mixLogconc {MixSemiRob}

Clustering with Mixtures of Log-concave Distributions using EM Algorithm (Univariate)

Description

‘mixLogconc’ is used to estimate the parameters of a mixture of univariate log-concave distributions.

Usage

mixLogconc(x, C = 2, ini = NULL, nstart = 20, tol = 1e-05)

Arguments

x

an n by 1 data matrix where n is the number of observations.

C

number of mixture components. Default is 2.

ini

initial value for the EM algorithm. Default value is NULL, which obtains the initial value using the EMnormal function. It can be a list with the form of list(pi, mu, sigma), where pi is a 1 by C matrix of mixing proportions, mu is a C by 1 matrix of component means, and sigma is a p by p by 1 array of standard deviations or covariance matrices of C mixture components.

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.

f

component densities at x.

References

Chang, G. T., and Walther, G. (2007). Clustering with mixtures of log-concave distributions. Computational Statistics & Data Analysis, 51(12), 6242-6251.

Hu, H., Wu, Y., and Yao, W. (2016). Maximum likelihood estimation of the mixture of log-concave densities. Computational Statistics & Data Analysis, 101, 137-147.

See Also

EMnormal, mixLogconcHD

Examples

set.seed(4)
x = matrix(rnorm(100, 2, sqrt(2)), nrow = 100)
x[1:60] = x[1:60] + 5
EMlogc = mixLogconc(x, C = 2)