pdplot {MixSim}R Documentation

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

Author(s)

Melnykov, V., Chen, W.-C., Maitra, R.

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)

[Package MixSim version 0.1-04 Index]