00_MixSim-package: Simulation of Gaussian Finite Mixture Models

In snoweye/MixSim: Simulating Data to Study Performance of Clustering Algorithms

Simulation of Gaussian Finite Mixture Models

Description

Simulation of Gaussian finite mixture models for prespecified levels of average or/and maximum overlap. Pairwise overlap is defined as the sum of two misclassification probabilities.

Details

Function 'MixSim' simulates a finite mixture model for a prespecified level of average or/and maximum overlap.

Function 'overlap' computes all misclassification probabilities for a finite mixture model.

Function 'pdplot' constructs a parallel distribution plot for a finite mixture model.

Function 'simdataset' simulates a dataset from a finite mixture model.

Author(s)

Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.

Maintainer: Volodymyr Melnykov <[e-mail]>

References

Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.

Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.

Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.

Meila, M. (2006) “Comparing clusterings - an information based distance”, Journal of Multivariate Analysis, 98, 873-895.

Examples

# Simulate parameters of a mixture model
A <- MixSim(BarOmega = 0.01, MaxOmega = 0.10, K = 10, p = 5)

# Display the mixture via the parallel distribution plot
pdplot(A$Pi, A$Mu, A$S, MaxInt = 0.5)

snoweye/MixSim documentation built on Sept. 12, 2023, 4:58 a.m.