outlier | R Documentation |

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

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

`data` |
A single-case data frame. See |

`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 |

`criteria` |
Specifies the criteria for outlier identification. Based on
the |

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.

`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 |

`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:
`add_l2()`

,
`as.data.frame.scdf()`

,
`as_scdf()`

,
`fill_missing()`

,
`moving_median()`

,
`ranks()`

,
`scdf()`

,
`select_cases()`

,
`set_vars()`

,
`shift()`

,
`smooth_cases()`

,
`standardize()`

,
`truncate_phase()`

```
## Identify outliers using 1.5 standard deviations as criterion
susanne <- random_scdf(level = 1.0)
res_outlier <- outlier(susanne, method = "SD", criteria = 1.5)
plot(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 <- outlier(Grosche2011, method = "Cook", criteria = "4/n")
plot(Grosche2011, marks = res_outlier)
```

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