mu.mle: Maximum-Likelihood Estimation of the Mutation Rate

In TimeMachine: Time Machine

Description

Estimates the mutation rate using a maximum-likelihood approach.

Usage

mu.mle(transitions, pi=NULL, population, n, mu.int, samples,
              threads=NULL, ...)

Arguments

transitions	Transition matrix between types
pi	Stationary distribution associated with the transition matrix, or NULL to compute it
population	Vector representing the initial population
n	Target population size
mu.int	Vector specifying the end-points of the interval to be searched for the optimal mutation rate
samples	Number of samples to simulate
threads	Number of threads used for simulations, or NULL to use the default OpenMP setting
...	Further arguments passed to optimize

Value

mu.hat	The maximum-likelihood estimate of the mutation rate
loglik	The log-likelihood value associated with the estimated mutation rate

Examples

# Load example dataset
data(pdm)
transitions <- full.transitions(pdm$unitary.transitions, pdm$loci)
pi <- stationary.dist(transitions)

mu.int <- c(0.1, 10)
samples <- 10

# MLE of the mutation rate for a single target population size
n <- 10
mle.res <- mu.mle(transitions, pi, pdm$population, n, mu.int, samples)
print(mle.res)

# MLE of the mutation rate for 10 different target population sizes, including
# up to the MRCA (n = 1)
ns <- 1:10
mle.res <- sapply(ns, mu.mle, transitions=transitions, pi=pi,
                  population=pdm$population, mu.int=mu.int, samples=samples)
print(mle.res)

TimeMachine documentation built on Sept. 11, 2018, 5:03 p.m.