After running `EAdet`

an imputation of the detected outliers with `EAimp`

may be run.

1 2 3 4 5 6 7 |

`data` |
a data frame or matrix with the data |

`weights` |
a vector of positive sampling weights |

`outind` |
a logical vecotr with component TRUE for outliers |

`reach` |
reach of the threshold function (usually set to the maximum distance to a nearest neighbour, see internal function |

`transmission.function` |
form of the transmission function of distance |

`power` |
sets |

`distance.type` |
distance type in function |

`maxl` |
Maximum number of steps without infection |

`monitor` |
if |

`threshold` |
Infect all remaining points with infection probability above the threshold |

`deterministic` |
if |

`duration` |
The duration of the detection epidemic |

`kdon` |
The number of donors that should be infected before imputation |

`fixedprop` |
If |

`EAimp`

uses the distances calculated in `EAdet`

(actually the counterprobabilities, which are stored in a global data set) and starts an epidemic at each observation to be imputed until donors for the missing values are infected. Then a donor is selected randomly.

`EAimp`

returns a list with components `parameters`

and `imputed.data`

.

`parameters`

contains the following components:

`sample.size` |
Number of observations |

`number.of.variables` |
Number of variables |

`n.complete.records` |
Number of records without missing values |

`n.usable.records` |
Number of records with less than half of values missing (unusable observations are discarded) |

`duration` |
Duration of epidemic |

`reach` |
Transmission distance (d0) |

`threshold` |
Input parameter |

`deterministic` |
Input parameter |

`computation.time` |
Elapsed computation time |

`imputed.data`

contains the imputed data.

Beat Hulliger

B\'eguin, C., and Hulliger, B. (2004). Multivariate oulier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A 167(Part 2.), 275-294.

`EAdet`

for outlier detection with the Epicemic Algorithm.

1 2 3 4 5 | ```
data(bushfirem,bushfire.weights)
det.res<-EAdet(bushfirem,bushfire.weights)
imp.res<-EAimp(bushfirem,bushfire.weights,outind=det.res$outind,
reach=det.res$output$max.min.di,kdon=3)
print(imp.res$output)
``` |

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