AdaptGauss {AdaptGauss} | R Documentation |

Adapt interactively a Gaussians Mixture Model GMM to the empirical PDF of the data (generated by DataVisualizations::ParetoDensityEstimation) such that N(Means,SDs)*Weights is a model for Data

AdaptGauss(Data, Means = NaN, SDs = NaN, Weights = NaN, ParetoRadius = NaN, LB = NaN, HB = NaN, ListOfAdaptGauss, fast = T)

`Data` |
Data for empirical PDF. Has to be an Array of values. NaNs and NULLs will be deleted |

`Means` |
Optional: Means of gaussians of GMM. |

`SDs` |
Optional: StandardDevations of gaussians of GMM. (Has to be the same length as Means) |

`Weights` |
Optional: Weights of gaussians of GMM. (Has to be the same length as Means) |

`ParetoRadius` |
Optional: Pareto Radius of Pareto Desity Estimation (PDE). |

`LB` |
Optional: Low boundary of estimation. All values below LB will be deleted. Default: min(Data) |

`HB` |
Optional: High boundary of estimation. All values above HB will be deleted. Default: max(Data) |

`ListOfAdaptGauss` |
Optional: If editing of an existing Model is the goal, enables to give the Output of AdaptGaus as the Input of AdaptGauss() instead of setting Means, SDs and Weights separately |

`fast` |
Default=TRUE; FALSE: Using mclust's EM see function |

Data: maximum length is 10000. If larger, Data will be randomly reduced to 10000 Elements. MeansIn/DeviationsIn/WeightsIN: If empty, either one or three Gaussian's are generated by kmeans algorithm. Pareto Radius: If empty: will be generated by DataVisualizations::ParetoDensityEstimation RMS: Root Mean Square error is normalized by RMS of Gaussian's with Mean=mean(data) and SD=sd(data), see [Ultsch et.al., 2015] for further details.

List with

`Means` |
Means of Gaussian's. |

`SDs` |
Standard SDs of Gaussian's. |

`Weights` |
Weights of Gaussian's. |

`ParetoRadius` |
Pareto Radius: Either ParetoRadiusIn, the pareto radius enerated by PretoDensityEstimation(if no Pareto Radius in Input). |

`RMS` |
Root Mean Square of Deviation between Gaussian Mixture Model GMM to the empirical PDF. Normalized by RMS of one Gaussian with mean=meanrobust(data) and sdev=stdrobust(data). Further Details in [Ultsch et al 2015] |

`BayesBoundaries` |
vector[1:L-1], Bayes decision boundaries |

Onno Hansen-Goos, Michael Thrun

Ultsch, A., Thrun, M.C., Hansen-Goos, O., Loetsch, J.: Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox(AdaptGauss), International Journal of Molecular Sciences, doi:10.3390/ijms161025897, 2015.

Thrun M.C., Ultsch, A.: Models of Income Distributions for Knowledge Discovery, European Conference on Data Analysis, DOI 10.13140/RG.2.1.4463.0244, Colchester 2015.

data1=c(rnorm(1000)) ## Not run: Vals1=AdaptGauss(data1) data2=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1) ## Not run: Vals2=AdaptGauss(data2,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5),0.3,-6,6)

[Package *AdaptGauss* version 1.5.6 Index]