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.

1 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.