pet: Percent exceeding the trend

In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Percent exceeding the trend

Description

The pet function returns the percentage of Phase B data points that exceed the prediction based on the Phase A trend. A binomial test against a 50/50 distribution is calculated. It also calculates the percentage of Phase B data points that exceed the upper (or lower) 95 percent confidence interval of the predicted progression.

Usage

pet(data, dvar, pvar, mvar, ci = 0.95, decreasing = FALSE, phases = c(1, 2))

Arguments

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(1,2).

Value

PET	Percent exceeding the trend.
PET.ci	Percent exceeding the upper / lower 95\ 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	-

Author(s)

Juergen Wilbert

See Also

Other overlap functions: cdc(), nap(), overlap(), pand(), pem(), pnd(), tau_u()

Examples

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

scan documentation built on Aug. 8, 2023, 5:07 p.m.