pdplot | R Documentation |

Constructs a parallel distribution plot for Gaussian finite mixture models.

pdplot(Pi, Mu, S, file = NULL, Nx = 5, Ny = 5, MaxInt = 1, marg = c(2,1,1,1))

`Pi` |
vector of mixing proportions. |

`Mu` |
matrix consisting of components' mean vectors (K * p). |

`S` |
set of components' covariance matrices (p * p * K). |

`file` |
name of .pdf-file. |

`Nx` |
number of color levels for smoothing along the x-axis. |

`Ny` |
number of color levels for smoothing along the y-axis. |

`MaxInt` |
maximum color intensity. |

`marg` |
plot margins. |

If 'file' is specified, produced plot will be saved as a .pdf-file.

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

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.

`MixSim`

, `overlap`

, and `simdataset`

.

data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) pdplot(Pi = Pi, Mu = Mu, S = S)

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