acfCSP

parameter

"Selection" or "Mutation", defaults to "Mutation"

indicates the number of CSP mixtures used

numMixtures

number of samples at the end of the trace used to calculate the acf

samples

Maximum amount of lag to calculate acf. Default is 10*log10(N), where N i the number of observations.

lag.max

logical. If TRUE (default) a plot of the acf is created

plot

The function calculates and by defaults plots the acf and estimates the autocorrelation in the trace

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addition 'AnaCoDa' contains three currently unpublished models.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.

acfCSP: Plots ACF for codon specific parameter traces

Description

Usage

Arguments

See Also