computeNPlanSaviT: Helper function: Computes nPlan based on deltaMin and power
In safestats: Safe Anytime-Valid Inference

computeNPlanSafeT

R Documentation

Helper function: Computes nPlan based on deltaMin and power

Description

Helper function: Computes nPlan based on deltaMin and power

Usage

computeNPlanSaviT(
  deltaTrue,
  power = 0.8,
  alpha = 0.05,
  alternative = c("twoSided", "less", "greater"),
  testType = c("oneSample", "paired", "twoSample"),
  deltaMin = NULL,
  beta = NULL,
  ratio = 1,
  parameter = NULL,
  nMax = 1e+08,
  eType = c("mom", "eGauss", "imom", "eCauchy", "grow", "lai"),
  wantSamplePaths = TRUE,
  wantSimData = TRUE,
  nuMin = 2,
  pb = TRUE,
  seed = NULL,
  nSim = 1000L,
  nBoot = nSim,
  sigma = 1,
  sigma2 = 1,
  relevanceTest = FALSE,
  relevanceSize = NULL,
  alphaRelevance = NULL,
  ...
)

Arguments

`deltaTrue`	numeric, data governing effect size used for simulations. Default deltaTrue=deltaMin.
`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".
`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.
`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.
`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 t 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 t test under continuous monitoring,or for the computation of the logarithm of the implied target.
`...`	further arguments to be passed to or from methods, but mainly to perform do.calls.
`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.
`deltaMin`	numeric that defines the minimal relevant standardised mean difference, the smallest population effect size that we would like to detect (with sufficient power).
`wantSimData`	logical, if `TRUE` then also output the simulated data
`nuMin`	numeric > 0, the minimum degrees of freedom under which the results are trivial, thus, 1.
`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)
`alphaRelevance`	numeric, the threshold for relevance test. Taken to be minimum of alpha and 1-power.

Value

a list which contains at least nPlan and an adapted bootObject of class boot().

Functions

computeNPlanSafeT(): Deprecated version of computeNPlanSaviT

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.