Description Usage Arguments Details Value Examples

Hierachical ordered density clustering (HODC) Algorithm with input generated by DPdensity

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`v` |
number of iterations set for DPM fitting by "DPdensity" |

`pvalue` |
a vector of p-values obtained from large scale statistical hypothesis testing |

`DPM.mcmc` |
list |

`DPM.prior` |
list |

Without the information of networking, we can have an approximation to the marginal density by DPM model fitting on **r**. Suppose the number of finite mixture normals is equal to L_0+L_1, which means the number of classes we have, we apply HODC algorithm in partitioning the $L_0$ and $L_1$ components into two classes.
For this function, the input is generated by Mclust

a list of HODC algorithm returned parameters.

- mean
the means of each of two cluster for every DPM fitting by "DPdensity"

- mu0
the means of the cluster with smaller mean

- mu1
the means of the cluster with larger mean

- variance
the variance of each of two cluster for every DPM fitting by "DPdensity"

- var0
the variances of the cluster with smaller mean

- var1
the variances of the cluster with larger mean

- probability
the probability of each of two cluster for every DPM fitting by "DPdensity"

- pro0
the probabilities of the cluster with smaller mean

- pro1
the probabilities of the cluster with larger mean

- classification
The classification corresponding to each cluster for every DPM fitting by "DPdensity"

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