MBASED (version 1.6.0)

maxLogLikelihoodCalculator1s: Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.

Description

Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.

Usage

maxLogLikelihoodCalculator1s(lociHapACounts, lociTotalCounts, lociHapANoASEProbs, lociRhos, checkArgs = FALSE)

Arguments

lociHapACounts

counts of haplotype A-supporting reads at individual loci. Must be a vector of non-negative integers.

lociTotalCounts

total read counts of at individual loci. Must be a vector of positive integers.

lociHapANoASEProbs

probabilities of observing haplotype A-supporting reads at individual loci under conditions of no ASE (e.g., vector with all entries set to 0.5, if there is no pre-existing allelic bias at any locus). Must be a vector with entries >0 and

lociRhos

dispersion parameters of beta distribution at individual loci (set to 0 if the read count-generating distribution at the locus is binomial). Must be a numeric vector with entries >=0 and

checkArgs

single boolean specifying whether arguments should be checked for adherence to specifications. DEFAULT: FALSE

Value

a list with two elements: maximum (MLE of haplotype A frequency) and objective (loglikelihood at MLE). These are the two elements that are output by the optimize() function, which is used internally by the maxLogLikelihoodCalculator1s.

Details

Given observed read counts supporting hapltoype A at a collection of loci, the total read counts at those loci, the probablities of observing haplotype A-supporting reads under conditions of no ASE and the dispersion parameters, this function returns a maximum likelihood estimate of the true underlying frequency of haplotype A as well as corresponding value of log-likelihood.

Examples

MBASED:::maxLogLikelihoodCalculator1s(lociHapACounts=c(5, 12), lociTotalCounts=c(10, 24), lociHapANoASEProbs=c(0.5, 0.5), lociRhos=c(0,0))