EMnormal: Parameter Estimation of Normal Mixture Using EM Algorithm

In MixSemiRob: Mixture Models: Parametric, Semiparametric, and Robust

Parameter Estimation of Normal Mixture Using EM Algorithm

Description

‘EMnormal’ is used to estimate the parameters of a univariate or multivariate normal mixture model using the expectation-maximization (EM) algorithm. The result can be used as the initial value for the mixLogconc and mixLogconcHD function.

Usage

EMnormal(x, C = 2, nstart = 20, tol = 1e-05)

Arguments

x	an n by p data matrix where n is the number of observations and p is the dimension of the data.
C	number of mixture components. Default is 2.
nstart	number of initializations to try. Default is 20.
tol	stopping criteria (threshold value) for the EM algorithm. Default is 1e-05.

Value

A list containing the following elements:

loglik	final log-likelihood.
pi	estimated mixing proportions.
mu	estimated component means.
sigma	estimated component standard deviation or covariance matrix.

See Also

mixLogconc, mixLogconcHD

Examples

#-----------------------------------------------------------------------------------------#
# Univariate Case
#-----------------------------------------------------------------------------------------#
x = matrix(rnorm(100, 2, sqrt(2)), nrow = 100)
x[1:60] = x[1:60] + 5
ini = EMnormal(x)

MixSemiRob documentation built on Sept. 20, 2023, 9:06 a.m.