outlier: Handling outliers in single-case data

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

Handling outliers in single-case data

Description

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

Usage

## S3 method for class 'sc_outlier'
print(x, digits = "auto", ...)

## S3 method for class 'sc_outlier'
export(object, caption = NA, footnote = NA, filename = NA, ...)

outlier(
  data,
  dvar,
  pvar,
  mvar,
  method = c("MAD", "Cook", "SD", "CI"),
  criteria = 3.5
)

Arguments

x	An object returned by outlier()
digits	The minimum number of significant digits to be use. If set to "auto" (default), values are predefined.
...	Further parameters passed to the print function
object	An scdf or an object exported from a scan function.
caption	Character string with table caption. If left NA (default) a caption will be created based on the exported object.
footnote	Character string with table footnote. If left NA (default) a footnote will be created based on the exported object.
filename	String containing the file name. If a filename is given the output will be written to that file.
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.
method	Specifies the method for outlier identification. Set method = "MAD" for mean average deiviation, method = "SD" for standard deviations, method = "CI" for confidence intervals, method = "Cook" for Cook's Distance based on the Piecewise Linear Regression Model.
criteria	Specifies the criteria for outlier identification. Based on the method setting.

Details

For method = "SD", criteria = 2 would refer t0 two standard deviations. For method = "MAD", criteria = 3.5 would refer to 3.5 times the mean average deviation. For method = "CI", criteria = 0.99 would refer to a 99 percent confidence interval. For method = "cook", criteria = "4/n" would refer to a Cook's Distance greater than 4/n.

Value

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.

Functions

• print(sc_outlier): Print results

• export(sc_outlier): Export html results

Author(s)

Juergen Wilbert

See Also

Other data manipulation functions: add_l2(), as.data.frame.scdf(), as_scdf(), fill_missing(), moving_median(), ranks(), rescale(), scdf(), select_cases(), set_vars(), shift(), smooth_cases(), standardize(), truncate_phase()

Examples

## Identify outliers using 1.5 standard deviations as criterion
susanne <- random_scdf(level = 1.0)
res_outlier <- outlier(susanne, method = "SD", criteria = 1.5)
res_outlier

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

scan documentation built on Sept. 11, 2025, 5:12 p.m.