scan: corrected_tau – R documentation

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corrected_tau

Baseline corrected tau

Description

Kendalls tau correlation for the dependent variable and the phase variable is calculated after correcting for a baseline trend.

Usage

corrected_tau(
  data,
  dvar,
  pvar,
  mvar,
  phases = c(1, 2),
  alpha = 0.05,
  continuity = TRUE,
  repeated = TRUE
)

corrected_tauSC(...)

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.
`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")`.
`alpha`	Sets the p-value at and below which a baseline correction is applied.
`continuity`	If TRUE applies a continuity correction for calculating p
`repeated`	If TRUE applies the repeated median method for caluclating slope and intercept (`mblm`)
`...`	Further arguments passed to the function.

Details

This method has been proposed by Tarlow (2016). The baseline data are checked for a singificant autocorrelation (based on Kendalls Tau). If so, a non-parameteric Theil-Sen regression is applied for the baseline data where the dependent values are regressed on the measurement time. The resulting slope information is then used to predict data of the B-phase. The dependent variable is now corrected for this baseline trend and the resudials of the Theil-Sen regression are taken for further caluculations. Finally, a tau is calculated for the dependent variable and the dichtomos phase variable. The function here provides two extensions to this procedure: The more accurate Siegel repeated median regression is applied when repeated = TRUE and a continuity correction is applied when continuity = TRUE (both are the default settings).

References

Tarlow, K. R. (2016). An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau. Behavior Modification, 41(4), 427–467. https://doi.org/10.1177/0145445516676750

Examples

dat <- scdf(c(A = 33,25,17,25,14,13,15, B = 15,16,16,5,7,9,6,5,3,3,8,11,7))
corrected_tau(dat)

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