getStepsToAdapt

<p>Method of MCMC class (access via mcmc$&lt;function name&gt;, where mcmc is an object initialized by initializeMCMCObject). Return number of iterations (total iterations = samples * thinning) to allow proposal widths to adapt</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addition 'AnaCoDa' contains three currently unpublished models.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.

getStepsToAdapt: getStepsToAdapt

Description

Arguments

Value