pdplot: Parallel Distribution Plot

In snoweye/MixSim: Simulating Data to Study Performance of Clustering Algorithms

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.

Details

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

Author(s)

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

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)

snoweye/MixSim documentation built on Sept. 12, 2023, 4:58 a.m.