Description Usage Arguments Value Author(s) References Examples

This function conducts the clustering of binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015).

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`Y` |
Binary data matrix (N * D), where N denotes sample size and D denotes the number of binary variables (0 or 1). |

`N.comp` |
The number of component to be extracted. |

`N.clust` |
The number of mixture components, which corresponds to the number of clusters. |

`lambda` |
A tuning parameter of an L1 penalty for loadings. A non-negative real value should be used as the value of lambda. |

`N.ite` |
The number of maximum of iterations for the EM algorithm. |

`N.random` |
The number of random sets of parameters for initial random starts. |

`show.random.ite` |
If |

`eps` |
The criterion for the convergence of the alternating least-squares
algorithm, which should be specified as a positive real value. If
the difference between the values of penalized log likelihood
functions of successive iteration is smaller than |

`mc.cores` |
If |

`F` |
An estimated component score matrix for cluster centroids. |

`A` |
An estimated loading matrix. |

`mu` |
Estimated mean values in the subspace. |

`U` |
The cluster assignment matrix (N * |

`g` |
The estimated mixture probability. |

`n.ite` |
The number of iteration needed for convergence. |

`loss` |
The value of log likelihood with L1 penalty. |

`bic` |
The value of BIC. |

`LL` |
The value of log likelihood. |

`cluster` |
Estimated clusters where subjects were assigned to |

`ptime` |
Time for calculation |

Michio Yamamoto

michio.koko@gmail.com

Yamamoto, M. and Hayashi, K. (2015). Clustering of multivariate binary data with dimension reduction via L1-regularized maximization. Pattern Recognition, 48, 3959-3968.

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