computeBetaBatchSaviZ: Helper function: Computes 1-power based on meanDiffTrue and...

In safestats: Safe Anytime-Valid Inference

Helper function: Computes 1-power based on meanDiffTrue and nPlan

Description

Helper function: Computes 1-power based on meanDiffTrue and nPlan

Usage

computeBetaBatchSaviZ(
  meanDiffTrue,
  nPlan,
  alpha = 0.05,
  sigma = 1,
  kappa = sigma,
  alternative = c("twoSided", "greater", "less"),
  testType = c("oneSample", "paired", "twoSample"),
  parameter = NULL,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow")
)

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.
sigma	numeric > 0 representing the assumed population standard deviation used to scale the data.
kappa	the true population standard deviation. Default kappa=sigma.
alternative	a character string specifying the alternative hypothesis. Must be one of "twoSided" (default), "greater" or "less".
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.

Value

numeric that represents the type II error

Functions

• computeBetaBatchSafeZ(): Deprecated version of computeBetaBatchSaviZ

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.

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