Model names to be used in the upclass package for univariate and multivariate data.

1 | ```
modelvec(d = 1)
``` |

`d` |
The dimension of the data. By default, |

if `d=1`

, returned is a vector with the first two of the following components only; otherwise, they are omitted and the vector contains the remaining components:

`"E"` |
Univariate, equal variance |

`"V"` |
Univariate, variable variance |

`"EII"` |
Multivariate, equal volume and spherical |

`"VII"` |
Multivariate, variable volume and spherical |

`"EEI"` |
Multivariate, equal volume, equal shape and axis aligned |

`"VEI"` |
Multivariate, variable volume, equal shape and axis aligned |

`"EVI"` |
Multivariate, equal volume, variable shape and axis aligned |

`"VVI"` |
Multivariate, variable volume, variable shape and axis aligned |

`"EEE"` |
Multivariate, equal volume, equal shape and equal orientation |

`"EEV"` |
Multivariate, equal volume, equal shape and variable orientation |

`"VEV"` |
Multivariate, variable volume, equal shape and variable orientation |

`"VVV"` |
Multivariate, variable volume, variable shape and variable orientation |

Banfield, J.D. and Raftery, A.E. (1993).
Model based Gaussian and non-gaussian clustering.
*Biometrics*, 49 (3): 803-821.

Fraley, C. and Raftery, A.E. (2002).
Model-based clustering, discriminant analysis, and density estimation.
*Journal of the Americal Statistical Association* 97 (458), 611-631.

`upclassify`

, `noupclassify`

.

1 2 3 4 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.