sess

Calculate the effective sample size, per chain and in total, of draws from an MCMC algorithm

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

Luke Duttweiler

genMCMCDiag

Generalized Convergence Diagnostics for Difficult MCMC
Algorithms

sess function

<dl><dt>mhDraws</dt>
<dd>List of data.frames. Each data.frame represents a single chain. Data.frame columns for which ESS is calculated should be named val.1, ..., val.k</dd>
<dt>...</dt>
<dd>Catches unnecessary additional arguments</dd></dl>

Arguments

Calculate the effective sample size, per chain and in total, of draws from an MCMC algorithm — sess

<dl>

<dt>mhDraws</dt>
<dd>List of data.frames. Each data.frame represents a single chain. Data.frame columns for which ESS is calculated should be named val.1, ..., val.k</dd>


<dt>...</dt>
<dd>Catches unnecessary additional arguments</dd>

</dl>

sess: Calculate the effective sample size, per chain and in total, of draws from an MCMC algorithm

Description

Usage

Value

Arguments