| Projection On 2D {EMCluster} | R Documentation |
The function projects multivariate data on 2D plane which can be displayed
by plotppcontour() later.
project.on.2d(x, emobj = NULL, pi = NULL, Mu = NULL,
LTSigma = NULL, class = NULL, method = c("PP", "SVD"))
x |
the data matrix, dimension n * p. |
emobj |
the desired model which is a list mainly contains |
pi |
the mixing proportion, length K. |
Mu |
the centers of clusters, dimension K * p. |
LTSigma |
the lower triangular matrices of dispersion, K * p(p+1)/2. |
class |
id of classifications, length n. |
method |
either projection pursuit or singular value decomposition. |
This function produces projection outputs of x and emobj.
A projection is returned which is a list contains
da is a n * 2 projected matrix of x.
Pi is the original proportion emobj$pi of
length K.
Mu is a K * 2 projected matrix of
emboj$Mu.
S is a 2 * 2 * K projected array of
emboj$LTSigma.
class is the original class id emobj$class.
proj.mat is the projection matrix of dimension
p * 2.
Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra.
https://www.stat.iastate.edu/people/ranjan-maitra/
## Not run:
library(EMCluster, quietly = TRUE)
set.seed(1234)
### Iris.
x <- as.matrix(iris[, 1:4])
ret <- init.EM(x, nclass = 3, min.n = 30)
ret.proj <- project.on.2d(x, ret)
### Plot.
pdf("iris_ppcontour.pdf", height = 5, width = 5)
plotppcontour(ret.proj$da, ret.proj$Pi, ret.proj$Mu, ret.proj$S,
ret.proj$class, main = "Iris K = 3")
dev.off()
## End(Not run)