Empirical power analysis for single-case data
The power_test 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_test(
design,
stat = c("plm_level", "rand", "tauU"),
n_sim = 100,
alpha = 0.05
)
power_testSC(...)design |
An object created by design_rSC |
stat |
Defines the tests the power analysis is based on. The
default |
n_sim |
Number of sample studies created for the the Monte-Carlo study.
Default is |
alpha |
Alpha level used to calculate the proportion of significant
tests. Default is |
... |
Further arguments passed to the function. |
Juergen Wilbert
## 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_test(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_test(design, n_sim = 10, stat = c("hplm_level", "rand"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.