Description Usage Arguments Value References Examples

This function executes the Multi Resolution Scanning algorithm to detect differences across multiple distributions.

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`X` |
Matrix of the data. Each row represents an observation. |

`G` |
Numeric vector of the group label of each observation. Labels are integers starting from 1. |

`n_groups` |
Number of groups. |

`Omega` |
Matrix defining the vertices of the sample space.
The |

`K` |
Depth of the tree. Default is |

`init_state` |
Initial state of the hidden Markov process.
The three states are |

`beta` |
Spatial clustering parameter of the transition probability matrix. Default is |

`gamma` |
Parameter of the transition probability matrix. Default is |

`delta` |
Optional parameter of the transition probability matrix. Default is |

`eta` |
Parameter of the transition probability matrix. Default is |

`alpha` |
Pseudo-counts of the Beta random probability assignments. Default is |

`return_global_null` |
Boolean indicating whether to return the posterior probability of the global null hypothesis. |

`return_tree` |
Boolean indicating whether to return the posterior representative tree. |

`min_n_node` |
Node in the tree is returned if there are more than |

An `mrs`

object.

Soriano J. and Ma L. (2016).
Probabilistic multi-resolution scanning for two-sample differences.
*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*.
http://onlinelibrary.wiley.com/doi/10.1111/rssb.12180/abstract

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