Description Usage Arguments Value References

Compute the Distance in Measure between the clustering induced by a kernel density estimator (based on a sample x and a bandwidth h) and the population clustering defined by a K-component normal mixture density.

1 |

`x` |
(vector) the data to be partitioned. |

`h` |
the bandwidth to be used to estimate the density via KDE. |

`mus` |
vector of means of the mixture components. |

`sigmas` |
vector of standard deviations of the mixture components. |

`props` |
vector of mixing proportions of the mixture components. |

`plot` |
if true, the true density and the estimated one are displayed. |

the value of the Distance in Measure.

Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science 30(4): 518-532.

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