Description Usage Arguments Details Value Author(s) References See Also Examples

Winsorisation of outliers according to the Mahalanobis distance followed by an imputation under the multivariate normal model. Only the outliers are winsorized. The Mahalanobis distance `MDmiss`

allows for missing values.

1 |

`data` |
Data frame with the data |

`center` |
(Robust) estimate of the center (location) of the observations |

`scatter` |
(Robust) estimate of the scatter (covariance-matrix) of the observations |

`outind` |
Logical vector indicating outliers with 1 or TRUE for outliers |

`seed` |
Seed for random number generator |

It is assumed that `center`

, `scatter`

and `outind`

stem from a multivariate outlier detection algorithm which produces robust estimates and which declares outliers observations with a large Mahalanobis distance. The cutpoint is calculated as the least (unsquared) Mahalanobis distance among the outliers. The winsorization reduces the weight of the outliers:

*y_i=μ_R +(y_i-μ_R)*c/d_i*

, where *μ_R* is the robust center and *d_i* is the (unsquared) Mahalanobis distance of observation i.

Function `winsimp`

returns a list whose first component `output`

is a sub-list with the follwing components:

`cutpoint` |
Cutpoint for outliers |

`proc.time` |
Processing time |

`n.missing.before` |
Number of missing values before |

`n.missing.after` |
Number of missing values after imputation |

The further component returned by `winsimp`

is

`imputed.data` |
Imputed data set. |

Beat Hulliger

Hulliger, B. (2007) Multivariate Outlier Detection and Treatment in Business Surveys, Proceedings of the III International Conference on Establishment Surveys, Montr\'eal.

`MDmiss`

.
Uses `imp.norm`

from the `norm`

package.

1 2 3 4 | ```
data(bushfirem,bushfire.weights)
det.res<-TRC(bushfirem,weight=bushfire.weights)
imp.res<-Winsimp(bushfirem,det.res$output$center,det.res$output$scatter,det.res$outind)
print(imp.res$output)
``` |

modi documentation built on May 31, 2017, 5 a.m.

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