R: Produce Projection on 2D

Projection On 2D {EMCluster} | R Documentation

## 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 * 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 * 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.

### Details

This function produces projection outputs of `x` and `emobj`.

### Value

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.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com 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)

[Package EMCluster version 0.2-13 Index]