MixSim (version 1.0-3)

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 proprtions.

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.

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 Finite Mixture Modeling and Clustering Algorithms'', Journal of Statistical Software, (submitted).

See Also

MixSim, overlap, and simdataset.

Examples

data(iris)
p <- dim(iris)[2] - 1
K <- 3
id <- as.numeric(iris[, 5])

# estimate mixture parameters
Pi <- sapply(1:K, function(k){ sum(id == k) }) / dim(iris)[1]
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)