getLogPosteriorMean

<p>postive value less than total length of the MCMC trace</p>

samples

<p>Method of MCMC class (access via mcmc$&lt;function name&gt;, where mcmc is an object initialized by initializeMCMCObject). Calculate the mean log posterior probability over the last n samples</p>

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addition 'AnaCoDa' contains three currently unpublished models.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.

getLogPosteriorMean: getLogPosteriorMean

Description

Arguments

Value