MixSim (version 0.1-01)

pdplot: Parallel Distribution Plot

Description

Constructs a parallel distribution plot for a Gaussian finite mixture model

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 x p)

S

set of components' covariance matrices (p x p x K)

file

name of .pdf-file

Nx

number of color levels for smoothing along x-axis

Ny

number of color levels for smoothing along 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. (200?) "Simulating data to study performance of finite mixture modeling and clustering algorithms", The Journal of Computational and Graphical Statistics.

Davies, R. (1980) "The distribution of a linear combination of chi-square random variables", Applied Statistics, 29, 323-333.

See Also

MixSim, overlap, simdataset

Examples

data(iris)

K <- 3
p <- dim(iris)[2] - 1
n <- dim(iris)[1]
id <- as.numeric(iris[,5])
Pi <- NULL
Mu <- NULL
S <- array(rep(0, p * p * K), c(p, p, K))

# estimate mixture parameters
for (k in 1:K){
	Pi <- c(Pi, sum(id == k) / n)
	Mu <- rbind(Mu, apply(iris[id == k,-5], 2, mean))
	S[,,k] <- var(iris[id == k,-5])
}

pdplot(Pi = Pi, Mu = Mu, S = S)