| Type: | Package |
| Title: | Gaussian Mixture Models (GMM) |
| Version: | 1.6 |
| Date: | 2024-02-02 |
| Maintainer: | Michael Thrun <m.thrun@gmx.net> |
| Description: | Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <doi:10.3390/ijms161025897>. |
| Imports: | Rcpp, shiny, pracma, methods, DataVisualizations, plotly |
| Suggests: | mclust, grid, foreach, dqrng, parallelDist, knitr (≥ 1.12), rmarkdown (≥ 0.9), reshape2, ggplot2 |
| LinkingTo: | Rcpp |
| Depends: | R (≥ 2.10) |
| License: | GPL-3 |
| LazyLoad: | yes |
| URL: | https://www.deepbionics.org |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| VignetteBuilder: | knitr |
| BugReports: | https://github.com/Mthrun/AdaptGauss/issues |
| Packaged: | 2024-02-02 15:13:20 UTC; mct_l |
| Author: | Michael Thrun 
[aut, cre],
Onno Hansen-Goos [aut, rev],
Rabea Griese [ctr, ctb],
Catharina Lippmann [ctr],
Florian Lerch [ctb, rev],
Quirin Stier [ctb, rev],
Jorn Lotsch [dtc, rev, fnd, ctb],
Luca Brinkmann [ctb, rev],
Alfred Ultsch [aut, cph, ths] |
| Repository: | CRAN |
| Date/Publication: | 2024-02-02 15:40:02 UTC |
Gaussian Mixture Models (GMM)
Description
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.
Details
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf [Ultsch 2005]. PDE has been designed in particular to identify groups/classes in a dataset. The expectation maximization algorithm estimates a Gaussian mixture model of density states [Bishop 2006] and the limits between the different states are defined by Bayes decision boundaries [Duda 2001]. The model can be verified with Chi-squared test, Kolmogorov-Smirnov test and QQ plot.
The correct number of modes may be found with AIC or BIC.
Index: This package was not yet installed at build time.
Author(s)
Michael Thrun, Onno Hansen-Goos, Rabea Griese, Catharina Lippmann, Florian Lerch, Jorn Lotsch, Alfred Ultsch Maintainer: Michael Thrun <m.thrun@gmx.net>
References
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.
Duda, R.O., P.E. Hart, and D.G. Stork, Pattern classification. 2nd. Edition. New York, 2001, p 512 ff
Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006, p 435 ff
Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, in Baier, D.; Werrnecke, K. D., (Eds), Innovations in classification, data science, and information systems, Proc Gfkl 2003, pp 91-100, Springer, Berlin, 2005.
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.
Examples
## Statistically significant GMM
## Not run:
data=c(rnorm(3000,2,1),rnorm(3000,7,3),rnorm(3000,-2,0.5))
gmm=AdaptGauss::AdaptGauss(data,
Means = c(-2, 2, 7),
SDs = c(0.5, 1, 4),
Weights = c(0.3333, 0.3333, 0.3333))
AdaptGauss::Chi2testMixtures(data,
gmm$Means,gmm$SDs,gmm$Weights,PlotIt=T)
AdaptGauss::QQplotGMM(data,gmm$Means,gmm$SDs,gmm$Weights)
## End(Not run)
## Statistically non significant GMM
## Not run:
data('LKWFahrzeitSeehafen2010')
gmm=AdaptGauss::AdaptGauss(LKWFahrzeitSeehafen2010,
Means = c(52.74, 385.38, 619.46, 162.08),
SDs = c(38.22, 93.21, 57.72, 48.36),
Weights = c(0.2434, 0.5589, 0.1484, 0.0749))
AdaptGauss::Chi2testMixtures(LKWFahrzeitSeehafen2010,
gmm$Means,gmm$SDs,gmm$Weights,PlotIt=T)
AdaptGauss::QQplotGMM(LKWFahrzeitSeehafen2010,gmm$Means,gmm$SDs,gmm$Weights)
## End(Not run)
Adapt Gaussian Mixture Model (GMM)
Description
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
Usage
AdaptGauss(Data, Means = NaN, SDs = NaN, Weights = NaN,
ParetoRadius = NaN, LB = NaN, HB = NaN,
ListOfAdaptGauss, fast = T)
Arguments
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 |
Details
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.
Value
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 |
Author(s)
Onno Hansen-Goos, Michael Thrun
References
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.
Examples
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)
Posterioris of Bayes Theorem
Description
Calculates the posterioris of Bayes theorem
Usage
Bayes4Mixtures(Data, Means, SDs, Weights, IsLogDistribution,
PlotIt, CorrectBorders,Color,xlab,lwd)
Arguments
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 L |
PlotIt |
Optional, Default: FALSE; TRUE do a Plot |
CorrectBorders |
Optional, ==TRUE data at right borders of GMM distribution will be assigned to last gaussian, left border vice versa. (default ==FALSE) normal Bayes Theorem |
Color |
Optional, character vector of colors, default rainbow() |
xlab |
Optional, label of x-axis, default 'Data', see intern R documentation |
lwd |
Width of Line, see intern R documentation |
Details
See conference presentation for further explanation.
Value
List with
Posteriors |
(1:N,1:L) of Posteriors corresponding to Data |
NormalizationFactor |
(1:N) denominator of Bayes theorem corresponding to Data |
Author(s)
Catharina Lippmann, Onno Hansen-Goos, Michael Thrun
References
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.
See Also
BayesDecisionBoundaries,AdaptGauss
BayesClassification
Description
Bayes Klassifikation den Daten zuordnen
Usage
BayesClassification(Data, Means, SDs, Weights, IsLogDistribution = Means
* 0, ClassLabels = c(1:length(Means)))
Arguments
Data |
vector of Data |
Means |
vector[1:L] of Means of Gaussians (of GMM) |
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 |
ClassLabels |
Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model |
Value
Cls(1:n,1:d) classiffication of Data, such that 1= first component of gaussian mixture model, 2= second component of gaussian mixture model and so on. For Every datapoint a number is returned.
Author(s)
Michael Thrun
Decision Boundaries calculated through Bayes Theorem
Description
Function finds the intersections of Gaussians or LogNormals
Usage
BayesDecisionBoundaries(Means,SDs,Weights,IsLogDistribution,MinData,MaxData,Ycoor)
Arguments
Means |
vector[1:L] of Means of Gaussians (of GMM) |
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 |
MinData |
Optional, Beginning of range, where the Boundaries are searched for, default min(M) |
MaxData |
Optional, End of range, where the Boundaries are searched for, default max(M) |
Ycoor |
Optional, Bool, if TRUE instead of vector of DecisionBoundaries list of DecisionBoundaries and DBY is returned |
Value
DecisionBoundaries |
vector[1:L-1], Bayes decision boundaries |
DBY |
if (Ycoor==TRUE), y values at the cross points of the Gaussians is also returned, that the return is a list of DecisionBoundaries and DBY |
Author(s)
Michael Thrun, Rabea Griese
References
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. 2nd. Edition. New York, p. 512ff
See Also
AdaptGauss,Intersect2Mixtures,Bayes4Mixtures
Posterioris of Bayes Theorem for a two group GMM
Description
Calculates the posterioris of Bayes theorem, splits the GMM in two groups beforehand.
Usage
BayesFor2GMM(Data, Means, SDs, Weights, IsLogDistribution = Means * 0,
Ind1 = c(1:floor(length(Means)/2)), Ind2 = c((floor(length(Means)/2)
+ 1):length(Means)), PlotIt = 0, CorrectBorders = 0)
Arguments
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 L |
Ind1 |
indices from (1:C) such that [M(Ind1),S(Ind1) ,W(Ind1) ]is one mixture, [M(Ind2),S(Ind2) ,W(Ind2) ] the second mixture default Ind1= 1:C/2, Ind2= C/2+1:C |
Ind2 |
indices from (1:C) such that [M(Ind1),S(Ind1) ,W(Ind1) ]is one mixture, [M(Ind2),S(Ind2) ,W(Ind2) ] the second mixture default Ind1= 1:C/2, Ind2= C/2+1:C |
PlotIt |
Optional, Default: FALSE; TRUE do a Plot |
CorrectBorders |
Optional, ==TRUE data at right borders of GMM distribution will be assigned to last gaussian, left border vice versa. (default ==FALSE) normal Bayes Theorem |
Details
See conference presentation for further explanation.
Value
List With
- Posteriors:
(1:N,1:L) of Posteriors corresponding to Data
- NormalizationFactor:
(1:N) denominator of Bayes theorem corresponding to Data
Author(s)
Alfred Ultsch, Michael Thrun
References
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.
See Also
BayesDecisionBoundaries,AdaptGauss
cumulative distribution of mixture model
Description
returns the cdf (cumulative distribution function) of a mixture model of gaussian or log gaussians
Usage
CDFMixtures(Kernels,Means,SDs,Weights,IsLogDistribution)
Arguments
Kernels |
at these locations N(Means,Sdevs)*Weights is used for cdf calcuation, NOTE: Kernels are usually (but not necessarily) sorted and unique |
Means |
vector(1:L), Means of Gaussians, L == Number of Gaussians |
SDs |
estimated Gaussian Kernels = standard deviations |
Weights |
optional, relative number of points in Gaussians (prior probabilities): sum(Weights) ==1, default weight is 1/L |
IsLogDistribution |
Optional, if IsLogDistribution(i)==1, then mixture is lognormal default == 0*(1:L) |
Value
List with
CDFGaussMixture |
(1:N,1), cdf of Sum of SingleGaussians at Kernels |
CDFSingleGaussian |
(1:N,1:L) ,cdf of mixtures at Kernels |
Author(s)
Rabea Griese
See Also
Pearson's chi-squared goodness of fit test
Description
Chi2testMixtures is goodness of fit test which establishes whether an observed distribution (data) differs from a Gauss Mixture Model (GMM). Returns a P value of a special case of a chi-square test and visualizes data versus a given GMM.
Usage
Chi2testMixtures(Data,Means,SDs,Weights,
IsLogDistribution,PlotIt,UpperLimit,VarName,NoRepetitions)
Arguments
Data |
vector of data points (1:n) |
Means |
vector of Means of Gaussians (1:c) |
SDs |
vector of standard deviations, estimated Gaussian Kernels (1:c) |
Weights |
vector of relative number of points in Gaussians (prior probabilities) (1:c) |
IsLogDistribution |
Optional, if IsLogDistribution(i)==1, then mixture is lognormal, default vector of zeros of length 1:L |
PlotIt |
Optional, Default: FALSE, do a Plot of the compared cdfs and the KS-test distribution (Diff) |
UpperLimit |
Optional. test only for Data <= UpperLimit, Default = max(Data) i.e all Data. |
VarName |
If PlotIt=TRUE, the name of the inspected variable, default 'Data' |
NoRepetitions |
Optional, scalar, default =1000, Number of Repetitions for monte carlo sampling |
Details
The null hypothesis is that the estimated data distribution does not differ significantly from the GMM. Let O_i be the observed features and E_i be the expected number E, than the test statistic is defined with the minimum chi-square estimate T=sum((O_i-E_i)^2/E_i)*1/m, where m the number of data points. The expected number Ei may be derived for each bin. If there is a significant difference between the O_i and the E_i, the Pvalue is small and the null hypothesis can be rejected.
Further details, see [Thrun & Ultsch, 2015].
Value
List with
Pvalue |
Pvalue of a suiting chi-square , Pvalue ==0 if Pvalue <0.001 |
BinCenters |
bin centers |
ObsNrInBin |
No. of data in bin |
ExpectedNrInBin |
No. of data that should be in bin according to GMM |
Chi2Value |
the TestStatistic T i.e.: sum((ObsNrInBin(Ind)-ExpectedNrInBin(Ind))^2/ExpectedNrInBin(Ind)) with
Ind = find(ExpectedNrInBin>=10)
The value of |
Note
The statistic assumption is that the the test statistic follows a chi square distribution. The number of degrees of freedom is equal to the number of datapoints n-1-3*c
Author(s)
Rabea Griese, Michael Thrun
References
Hartung, J., Elpelt, B., and Kloesener, K.H.: Statistik, 8. Aufl. Verlag Oldenburg (1991).
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, pp. 28-29, Colchester 2015.
Classify Data according to decision Boundaries
Description
The Decision Boundaries calculated through Bayes Theorem.
Usage
ClassifyByDecisionBoundaries(Data,DecisionBoundaries,ClassLabels)
Arguments
Data |
vector of Data |
DecisionBoundaries |
decision boundaries, |
ClassLabels |
Optional numbered class labels that are assigned to the classes. default (1:L), L number of different components of gaussian mixture model |
Value
Cls(1:n,1:d) classiffication of Data, such that 1= first component of gaussian mixture model, 2= second component of gaussian mixture model and so on. For Every datapoint a number is returned.
Author(s)
Michael Thrun
References
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. 2nd. Edition. New York, p. 512ff
See Also
BayesDecisionBoundaries, Bayes4Mixtures
EM Algorithm for GMM
Description
Expectation-Maximization algorithm to calculate optimal Gaussian Mixture Model for given data in one Dimension.
Usage
EMGauss(Data, K, Means, SDs,Weights, MaxNumberofIterations,fast)
Arguments
Data |
vector of data points |
K |
estimated amount of Gaussian Kernels |
Means |
vector(1:L), Means of Gaussians, L == Number of Gaussians |
SDs |
estimated Gaussian Kernels = standard deviations |
Weights |
optional, relative number of points in Gaussians (prior probabilities): sum(Weights) ==1, default weight is 1/L |
MaxNumberofIterations |
Optional, Number of Iterations; default=10 |
fast |
Default: FALSE: Using mclust's EM see function |
Details
No adding or removing of Gaussian kernels. Number of Gaussian hast to be set by the length of the vector of Means, SDs and Weights.
This EM is only for univariate data. For multivariate data see package mclust
Value
List with
Means |
means of GMM generated by EM algorithm |
SDs |
standard deviations of GMM generated by EM algorithm |
Weights |
prior probabilities of Gaussians |
Author(s)
Onno Hansen-Goos, Michael Thrun, Florian Lerch
References
Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006, p 435 ff
See Also
Plots the Gaussian Mixture Model (GMM) withing ggplot2
Description
PlotMixtures and PlotMixturesAndBoundaries for ggplot2
Usage
GMMplot_ggplot2(Data, Means, SDs, Weights,
BayesBoundaries, SingleGausses = TRUE, Hist = FALSE)
Arguments
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 |
BayesBoundaries |
Optional, x values for baye boundaries, if missing 'BayesDecisionBoundaries' is called |
SingleGausses |
Optional, SingleGausses=T than components of the mixture in blue will be shown. |
Hist |
Optional, geom_histogram overlayed |
Value
ggplot2 object
Note
MT standardized code for CRAN and added dec boundaries and doku
Author(s)
Joern Loetsch, Michael Thrun (ctb)
See Also
PlotMixturesAndBoundaries, PlotMixtures, BayesDecisionBoundaries
Examples
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
GMMplot_ggplot2(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5),SingleGausses=TRUE)
Information Criteria For GMM
Description
Calculates the AIC and BIC criteria
Usage
InformationCriteria4GMM(Data, Means, SDs, Weights, IsLogDistribution)
Arguments
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 L, LogNormal Modes are at this point only experimental |
Details
AIC = 2*k -2*LogLikelihood, k = nr. of model parameter = 3*Nr. of Gaussians One Gaussian: K=2 (Weight is then not an parameter!) SMALL SAMPLE CORRECTION: for n= nr of Data and n < 40 * k, AIC is adjusted to AIC=AIC+ (2*k*(k+1))/(n-k-1)
BIC = k* log(n) - 2*LogLikelihood
Only for a Gaussian Mixture Model (GMM) verified, for the Log Gaussian, Gaussian, Log Gaussian (LGL) Model only experimental
Value
List with
K |
Number of gaussian mixtures |
AIC |
Akaike Informations criterium |
BIC |
Bayes Information criterium |
LogLikelihood |
LogLikelihood of GMM, see |
PDFmixture |
probability density function of GMM, see |
LogPDFdata |
log(PDFmixture) |
Author(s)
Michael Thrun
References
Aubert, A. H., Thrun, M. C., Breuer, L., & Ultsch, A.: Knowledge discovery from data structure: hydrology versus biology controlled in-stream nitrate concentration, Scientific reports, Vol. (in revision), pp., 2016.
Aho, K., Derryberry, D., & Peterson, T.: Model selection for ecologists: the worldviews of AIC and BIC. Ecology, 95(3), pp. 631-636, 2014.
Intersect of two Gaussians
Description
Finds the intersect of two gaussians or log gaussians
Usage
Intersect2Mixtures(Mean1,SD1,Weight1,Mean2,SD2,Weight2,IsLogDistribution,MinData,MaxData)
Arguments
Mean1 |
mean of 1.gaussian |
SD1 |
standard deviations of 1.gaussian |
Weight1 |
weight of 1. guassian |
Mean2 |
mean of 2.gaussian |
SD2 |
standard deviations of 2.gaussian |
Weight2 |
weight of 2. guassian |
IsLogDistribution |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 2 |
MinData |
Optional, Beginning of range, where the intersect is searched for, default min(Mean1,Mean2) |
MaxData |
Optional, End of range, where the intersect is searched for, default max(Mean1,Mean2) |
Value
CutX |
x value, where gaussian 1=gaussian2 |
CutY |
y value, where gaussian 1=gaussian2 |
Author(s)
Michael Thrun, Rabea Griese
See Also
Kolmogorov-Smirnov test
Description
Returns a P value and visualizes for Kolmogorov-Smirnov test of Data versus a given Gauss Mixture Model
Usage
KStestMixtures(Data,Means,SDs,Weights,IsLogDistribution,
PlotIt,UpperLimit,NoRepetitions,Silent)
Arguments
Data |
vector of data points |
Means |
vector of Means of Gaussians |
SDs |
vector of standard deviations, estimated Gaussian Kernels |
Weights |
vector of relative number of points in Gaussians (prior probabilities) |
IsLogDistribution |
Optional, if IsLogDistribution(i)==1, then mixture is lognormal, default vector of zeros of length 1:L |
PlotIt |
Optional, Default: FALSE, do a Plot of the compared cdfs and the KS-test distribution (Diff) |
UpperLimit |
Optional. test only for Data <= UpperLimit, Default = max(Data) i.e all Data. |
NoRepetitions |
Optional, default =1000, scalar, Number of Repetitions for monte carlo sampling |
Silent |
Optional, default=TRUE, If FALSE, shows progress of computation by points (On windows systems a progress bar) |
Details
The null hypothesis is that the estimated data distribution does not differ significantly from the GMM. If there is a significant difference, then the Pvalue is small and the null hypothesis is rejected.
Value
List with
Pvalue |
Pvalue of a suiting Kolmogorov-Smirnov test, Pvalue ==0 if Pvalue <0.001 |
DataKernels |
such that plot(DataKernels,DataCDF) gives the cdf(Data) |
DataCDF |
such that plot(DataKernels,DataCDF) gives the cdf(Data) |
CDFGaussMixture |
No. of data that should be in bin according to GMM |
Author(s)
Michael Thrun, Alfred Ultsch
References
Smirnov, N., Table for Estimating the Goodness of Fit of Empirical Distributions. 1948, (2), 279-281.
Truck driving time seaport 2010
Description
Truck driving time to seaports measured in 2010.
Usage
data("LKWFahrzeitSeehafen2010")Format
The format is: num [1:11441] 84.7 13.2 11.5 41.4 52.9 ...
References
Behnisch, M., Ultsch, A.: Knowledge Discovery in Spatial Planning Data - A Concept for Cluster Understanding, in: Helbich, M., Arsanjani, J. J., Leitner, M. (eds.): Computational Approaches for Urban Environments, in: Gatrell, J.D., Jensen, R.R.: Geotechnologies and the Environment Series, Vol, 13, Springer, Berlin, pp. 49-75, 2015.
Examples
data(LKWFahrzeitSeehafen2010)
## maybe str(LKWFahrzeitSeehafen2010) ; plot(LKWFahrzeitSeehafen2010) ...
Likelihood Ratio for Gaussian Mixtures
Description
Computes the likelihood ratio for two Gaussian Mixture Models.
Usage
LikelihoodRatio4Mixtures(Data,NullMixture,OneMixture,PlotIt,LowerLimit,UpperLimit)
Arguments
Data |
Data points. |
NullMixture |
A Matrix: cbind(Means0,SDs0,Weights0) or cbind(Means0,SDs0,Weights0,IsLog0). The null model; usually with less Gaussians than the OneMixture |
OneMixture |
A Matrix: cbind(Means1,SDs1,Weights1) or cbind(Means1,SDs1,Weights1,IsLog1). The alternative model usually with more Gaussians than the OneMixture. |
PlotIt |
Optional: Boolean, if TRUE a Plot of the compared cdf's and the KS-test distribution (Diff) is shown |
LowerLimit |
Optional: test only for Data >= LowerLimit, Default = min(Data) i.e all Data. |
UpperLimit |
Optional: test only for Data <= UpperLimit, Default = max(Data) i.e all Data. |
Value
List with
Pvalue |
the error that we make, if we accept OneMixture as the better Model over the NullMixture |
NullLogLikelihood |
log likelihood of GMM Null |
OneLogLikelihood |
log likelihood of GMM One |
Author(s)
Alfred Ultsch, Michael Thrun, Catharina Lippmann
Examples
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
## Not run: Vals=AdaptGauss(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5),0.3,-6,6)
NullMixture=cbind(Vals$Means,Vals$SDs,Vals$Weights)
## End(Not run)
## Not run: Vals2=AdaptGauss(data,c(-1,0,2,3),c(2,1,1,1),c(0.25,0.25,0.25,0.25),0.3,-6,6)
OneMixture=cbind(Vals2$Means,Vals2$SDs,Vals2$Weights)
## End(Not run)
## Not run:
res=LikelihoodRatio4Mixtures(data,NullMixture,OneMixture,T)
## End(Not run)
LogLikelihood for Gaussian Mixture Models
Description
Computes the LogLikelihood for Gaussian Mixture Models.
Usage
LogLikelihood4Mixtures(Data, Means, SDs, Weights, IsLogDistribution)
Arguments
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) |
IsLogDistribution |
Optional, ==1 if distribution(i) is a LogNormal, default vector of zeros of length 1:L |
Value
List with
LogLikelihood |
LogLikelihood = = sum(log(PDFmixture) |
LogPDF |
=log(PDFmixture) |
PDFmixture |
die Probability density function for each point |
Author(s)
Alfred Ultsch, Catharina Lippmann
References
Pattern Recogintion and Machine Learning, C.M. Bishop, 2006, isbn: ISBN-13: 978-0387-31073-2, p. 433 (9.14)
Calculates pdf for GMM
Description
Calculate Gaussianthe probability density function for a Mixture Model
Usage
Pdf4Mixtures(Data, Means, SDs, Weights,IsLogDistribution,PlotIt)
Arguments
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 |
PlotIt |
Optional: =TRUE plot of pdf |
Value
List with
PDF4modes |
matrix, where the columns are the gaussians |
PDF |
matrix, where the columns are the gaussians weighted by Weights |
PDFmixture |
linear superpositions of PDF - prior probabilities of Gaussians |
Author(s)
Michael Thrun
See Also
Examples
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
Pdf4Mixtures(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5), PlotIt=TRUE)
Shows GMM
Description
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
Arguments
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 |
Plotter |
Optional, plotting package, either native or plotly |
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) |
Details
Example shows that overlapping variances of gaussians will result in inappropriate decision boundaries.
Author(s)
Michael Thrun, Quirin Stier
See Also
Examples
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
PlotMixtures(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5),SingleColor='blue',SingleGausses=TRUE)
Shows GMM with Boundaries
Description
Plots Gaussian Mixture Model with Bayes decision boundaries, such that:
Black is the PDE of Data
Red is color of the GMM
Magenta are the Bayes boundaries
Usage
PlotMixturesAndBoundaries(Data, Means, SDs, Weights,
IsLogDistribution = rep(FALSE, length(Means)), Plotter="native",
SingleColor = "blue", MixtureColor = "red", DataColor = "black",
BoundaryColor = "magenta", xlab, ylab,
SingleGausses =TRUE, ...)
Arguments
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 |
Plotter |
Optional, plotting package, either native or plotly |
SingleColor |
Optional, Color for line plot of all the single gaussians, default magenta |
MixtureColor |
Optional, Color of line plot for the mixture, default red |
DataColor |
Optional, Color of line plot for the data, default black |
BoundaryColor |
Optional, Color of bayesian boundaries |
xlab |
Optional, x label, see |
ylab |
Optional, y label, ee |
SingleGausses |
Optional, SingleGausses=T than components of the mixture in blue will be shown. |
... |
Optional, see |
Author(s)
Michael Thrun
See Also
BayesDecisionBoundaries,PlotMixtures
Quantile Quantile Plot of Data
Description
Quantile Quantile plot of data against gaussian distribution mixture model with optional best-fit-line
Usage
QQplotGMM(Data,Means,SDs,Weights,IsLogDistribution,Method,Line,
PlotSymbol,col,xug,xog,LineWidth,PointWidth,PositiveData,Type,NoQuantiles,
ylabel,main,lwd,pch,xlabel,...)
Arguments
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 Zeros of Length L |
Method |
Optional, ==1 default. ==2 for robust calculation |
Line |
Optional, Default: TRUE=Regression Line is drawn |
col |
Character: color of regression line (only for Method = 2) |
xug |
Optional, lower limit of the interval [xug, xog], in which a line will be interpolated |
xog |
Optional, upper limit of the interval [xug, xog], in which a line will be interpolated |
PlotSymbol |
Optional, plot symbol. Default is 20. |
LineWidth |
Optional, width of regression line, if Line==TRUE |
PointWidth |
Optional, width of points |
PositiveData |
Optional, Boolean: If true only positive values of GMM sampling are used. Default FALSE |
Type |
Optional,Integer: number of method used for computing the quantiles, see |
NoQuantiles |
Optional, Integer: Number of quantiles to compute (only for Method = 2) |
ylabel |
Optional, see |
main |
Optional, see |
lwd |
Optional, Integer: graphic parameter - line width option (only for Method = 2) |
pch |
Integer: graphic parameter for points (only for Method = 2) |
xlabel |
Optional, see |
... |
Note: xlab cannot be changed, other parameters see |
Details
Only verified for a Gaussian Mixture Model, usage of IsLogDistribution for LogNormal Modes is experimental!
Value
List with
x |
The x coordinates of the points that were plotted |
y |
The original data vector, i.e., the corresponding y coordinates |
Author(s)
Michael Thrun
References
Michael, J. R. (1983). The stabilized probability plot. Biometrika, 70(1), 11-17.
See Also
Examples
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
QQplotGMM(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5))
Random Number Generator for Log or Gaussian Mixture Model
Description
Function finds the intersections of Gaussians or LogNormals
Usage
RandomLogGMM(Means,SDs,Weights,IsLogDistribution,TotalNoPoints)
Arguments
Means |
vector[1:L] of Means of Gaussians (of GMM) |
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 L |
TotalNoPoints |
Optional, number of point for log or GMM generated |
Value
Returns vector of [1:TotalNoPoints] of genrated points for log oder gaussian mixture model
Author(s)
Alfred Ultsch,Michael Thrun, Rabea Griese
See Also
computes a special case of log normal distribution density
Description
Symlognpdf is an internal function for AdaptLGL.
Usage
Symlognpdf(Data, Mean, SD)
Arguments
Data |
vector of data points used for sampling |
Mean |
Mean of log Gaussian |
SD |
Standard deviation of log Gaussian |
Value
M>0 Log normal distribution density
M<0 Log normal distribution density mirrored at y axis
Note
not for external usage.
See Also
AdaptLGL