EMCluster (version 0.2-16)

Likelihood Mixture Tests: Likelihood Mixture Tests

Description

This function test two mixture Gaussian models with unstructured covariance matrix and different numbers of clusters.

Usage

lmt(emobj.0, emobj.a, x, tau = 0.5, n.mc.E.delta = 1000,
    n.mc.E.chi2, verbose = FALSE)

Value

A list of class lmt are returned.

Arguments

emobj.0

a emret object for the null hypothesis.

emobj.a

a emret object for the alternative hypothesis.

x

the data matrix, dimension \(n\times p\).

tau

proportion of null and alternative hypotheses.

n.mc.E.delta

number of Monte Carlo simulations for expectation of delta (difference of logL).

n.mc.E.chi2

number of Monte Carlo simulations for expectation of chisquare statistics.

verbose

if verbose.

Author

Wei-Chen Chen and Ranjan Maitra.

Details

This function calls several subroutines to compute information, likelihood ratio statistics, degrees of freedom, non-centrality of chi-squared distributions ... etc. Based on Monte Carlo methods to estimate parameters of likelihood mixture tests, this function return a p-value for testing H0: emobj.0 v.s. Ha: emobj.a.

References

https://www.stat.iastate.edu/people/ranjan-maitra

See Also

init.EM.

Examples

if (FALSE) {
library(EMCluster, quietly = TRUE)
set.seed(1234)

x <- as.matrix(iris[, 1:4])
p <- ncol(x)
min.n <- p * (p + 1) / 2
.EMC$short.iter <- 200

ret.2 <- init.EM(x, nclass = 2, min.n = min.n, method = "Rnd.EM")
ret.3 <- init.EM(x, nclass = 3, min.n = min.n, method = "Rnd.EM")
ret.4 <- init.EM(x, nclass = 4, min.n = min.n, method = "Rnd.EM")

(lmt.23 <- lmt(ret.2, ret.3, x))
(lmt.34 <- lmt(ret.3, ret.4, x))
(lmt.24 <- lmt(ret.2, ret.4, x))
}