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

View source: R/deprecated_power.testSC.R

!! This function is deprecated. Please use the power_testSC fucntion !! The power.testSC command conducts a Monte-Carlo study on the test-power and alpha-error of a randomization-test and a piecewise-regression model. The distribution values of the Monte-Carlo sample are either specified by the user or estimated based on actual data.

power.testSC(
  data = NULL,
  dvar,
  pvar,
  mvar,
  parameters = NULL,
  stat = c("rand.test", "plm"),
  test.parameter = c("level", "slope"),
  rand.test.stat = c("Mean B-A", "B"),
  cases = NULL,
  rtt = NULL,
  level = NULL,
  slope = NULL,
  MT = NULL,
  B.start = NULL,
  trend = NULL,
  n_sim = 100,
  limit = 5,
  m = NULL,
  s = NULL,
  startpoints = NA,
  extreme.p = 0,
  extreme.d = c(-4, -3),
  exclude.equal = "auto",
  alpha = 0.05,
  distribution = "normal",
  silent = TRUE
)

`data`	A single-case data frame. See `scdf` to learn about this format.
`dvar`	Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.
`pvar`	Character string with the name of the phase variable. Defaults to the attributes in the scdf file.
`mvar`	Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.
`parameters`	-
`stat`	Defines the tests the power analysis is computed for. The default `stat = c("rand.test","plm")` computes a power analysis for the `randSC` and the `plm` analyses. Further possibilities are `"hplm"` for a hierarchiacal linear regression model and `"plm.poisson"` for a generalized piecewise-regression model under the assumption of poisson distributed errors.
`test.parameter`	Indicates whether the power and alpha error for a level effect, a slope effect, or both effects should be estimated. The default setting `test.parameter = c("level", "slope")` requests both.
`rand.test.stat`	Defines the statistic the randomization test is based on. The first values stipulates the statistic for the level-effect computation and the second value for the slope-effect computation. Default is `rand.test.stat = c("Mean B-A","B")`. Please see `randSC` for more information on the test statistics.
`cases`	Number of cases per study.
`rtt`	Reliability of the underlying simulated measurements. Default is `rtt = 0.8`.
`level`	Defines the level increase (effect size d) at the beginning of phase B.
`slope`	Defines the increase in scores - starting with phase B - expressed as effect size d per MT. `slope = .1` generates an incremental increase of 0.1 standard deviations per MT for all phase B measurements.
`MT`	Number of measurements (in each study).
`B.start`	Phase B starting point. A single value (e.g., `B.start = 6`) defines `B.start` for all studies and cases. A vector of starting values is given with the chain command (e.g., `B.start = c(6, 7, 8)`). A value between 0 and 1 is interpreted as a proportion (e.g., `B.start = c(0.3, 0.5, 0.8)` would start phase B at 30, 50, and 80% of the MTs).
`trend`	Defines the effect size d of a trend per MT added across the whole data-set.
`n_sim`	Number of sample studies created for the the Monte-Carlo study. Default is `n = 100`
`limit`	Minimal number of data points per phase in the sample. Default is `limit = 5`.
`m`	Mean of the sample distribution the data are drawn from.
`s`	Standard deviation of the sample distribution the data are drawn from.
`startpoints`	Alternative to the `limit` parameter start points exactly defines the possible start points of phase B (e.g., `startpoints = 4:9` restricts the phase B start points to measurements 4 to 9. `startpoints` overruns the `limit` parameter.
`extreme.p`	Probability of extreme values. `extreme.p = .05` gives a five percent probability of an extreme value. Default is `extreme.p = 0`.
`extreme.d`	Range for extreme values, expressed as effect size d. `extreme.d = c(-7,-6)` uses extreme values within a range of -7 and -6 standard deviations. Caution: the first value must be smaller than the second, otherwise the procedure will fail. Default is `extreme.d = c(-4,-3)`.
`exclude.equal`	If set to `exclude.equal = FALSE`, random distribution values equal to the observed distribution are counted as null-hypothesis conform. That is, they decrease the probability of rejecting the null-hypothesis (increase the p-value). Default is `exclude.equal = "auto"`, which means `FALSE` for multiple baseline designs and `TRUE` for one single-case.
`alpha`	Alpha level used to calculate the proportion of significant tests. Default is `alpha = 0.05`.
`distribution`	Indicates whether the random sample is based on a `"normal"` or a `"poisson"` distribution. Default is `distribution = "normal"`.
`silent`	If set `TRUE`, the results are not printed after computation. Default is `silent = FALSE`.

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?
mc_par <- list(
  n_cases = 1, mt = 15, B.start = round(runif (300,5,12)), 
  rtt = 0.8, level = 1.4
)
res <- power.testSC(
  parameters = mc_par, 
  stat = c("rand.test","hplm"), 
  test.parameter = "level",
  startpoints = 5:12,
  n_sim = 100
)
## Would you achieve higher power by setting up a MBD with three cases?
mc_par <- list(
  n_cases = 3, mt = 15, B.start = round(runif (300,5,12)), 
  rtt = 0.8, level = 1.4
)
power.testSC(
  parameters = mc_par, 
  stat = c("rand.test","hplm"), 
  test.parameter = "level", 
  startpoints = 5:12,
  n_sim = 10
)