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