designSaviZ1aWantNPlan: Helper function to designing a savi Z-test (output nPlan)

In safestats: Safe Anytime-Valid Inference

Helper function to designing a savi Z-test (output nPlan)

Description

Finds the parameter and power when provided with only alpha, esMin, and nPlan

Usage

designSaviZ1aWantNPlan(
  meanDiffMin,
  power,
  alpha = 0.05,
  alternative = c("twoSided", "greater", "less"),
  sigma = 1,
  kappa = sigma,
  beta = NULL,
  testType = c("oneSample", "paired", "twoSample"),
  ratio = 1,
  parameter = NULL,
  meanDiffTrue = NULL,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow"),
  wantSamplePaths = TRUE,
  wantSimData = TRUE,
  pb = TRUE,
  seed = NULL,
  nSim = 1000L,
  nBoot = nSim,
  relevanceTest = FALSE,
  relevanceSize = NULL,
  alphaRelevance = NULL,
  highN = 10000L,
  ...
)

Arguments

meanDiffMin	numeric that defines the minimal relevant mean difference, the smallest population mean difference that we would like to detect (with sufficient power).
beta	numerical in (0,1). Old parameter now replaced by the power parameter
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".
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.
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.
relevanceTest	logical, if TRUE then impose a rule to stop for minimal efficiency if e <= alphaRelevance. Default FALSE.
...	further arguments to be passed to or from methods.
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.
meanDiffTrue	numeric, data governing mean difference used for simulations. Default meanDiffTrue=meanDiffMin.
wantSimData	logical, if TRUE then also output the simulated data
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.
highN	integer, largest possible sampling horizon. This might be the largest n that we are able to fund, which by default is set to 1e4L. Typically, highN is not used, as the function computeNPlanBatchSaviZ() tries to find the sampling horizon. If all fails, then use highN as the sampling horizon.

Value

A list with the parameter and the targeted nPlan amongst other items

Functions

• designSafeZ1aWantNPlan(): Deprecated version of designSaviZ1aWantNPlan

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

designSaviZ1aWantNPlan(meanDiffMin=0.9, power=0.7, nSim=10)

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