Expectation maximization - Beta-Binomial and Uniform Distributions

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

Usage

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

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

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 model type. Options: "free" (estimated parameter(s): alpha, mean, and variance), "fixed" (estimated parameter(s): alpha), "fixed-2" (estimated parameter(s): alpha and variance), or "fixed-3" (estimated parameter(s): variance). If avec is length of 1, fixed and fixed-3 will not be able to return a log-likelihood.

Value

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