mixLogconcHD {MixSemiRob} | R Documentation |
Clustering with Mixtures of Log-concave Distributions using EM Algorithm (Multivariate)
Description
‘mixLogconcHD’ is used to estimate the parameters of a mixture of multivariate log-concave distributions. The correlation structure among components is calculated by the normal copula.
Usage
mixLogconcHD(x, C, ini = NULL, nstart = 20, tol = 1e-05, maxiter = 100)
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. |
ini |
initial value for the EM algorithm. Default value is NULL, which
obtains the initial value using the |
nstart |
number of initializations to try. Default is 20. |
tol |
stopping criteria (threshold value) for the EM algorithm. Default is 1e-05. |
maxiter |
maximum number of iterations for the EM algorithm. Default is 100. |
Value
A list containing the following elements:
loglik |
final log-likelihood. |
pi |
estimated mixing proportions. |
f |
component densities at x. |
sigma |
estimated standard deviation or covariance matrix. |
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
Examples
x = mvtnorm::rmvnorm(100, c(0, 0), matrix(c(2, 1, 1, 2), nrow = 2))
x = matrix(x, nrow = 100)
x[1:60, ] = x[1:60, ] + 5
EMlogc = mixLogconcHD(x, C = 2)