Package 'opGMMassessment'

Example data of lysophosphatidic acids, LPA.

Description

Data set containing times of detector hits after chromatographic separation of five different lysophosphatidic acids (Classes CLs = LPA 16:0, 18:0, 18:3, 20:0, and 20:4).

Usage

data("Chromatogram")

Details

Size 1166 x 3 , stored in Chromatogram$[Cls, Time, Lipids]

Examples

data(Chromatogram)
str(Chromatogram)

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Plot of Gaussian mixtures

Description

The function plots the components of a Gaussian mixture and superimposes them on a histogram of the data.

Usage

GMMplotGG(Data, Means, SDs, Weights, BayesBoundaries, 
	SingleGausses = TRUE, Hist = FALSE, Bounds = TRUE, SumModes = TRUE, PDE = TRUE)

Arguments

Data	the data as a vector.
Means	a list of mean values for a Gaussian mixture.
SDs	a list of standard deviations for a Gaussian mixture.
Weights	a list of weights for a Gaussian mixture.
BayesBoundaries	a list of Bayesian boundaries for a Gaussian mixture.
SingleGausses	whether to plot the single Gaussian components as separate lines.
Hist	whether to plot a histgram of the original data.
Bounds	whether to plot the Bayesian boundaries for a Gaussian mixture as vertical lines.
SumModes	whether to plot the summed-up mixes.
PDE	whether to use the Pareto density estimation instead of the standard R density function.

Value

Returns a ggplot2 object.

p1	the plot of Gaussian mixtures.

Author(s)

Jorn Lotsch and Sebastian Malkusch

References

Lotsch, J., Malkusch S. (2021): opGMMassessment – an R Package for automated Guassian mixture modeling.

Examples

## example 1
data(iris)
Means0 <- tapply(X = as.vector(iris[,3]), INDEX =  as.integer(iris$Species), FUN = mean)
SDs0 <- tapply(X = as.vector(iris[,3]), INDEX =  as.integer(iris$Species), FUN = sd)
Weights0 <- c(1/3, 1/3, 1/3)
GMM.Sepal.Length <- GMMplotGG(Data = as.vector(iris[3]), 
	Means = Means0, 
	SDs = SDs0, 
	Weights = Weights0, 
	Hist = TRUE)

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Example Gaussian mixture data.

Description

Data set containing 1000 instances distributed according to a Gaussian mixture with m = [-10, 0, 10], s = [1, 2, 3], w = [0.07, 0.05, 0.88].

Usage

data("Mixture3")

Details

Size 1000 x 1

Examples

data(Mixture3)
str(Mixture3)

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Gaussian mixture assessment

Description

The package provides the necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. It provides various methods for parameter estimation and for determining the number of modes in the mixture.

Usage

opGMMassessment(Data, FitAlg = "MCMC", Criterion = "LR",
MaxModes = 8, MaxCores = getOption("mc.cores", 2L), PlotIt = FALSE, KS = TRUE, Seed)

Arguments

Data	the data as a vector.
FitAlg	which fit algorithm to use: "ClusterRGMM" = GMM from ClusterR, "densityMclust" from mclust, "DO" from DistributionOptimization (slow), "MCMC" = NMixMCMC from mixAK, or "normalmixEM" from mixtools.
Criterion	which criterion should be used to establish the number of modes from the best GMM fit: "AIC", "BIC", "FM", "GAP", "LR" (likelihood ratio test), "NbClust" (from NbClust), "SI" (Silverman).
MaxModes	the maximum number of modes to be tried.
MaxCores	the maximum number of processor cores used under Unix.
PlotIt	whether to plot the fit directly (plot will be stored nevertheless).
KS	perform a Kolmogorow-Smirnow test of the fit versus original distribution.
Seed	optional seed parameter set internally.

Value

Returns a list of Gaussian modes.

Cls	the classes to which the cases are assigned according to the Gaussian mode membership.
Means	means of the Gaussian modes.
SDs	standard deviations of the Gaussian modes.
Weights	weights of the Gaussian modes.
Boundaries	Bayesian boundaries between the Gaussian modes.
Plot	Plot of the obtained mixture.
KS	Results of the Kolmogorov-Smirnov test.

Author(s)

Jorn Lotsch and Sebastian Malkusch

References

Lotsch J, Malkusch S, Ultsch A. Comparative assessment of automated algorithms for the separation of one-dimensional Gaussian mixtures. Informatics in Medicine Unlocked, Volume 34, 2022, https://doi.org/10.1016/j.imu.2022.101113. (https://www.sciencedirect.com/science/article/pii/S2352914822002507)

Examples

## example 1
data(iris)
opGMMassessment(Data = iris$Petal.Length,
  FitAlg = "normalmixEM", 
  Criterion = "BIC",
  PlotIt = TRUE,
  MaxModes = 5,
  MaxCores = 1,
  Seed = 42)

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