# 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

1 |

### Arguments

`data` |
A single-case data frame or a list of single-case data frames. See |

`criteria` |
Specifies the criteria for outlier identification. Set |

### 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 |

`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. |

### Author(s)

Juergen Wilbert

### See Also

`describeSC`

, `fillmissingSC`

, `plotSC`

### Examples

1 2 3 4 5 6 7 8 9 | ```
## 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))
``` |