power_testSC: Empirical power analysis for single-case data
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Author(s) See Also Examples

The power_testSC command conducts a Monte-Carlo study on the test-power and alpha-error of a set of single-cases. The distribution values of the Monte-Carlo sample are either specified by the user or estimated based on actual data.

power_testSC(
  design,
  stat = c("plm_level", "rand", "tauU"),
  n_sim = 100,
  alpha = 0.05
)

`design`	An object created by design_rSC
`stat`	Defines the tests the power analysis is based on. The default `stat = c("plm_level", "rand", "tauU")` computes a power analysis based on `tauUSC`, `randSC` and `plm` analyses. Further possibilities are: "plm_slope", "plm_poisson_level", "plm_poisson_slope", "hplm_level", "hplm_slope", "base_tau".
`n_sim`	Number of sample studies created for the the Monte-Carlo study. Default is `n = 100`
`alpha`	Alpha level used to calculate the proportion of significant tests. Default is `alpha = 0.05`.

Juergen Wilbert

plm, randSC

## Assume you want to conduct a single-case study with 15 MTs, using a highly reliable test,
## an expected level effect of \eqn{d = 1.4}, and randomized start points between MTs 5
## and 12 can you expect to identify the effect using plm or randomization test?
design <- design_rSC(
  n = 1, phase.design = list(A = 6, B = 9), 
  rtt = 0.8, level = 1.4
)
res <- power_testSC(design, n_sim = 10)

## Would you achieve higher power by setting up a MBD with three cases?
design <- design_rSC(
  n = 3, phase.design = list(A = 6, B = 9), 
  rtt = 0.8, level = 1.4
)
res <- power_testSC(design, n_sim = 10, stat = c("hplm_level", "rand"))