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Last data update: 2014.03.03

R: EM algorithm for Gaussian mixture models

EMAlgorithm

R Documentation

EM algorithm for Gaussian mixture models

Description

The regular expectation-maximization algorithm for general multivariate Gaussian mixture models.

Usage

EMAlgorithm(x, theta, eps = 1e-06, max.ite = 1e+05, trace.theta = FALSE,
  verbose = FALSE)

Arguments

`x`	A matrix of observations where rows correspond to features and columns to experiments.
`theta`	A list of parameters as described in `rtheta`.
`eps`	The maximal required difference in successive likelihoods to establish convergence.
`max.ite`	The maximum number of iterations.
`trace.theta`	Logical. If `TRUE`, all estimates are stored and returned. Default is `FALSE`.
`verbose`	Set to `TRUE` for verbose output. Default is `FALSE`.

Details

Though not as versatile, the algorithm can be a faster alternative to Mclust in the mclust-package.

Value

A list of length 3 with elements:

`theta`	A list of the estimated parameters as described in `rtheta`.
`loglik.tr`	A numeric vector of the log-likelihood trace.
`kappa`	A matrix where `kappa[i,j]` is the probability that `x[i, ]` is realized from the `j`'th component.

Author(s)

Anders Ellern Bilgrau <anders.ellern.bilgrau@gmail.com>

Examples

set.seed(10)
data <- SimulateGMCMData(n = 1000, d = 2, m = 3)
start.theta <- rtheta(d = 2, m = 3)
res <- GMCM:::EMAlgorithm(data$z, theta = start.theta)

par(mfrow = c(1,2))
plot(data$z, cex = 0.5, pch = 16, main = "Simulated data",
     col = rainbow(3)[data$K])
plot(data$z, cex = 0.5, pch = 16, main = "GMM clustering",
     col = rainbow(3)[apply(res$kappa,1,which.max)])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GMCM)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GMCM/EMAlgorithm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: EMAlgorithm
> ### Title: EM algorithm for Gaussian mixture models
> ### Aliases: EMAlgorithm
> 
> ### ** Examples
> 
> set.seed(10)
> data <- SimulateGMCMData(n = 1000, d = 2, m = 3)
> start.theta <- rtheta(d = 2, m = 3)
> res <- GMCM:::EMAlgorithm(data$z, theta = start.theta)
> 
> par(mfrow = c(1,2))
> plot(data$z, cex = 0.5, pch = 16, main = "Simulated data",
+      col = rainbow(3)[data$K])
> plot(data$z, cex = 0.5, pch = 16, main = "GMM clustering",
+      col = rainbow(3)[apply(res$kappa,1,which.max)])
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>