getCodonSpecificPosteriorMeanForCodon

mixture to calculate CSP posterior mean. Should be between 1 and n, where n is number of mixtures.

mixtureElement

number of samples to use for calculating posterior mean

samples

codon

CSP to calculate posterior mean for. 0: Mutation (ROC,FONSE) or Alpha (PA, PANSE). 1: Selection (ROC,FONSE), Lambda (PANSE), Lambda^prime (PA). 2: NSERate (PANSE)

paramType

If model uses reference codon, then ignore this codon (fixed at 0). Should be TRUE for ROC and FONSE. Should be FALSE for PA and PANSE.

withoutReference

If true, calculate posterior mean on log scale. Should only be used for PA and PANSE.

log_scale

Method of Parameter class (access via parameter$&lt;function name&gt;, where parameter is an object initialized by initializeParameterObject). Calculate codon-specific parameter (CSP) posterior mean

Is a collection of models to analyze genome scale codon
data using a Bayesian framework. Provides visualization
routines and checkpointing for model fittings. Currently
published models to analyze gene data for selection on codon
usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
(Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
addition 'AnaCoDa' contains three currently unpublished models.
The FONSE (First order approximation On NonSense Error) model
analyzes gene data for selection on codon usage against of
nonsense error rates. The PA (PAusing time) and PANSE (PAusing
time + NonSense Error) models use ribosome footprinting data to
analyze estimate ribosome pausing times with and without
nonsense error rate from ribosome footprinting data.

getCodonSpecificPosteriorMeanForCodon: getCodonSpecificPosteriorMeanForCodon

Description

Arguments

Value