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.Kmeans' 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.plot' produces the density plot or contour plot of Manly mixture.
Function 'Manly.model' incorporates all Manly mixture related functionality.
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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