pet: Percent exceeding the trend
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Value Author(s) See Also Examples

The pet function provides the percentage of phase B data points exceeding the prediction based on the phase A trend. A binomial test against a 50/50 distribution is computed. Furthermore, the percentage of phase B data points exceeding the upper (or lower) 95 percent confidence interval of the predicted progress is computed.

pet(
  data,
  dvar,
  pvar,
  mvar,
  ci = 0.95,
  decreasing = FALSE,
  phases = c("A", "B")
)

`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.
`ci`	Width of the confidence interval. Default is `ci = 0.95`.
`decreasing`	If you expect data to be lower in the B phase, set `decreasing = TRUE`. Default is `decreasing = FALSE`.
`phases`	A vector of two characters or numbers indicating the two phases that should be compared. E.g., `phases = c("A","C")` or `phases = c(2,4)` for comparing the second to the fourth phase. Phases could be combined by providing a list with two elements. E.g., `phases = list(A = c(1,3), B = c(2,4))` will compare phases 1 and 3 (as A) against 2 and 4 (as B). Default is `phases = c("A","B")`.

`PET`	Percent exceeding the trend.
`PET.ci`	Percent exceeding the upper / lower 95%-CI boundary.
`p`	P value of Binomial Test.
`ci.percent`	Width of confidence interval in percent.
`se.factors`	Standard error.
`N`	Number of cases.
`decreasing`	Logical argument from function call (see `Arguments` above).
`case.names`	Assigned name of single-case.
`phases`	-

Juergen Wilbert

Other overlap functions: corrected_tauSC(), nap(), overlapSC(), pand(), pem(), pnd(), tauUSC()

## Calculate the PET and use a 99%-CI for the additional calculation
# create random example data
design <- design_rSC(n = 5, slope = 0.2)
dat <- rSC(design, seed = 23)
pet(dat, ci = .99)