EMMIX: EM Algorithm for Univariate or Multivariate Finite Mixture...

In rebmix: Finite Mixture Modeling, Clustering & Classification

EM Algorithm for Univariate or Multivariate Finite Mixture Estimation

Description

Returns as default the EM algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac or von Mises component densities. If model equals "REBMVNORM" output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.

Usage

## S4 method for signature 'REBMIX'
EMMIX(model = "REBMIX", Dataset = list(), 
       Theta = NULL, EMcontrol = NULL, ...)
## ... and for other signatures

Arguments

model	see Methods section below.
Dataset	a list of length n_{\mathrm{D}} of data frames of size n \times d containing d-dimensional datasets. Each of the d columns represents one random variable. Numbers of observations n equal the number of rows in the datasets.
Theta	an object of class EMMIX.Theta or EMMVNORM.Theta.
EMcontrol	an object of class EM.Control.
...	currently not used.

Value

Returns an object of class REBMIX or REBMVNORM.

Methods

signature(model = "REBMIX")

a character giving the default class name "REBMIX" for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac or von Mises component densities.

signature(model = "REBMVNORM")

a character giving the class name "REBMVNORM" for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.

Author(s)

Branislav Panic

References

B. Panic, J. Klemenc, M. Nagode. Improved initialization of the EM algorithm for mixture model parameter estimation. Mathematics, 8(3):373, 2020. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/math8030373")}.

Examples

## Not run: 
devAskNewPage(ask = TRUE)

# Load faithful dataset.

data(faithful)

# Plot faithfull dataset.

plot(faithful)

# Number of dimensions.

d <- ncol(faithful)

# Obtain 2 component solution with Gaussian mixtures.

c <- 2

# Create EMMVNORM.Theta object with new call.

Theta <- new("EMMVNORM.Theta", d = d, c = c)

# Set parameters of Theta.
# Weights.

a.w(Theta) <- c(0.5, 0.5)

# Means.

a.theta1.all(Theta) <- c(2.0, 55.0, 4.5, 80.0)

# Covariances.

a.theta2.all(Theta) <- c(1, 0, 0, 1, 1, 0, 0, 1)

# Run EMMIX method.

model <- EMMIX(model = "REBMVNORM", Dataset = list(faithful), Theta = Theta)

# show.

model

# summary.

summary(model)

# plot.

plot(model, nrow = 3, ncol = 2, what = c("pdf", "marginal pdf", "marginal cdf"))

# Create EMMIX.Theta object with new call.

Theta <- new("EMMIX.Theta", c = c, pdf = c("normal", "normal"))

# Set parameters of Theta.
# Weights.

a.w(Theta) <- c(0.5, 0.5)

# Means.

a.theta1.all(Theta) <- c(2.0, 55.0, 4.5, 80.0)

# Covariances.

a.theta2.all(Theta) <- c(1, 1, 1, 1)

# Run EMMIX method.

model <- EMMIX(Dataset = list(faithful), Theta = Theta)

# show.

model

# summary.

summary(model)

# plot.

plot(model, nrow = 3, ncol = 2, what = c("pdf", "marginal pdf", "marginal cdf"))

## End(Not run)

rebmix documentation built on July 26, 2023, 5:32 p.m.