Expectation maximization - Beta-Binomial Distribution

This function calculates the negative log-likelihood using the expectation maximization algorithm with Nelder-Mead numerical optimization and beta-binomial distribution.

Usage

emstepBB(parmlist, xm, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance.

xm

Matrix where the first column is total coverage and the second is the count of base A or B.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, \(c_{L}\) and \(c_{U}\).

type

String indicating "Free" or "Fixed".

Value

List of elements including the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

if(exists("crazy")){
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepBB(p,
                   xm,
                   niter = 100,
                   epsilon = 0.1,
                   trunc = c(0.0,0.0))
}