outlierSC: Handling outliers in single-case data
In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Description Usage Arguments Value Author(s) See Also Examples

Identifies and drops outliers within a single-case data frame (scdf).

1	outlierSC(data, dvar, pvar, mvar, criteria = c("MAD", "3.5"))

`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.
`criteria`	Specifies the criteria for outlier identification. Set `criteria = c("SD", 2)` to define two standard deviations as limit. This is also the default setting. To use the 99% Confidence Interval use `criteria = c("CI", 0.99)`. Set `criteria = c("Cook", "4/n")` to define any data point with a Cook's Distance greater than 4/n as an outlier, based on the Piecewise Linear Regression Model.

`data`	A single-case data frame with substituted outliers.
`dropped.n`	A list with the number of dropped data points for each single-case.
`dropped.mt`	A list with the measurement-times of dropped data points for each single-case (values are based on the `mt` variable of each single-case data frame).
`sd.matrix`	A list with a matrix for each case with values for the upper and lower boundaries based on the standard deviation.
`ci.matrix`	A list with a matrix for each single-case with values for the upper and lower boundaries based on the confidence interval.
`cook`	A list of Cook's Distances for each measurement of each single-case.
`criteria`	Criteria used for outlier analysis.
`N`	Number of single-cases.
`case.names`	Case identifier.

Juergen Wilbert

Other data manipulation functions: fillmissingSC(), longSCDF(), rankSC(), scaleSC(), shiftSC(), smoothSC(), truncateSC()

## Identify outliers using 1.5 standard deviations as criterion
susanne <- rSC(level = 1.0)
res.outlier <- outlierSC(susanne, criteria = c("SD", 1.5))
plotSC(susanne, marks = res.outlier)

## Identify outliers in the original data from Grosche (2011) using Cook's Distance
## greater than 4/n as criterion
res.outlier <- outlierSC(Grosche2011, criteria = c("Cook", "4/n"))
plotSC(Grosche2011, marks = res.outlier)