representing the number of bootstrap samples to assess the accuracy of the approximations of the power, the number of samples for the savi z test under continuous monitoring,or for the computation of the logarithm of the implied target.
...	further arguments to be passed to or from methods.
meanDiffMin	numeric that defines the minimal relevant mean difference, the smallest population mean difference that we would like to detect (with sufficient power).
wantSimData	logical, if TRUE then also output the simulated data
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)
alphaRelevance	numeric, the threshold for relevance test. Taken to be minimum of alpha and 1-power.

Value

a list which contains at least power and an adapted bootObject of class boot.

Functions

• computeBetaSafeZ(): Deprecated version of computeBetaSaviZ

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

computePowerSaviZ(meanDiffTrue=0.7, 20, nSim=10)

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