pdplot {MixSim} | R Documentation |
Constructs a parallel distribution plot for a Gaussian finite mixture model
pdplot(Pi, Mu, S, file = NULL, Nx = 5, Ny = 5, MaxInt = 1, marg = c(2,1,1,1))
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 |
If 'file' is specified, produced plot will be saved as a .pdf-file
Melnykov, V., Chen, W.-C., Maitra, R.
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.
MixSim, overlap, simdataset
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)