Description Usage Arguments Details Author(s) References See Also Examples

Constructs a parallel distribution plot for Gaussian finite mixture models.

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`

.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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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