Plots Gaussian Mixture Model without Bayes decision boundaries, such that:

Black is the PDE of Data

Red is color of the GMM

Blue is the color of components of the mixture

`Data` |
vector (1:N) of data points |

`Means` |
vector[1:L] of Means of Gaussians (of GMM),L == Number of Gaussians |

`SDs` |
vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means |

`Weights` |
vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means |

`IsLogDistribution` |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L |

`SingleColor` |
Optional,Color for line plot of all the single gaussians, default magenta |

`MixtureColor` |
Optional,Color of line lot for the mixture default red |

`DataColor` |
Optional,Color of line plot for the data, default black |

`SingleGausses` |
Optional, If TRUE, single gaussians are shown, default FALSE |

`axes` |
Optional,Default:TRUE with axis, see argument |

`xlab` |
Optional, see |

`ylab` |
Optional, see |

`xlim` |
Optional, see |

`ylim` |
Optional, see |

`ParetoRad` |
Optional: Precalculated Pareto Radius to use |

`...` |
other plot arguments like xlim = c(1,10) |

Michael Thrun

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

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