Kendall's tau correlation for the dependent variable and the phase variable is calculated after correcting for a baseline trend.
corrected_tau(
data,
dvar,
pvar,
mvar,
phases = c(1, 2),
alpha = 0.05,
continuity = FALSE,
repeated = FALSE
)
A single-case data frame. See scdf
to learn about
this format.
Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.
Character string with the name of the phase variable. Defaults to the attributes in the scdf file.
Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.
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)
.
Sets the p-value at and below which a baseline correction is applied.
If TRUE applies a continuity correction for calculating p
If TRUE applies the repeated median method for calculating
slope and intercept (mblm
)
This method has been proposed by Tarlow (2016). The baseline data
are checked for a significant autocorrelation (based on Kendall's Tau).
If so, a non-parametric 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
residuals of the Theil-Sen regression are taken for further calculations.
Finally, Kendall's tau is calculated for the dependent variable and the
dichotomous 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
.
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
Other regression functions:
hplm()
,
mplm()
,
plm()
Other overlap functions:
nap()
,
overlap()
,
pand()
,
pem()
,
pet()
,
pnd()
,
tau_u()
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)
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