Finite mixture modeling and model-based clustering based on Manly mixture models.
The utility of this package includes finite mixture modeling and model-based clustering based on Manly mixtures as well as forward and backward model selection procedures.
|License:||GPL (>= 2)|
Function 'Manly.sim' simulates Manly mixture datasets.
Function 'Manly.overlap' estimates the pairwise overlaps for a Manly mixture.
Function 'Manly.EM' runs the EM algorithm for Manly mixture models.
Function 'Manly.select' runs forward and backward model selection procedures.
Function 'Manly.CEM' runs k-means model with Manly transformation.
Function 'Manly.var' produces the variance-covariance matrix of the parameter estimates from Manly mixture model.
Function 'Manly.contour' produces the contour plot of Manly mixture.
Xuwen Zhu and Volodymyr Melnykov.
Maintainer: Xuwen Zhu <email@example.com>
Zhu, X. and Melnykov, V. (2016) “Manly Transformation in Finite Mixture Modeling”, Journal of Computational Statistics and Data Analysis, doi:10.1016/j.csda.2016.01.015.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, 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.
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set.seed(123) K <- 3; p <- 4 X <- as.matrix(iris[,-5]) id.true <- rep(1:K, each = 50) # Obtain initial memberships based on the K-means algorithm id.km <- kmeans(X, K)$cluster # Run the CEM algorithm for Manly K-means model la <- matrix(0.1, K, p) C <- Manly.Kmeans(X, id = id.km, la = la) # Run the EM algorithm for a Gaussian mixture model based on K-means solution G <- Manly.EM(X, id = id.km) id.G <- G$id # Run FORWARD SELECTION ('silent' is on) F <- Manly.select(X, model = G, method = "forward", silent = TRUE) # Run the EM algorithm for a full Manly mixture model based on Gaussian mixture solution la <- matrix(0.1, K, p) M <- Manly.EM(X, id = id.G, la = la) # Run BACKWARD SELECTION ('silent' is off) B <- Manly.select(X, model = M, method = "backward") BICs <- c(G$bic, M$bic, F$bic, B$bic) names(BICs) <- c("Gaussian", "Manly", "Forward", "Backward") BICs
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