R: Likelihood Mixture Tests

Likelihood Mixture Tests {EMCluster} | R Documentation

## 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)

### 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 * 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.

### 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`.

### Value

A list of class `lmt` are returned.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra.

### References

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

### See Also

`init.EM`.

### Examples

    ## Not run:
    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))

    ## End(Not run)

[Package EMCluster version 0.2-13 Index]