| Type: | Package |
| Title: | Optimized Automated Gaussian Mixture Assessment |
| Version: | 0.4 |
| Author: | Jorn Lotsch [aut,cre] (<https://orcid.org/0000-0002-5818-6958>), Sebastian Malkusch [aut] (<https://orcid.org/0000-0001-6766-140X>), Martin Maechler [ctb], Peter Rousseeuw [ctb], Anja Struyf [ctb], Mia Hubert [ctb], Kurt Hornik [ctb] |
| Maintainer: | Jorn Lotsch <j.lotsch@em.uni-frankfurt.de> |
| Description: | Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>. |
| Depends: | R (≥ 3.5.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | AdaptGauss, DataVisualizations, DistributionOptimization, cluster, mixtools, grDevices, methods, foreach, stats, utils, rlang, ggplot2, parallel, caTools, dplyr, mclust, mixAK, multimode, NbClust, ClusterR, doParallel |
| NeedsCompilation: | no |
| Packaged: | 2024-04-14 16:45:18 UTC; joern |
| Repository: | CRAN |
| Date/Publication: | 2024-04-14 17:10:02 UTC |
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$[Cls, Time, Lipids]
Examples
data(Chromatogram)
str(Chromatogram)
Plot of Gaussian mixtures
Description
The function plots the components of a Gaussian mixture and superimposes them on a histogram of the data.
Usage
GMMplotGG(Data, Means, SDs, Weights, BayesBoundaries,
SingleGausses = TRUE, Hist = FALSE, Bounds = TRUE, SumModes = TRUE, PDE = TRUE)
Arguments
Data |
the data as a vector. |
Means |
a list of mean values for a Gaussian mixture. |
SDs |
a list of standard deviations for a Gaussian mixture. |
Weights |
a list of weights for a Gaussian mixture. |
BayesBoundaries |
a list of Bayesian boundaries for a Gaussian mixture. |
SingleGausses |
whether to plot the single Gaussian components as separate lines. |
Hist |
whether to plot a histgram of the original data. |
Bounds |
whether to plot the Bayesian boundaries for a Gaussian mixture as vertical lines. |
SumModes |
whether to plot the summed-up mixes. |
PDE |
whether to use the Pareto density estimation instead of the standard R density function. |
Value
Returns a ggplot2 object.
p1 |
the plot of Gaussian mixtures. |
Author(s)
Jorn Lotsch and Sebastian Malkusch
References
Lotsch, J., Malkusch S. (2021): opGMMassessment – an R Package for automated Guassian mixture modeling.
Examples
## example 1
data(iris)
Means0 <- tapply(X = as.vector(iris[,3]), INDEX = as.integer(iris$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)
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)
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)