Description Usage Arguments Value Author(s) References Examples

Fit a GMM via Stochastic Approximation. See Reference.

1 2 |

`X` |
numeric matrix of the data. |

`Y` |
Group membership (if known). Where groups are integers in 1:ngroups. If provided ngroups can |

`Burnin` |
Ratio of observations to use as a burn in before algorithm begins. |

`ngroups` |
Number of mixture components. If Y is provided, and groups is not then is overridden by Y. |

`kstart` |
number of kmeans starts to initialise. |

`plot` |
If TRUE generates a plot of the clustering. |

A list containing

`Cluster` |
The clustering of each observation. |

`plot` |
A plot of the clustering (if requested). |

`l2` |
Estimate of Lambda^2 |

`ARI1` |
Adjusted Rand Index 1 - using k-means |

`ARI2` |
Adjusted Rand Index 2 - using GMM Clusters |

`ARI3` |
Adjusted Rand Index 3 - using intialiation k-means |

`KM` |
Initial K-means clustering of the data. |

`pi` |
The cluster proportions (vector of length ngroups) |

`tau` |
tau matrix of conditional probabilities. |

`fit` |
Full output details from inner C++ loop. |

Andrew T. Jones and Hien D. Nguyen

Nguyen & Jones (2018). Big Data-Appropriate Clustering via Stochastic Approximation and Gaussian Mixture Models. In Data Analytics (pp. 79-96). CRC Press.

1 2 3 | ```
sims<-generateSimData(ngroups=10, Dimensions=10, Number=10^4)
res1<-SAGMMFit(sims$X, sims$Y)
res2<-SAGMMFit(sims$X, ngroups=5)
``` |

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