scan (version 0.20)

outlierSC: Handling outliers in single-case data

Description

Identifies and drops outliers within a single-case data frame or a list of single-case data frames.

Usage

outlierSC(data, criteria = c("SD", "2"))

Arguments

data

A single-case data frame or a list of single-case data frames. See makeSCDF to learn about this format.

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.

Value

data

A data frame (for each single-case) without 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.

See Also

describeSC, fillmissingSC, plotSC

Examples

# NOT RUN {
## Identify outliers using 1.5 standard deviations as criterion
susanne <- rSC(d.level = 1.0)
res <- outlierSC(susanne, criteria = c("SD", 1.5))
plotSC(susanne, marks = list(positions = res$dropped.mt))

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