MixSim (version 1.1-8)

pdplot: Parallel Distribution Plot

Description

Constructs a parallel distribution plot for Gaussian finite mixture models.

Usage

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

Arguments

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.

Author

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

Details

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

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.

See Also

MixSim, overlap, and simdataset.

Examples

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)