scan: pem – R documentation

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pem

Percent exceeding the median

Description

The pem function returns the percentage of phase B data exceeding the phase A median. Additionally, a chi square test against a 50/50 distribution is computed. Different measures of central tendency can be addressed for alternative analyses.

Usage

pem(
  data,
  dvar,
  pvar,
  decreasing = FALSE,
  binom.test = TRUE,
  chi.test = FALSE,
  FUN = median,
  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.
`decreasing`	If you expect data to be lower in the B phase, set `decreasing = TRUE`. Default is `decreasing = FALSE`.
`binom.test`	Computes a binomial test for a 50/50 distribution. Default is `binom.test = TRUE`.
`chi.test`	Computes a Chi-square test. The default setting `chi.test = FALSE` skips the Chi-square test.
`FUN`	Data points are compared with the phase A median. Use this argument to implement alternative measures of central tendency. Default is `FUN = median`
`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")`.
`...`	Additional arguments for the `FUN` parameter (e.g. `FUN = mean, trim = 0.1` will use the 10 percent trimmed arithmetic mean instead of the median for comparisons). The function must take a vector of numeric values and the `na.rm` argument and return a numeric value.

Author(s)

Juergen Wilbert

Examples

## Calculate the PEM including the Binomial and Chi-square tests for a single-case
dat <- rSC(5, level = 0.5)
pem(dat, chi.test = TRUE)

scan

Single-Case Data Analyses for Single and Multiple Baseline Designs

v0.51

GPL

Authors

Juergen Wilbert [aut, cre], Timo Lueke [aut]

Initial release

2021-2-11

pem