Description Usage Arguments Details Value Examples
Function that given observed count data along a known haplotype returns a maximum likelihood estimate of the underlying haplotype frequency.
1 2 3 4 | maxLogLikelihoodCalculator2s(lociHapACountsSample1, lociTotalCountsSample1,
lociHapACountsSample2, lociTotalCountsSample2, lociHapANoASEProbsSample1,
lociHapANoASEProbsSample2, lociRhosSample1, lociRhosSample2,
checkArgs = FALSE)
|
lociHapACountsSample1,lociHapACountsSample2 |
counts of haplotype A-supporting reads at individual loci in sample1 and sample2, respectively. Both arguments must be vectors of non-negative integers. |
lociTotalCountsSample1,lociTotalCountsSample2 |
total read counts of at individual loci in sample1 and sample2, respectively. Both arguments must be vectors of non-negative integers. |
lociHapANoASEProbsSample1,lociHapANoASEProbsSample2 |
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) in sample1 and sample2, respectively. Both arguments must be vectors with entries >0 and <1. |
lociRhosSample1,lociRhosSample2 |
dispersion parameters of beta distribution at individual loci (set to 0 if the read count-generating distribution at the locus is binomial) in sample1 and sample2, respectively. Both arguments must be vectors with entries >=0 and <1. |
checkArgs |
single boolean specifying whether arguments should be checked for adherence to specifications. DEFAULT: FALSE |
Given observed read counts supporting hapltoype A at a collection of loci in two samples, 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.
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 maxLogLikelihoodCalculator2s.
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