Description Usage Arguments Details Value Note Author(s) References Examples

Detects if data points are noise or part of a cluster, based on a Poisson process model.

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`data` |
numerical matrix or data frame. |

`k` |
integer. Number of considered nearest neighbors per point. |

`distances` |
distance matrix object of class |

`edge.correct` |
logical. If |

`wrap` |
numerical. If |

`convergence` |
numerical. Convergence criterion for EM-algorithm. |

`plot` |
logical. If |

`quiet` |
logical. If |

`x` |
object of class |

`...` |
necessary for print methods. |

The assumption is that the noise is distributed as a homogeneous Poisson process on a certain region and the clusters are distributed as a homogeneous Poisson process with larger intensity on a subregion (disconnected in case of more than one cluster). The distances are then distributed according to a mixture of two transformed Gamma distributions, and this mixture is estimated via the EM-algorithm. The points are assigned to noise or cluster component by use of the estimated a posteriori probabilities.

`NNclean`

returns a list of class `nnclean`

with components

`z` |
0-1-vector of length of the number of data points. 1 means cluster, 0 means noise. |

`probs` |
vector of estimated a priori probabilities for each point to belong to the cluster component. |

`k` |
see above. |

`lambda1` |
intensity parameter of cluster component. |

`lambda2` |
intensity parameter of noise component. |

`p` |
estimated probability of cluster component. |

`kthNND` |
distance to kth nearest neighbor. |

The software can be freely used for non-commercial purposes, and can be freely distributed for non-commercial purposes only.

R-port by Christian Hennig
[email protected]
http://www.homepages.ucl.ac.uk/~ucakche,

original Splus package by S. Byers and A. E. Raftery.

Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter
Removal for Estimating Features in Spatial Point Processes,
*Journal of the American Statistical Association*, 93, 577-584.

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