a0_project.on.2d: Produce Projection on 2D

In EMCluster: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution

Produce Projection on 2D

Description

The function projects multivariate data on 2D plane which can be displayed by plotppcontour() later.

Usage

project.on.2d(x, emobj = NULL, pi = NULL, Mu = NULL,
    LTSigma = NULL, class = NULL, method = c("PP", "SVD"))

Arguments

x	the data matrix, dimension n\times p.
emobj	the desired model which is a list mainly contains pi, Mu, and LTSigma, usually a returned object from init.EM.
pi	the mixing proportion, length K.
Mu	the centers of clusters, dimension K\times p.
LTSigma	the lower triangular matrices of dispersion, K\times p(p+1)/2.
class	id of classifications, length n.
method	either projection pursuit or singular value decomposition.

Details

This function produces projection outputs of x and emobj.

Value

A projection is returned which is a list contains

• da is a n \times 2 projected matrix of x.

• Pi is the original proportion emobj$pi of length K.

• Mu is a K \times 2 projected matrix of emboj$Mu.

• S is a 2 \times 2 \times K projected array of emboj$LTSigma.

• class is the original class id emobj$class.

• proj.mat is the projection matrix of dimension p \times 2.

Author(s)

Wei-Chen Chen [e-mail] and Ranjan Maitra.

References

https://www.stat.iastate.edu/people/ranjan-maitra

See Also

project.on.2d().

Examples

## 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)

EMCluster documentation built on Sept. 11, 2024, 5:27 p.m.