designFreqZ {safestats}

Design a Frequentist Z-Test

Description

Computes the number of samples necessary to reach a tolerable type I and type II error for the frequentist z-test.

Usage

designFreqZ(
  meanDiffMin,
  alternative = c("twoSided", "greater", "less"),
  alpha = 0.05,
  beta = 0.2,
  testType = c("oneSample", "paired", "twoSample"),
  ratio = 1,
  sigma = 1,
  h0 = 0,
  kappa = sigma,
  lowN = 3L,
  highN = 100L,
  ...
)

Arguments

meanDiffMin

numeric that defines the minimal relevant mean difference, the smallest population mean that we would like to detect.

alternative

a character string specifying the alternative hypothesis must be one of "twoSided" (default), "greater" or "less".

alpha

numeric in (0, 1) that specifies the tolerable type I error control –independent on n– that the designed test has to adhere to. Note that it also defines the rejection rule e10 > 1/alpha.

beta

numeric in (0, 1) that specifies the tolerable type II error control necessary to calculate both "n" and "phiS". Note that 1-beta defines the power.

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.

sigma

numeric > 0 representing the assumed population standard deviation used for the test.

h0

numeric, represents the null hypothesis, default h0=0.

kappa

the true population standard deviation. Default kappa=sigma.

lowN

integer that defines the smallest n of our search space for n.

highN

integer that defines the largest n of our search space for n. This might be the largest n that we are able to fund.

...

further arguments to be passed to or from methods.

Value

returns a 'freqZDesign' object.

Examples

freqDesign <- designFreqZ(meanDiffMin = 0.5, highN = 100)
freqDesign$nPlan
freqDesign2 <- designFreqZ(meanDiffMin = 0.2, lowN = 32, highN = 200)
freqDesign2$nPlan