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)