Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.

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`object` |
an object of class |

`dim` |
the dimensions of the reduced subspace used for prediction. |

`newdata` |
a data frame or matrix giving the data. If missing the data obtained from the call to |

`eval.points` |
a data frame or matrix giving the data projected on the reduced subspace. If provided |

`...` |
further arguments passed to or from other methods. |

Returns a list of with the following components:

`dir` |
a matrix containing the data projected onto the |

`density` |
densities from mixture model for each data point. |

`z` |
a matrix whose |

`uncertainty` |
The uncertainty associated with the classification. |

`classification` |
A vector of values giving the MAP classification. |

Luca Scrucca

Scrucca, L. (2010) Dimension reduction for model-based clustering.
*Statistics and Computing*, 20(4), pp. 471-484.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

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