Description Usage Arguments Value Examples

With the previous calculated similarity matrix or the original categorical dataframe, the results of both overlap clustering and hierarchical clustering are obtained with several recommended cluster numbers(k) after processing the merge cluster step.

1 2 3 |

`data` |
an original categorical data with n observations and p variables. |

`data.pre` |
an list obtained by |

`alpha` |
A power scaling for Bossa scores, representing the weight of variable sigma value. |

`p` |
A set of quantiles(90 similarity matrix to form clusters at different levels of within-cluster similarity. |

`lin` |
A tuning parameter to control the size of each overlap cluster before merging, smaller lin leads to larger cluster size. |

`is.pca` |
A logical variable indicating if the Bossa scores should transformed to principle components and then calculate the similarity matrix. It is recommended when processing the ultra-dimension data. |

`pca.sum.prop` |
A numeric indicating how many components should be reserved
in order to make this proportion of variance. The default is |

`n.comp` |
The number of components of PCA. The default is |

`fix.pca.comp` |
A numeric variable indicating whether choosing the fixed number of components or the fixed proportion of variance and the default is to choose fixed proportion. |

`cri` |
A tuning parameter, if p value smaller than cri, then reject
the NULL hypothesis and merge overlap sub-clusters. And cri can be any numeric less
than |

`lintype` |
The agglomeration method to be used in |

`perplexity` |
A parameter of tsne |

An object including overlap clusters after merging and non-overlap
clusters, which can be showed by function `bossa_interactive`

1 2 3 4 | ```
{
data(bo.simu.data)
object <- BossaClust(bo.simu.data)
}
``` |

boclust documentation built on Dec. 4, 2017, 9:04 a.m.

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