ESS: Effective Sample Size due to Autocorrelation

Description Usage Arguments Details Value References See Also

View source: R/ESS.R


This function may be used to estimate the effective sample size (ESS) (not to be confused with Elliptical Slice Sampling) of a continuous target distribution, where the sample size is reduced by autocorrelation. ESS is a measure of how well each continuous chain is mixing.

ESS is a univariate function that is often applied to each continuous, marginal posterior distribution. A multivariate form is not included. By chance alone due to multiple independent tests, 5% of the continuous parameters may indicate that ESS is below a user threshold of acceptability, such as 100, even when above the threshold. Assessing convergence is difficult.





This required argument is a vector or matrix of posterior samples.


Effective Sample Size (ESS) was recommended by Radford Neal in the panel discussion of Kass et al. (1998). When a continuous, marginal posterior distribution is sampled with a Markov chain Monte Carlo (MCMC) algorithm, there is usually autocorrelation present in the samples. More autocorrelation is associated with less posterior sampled information, because the information in the samples is autocorrelated, or put another way, successive samples are not independent from earlier samples. This reduces the effective sample size of, and precision in representing, the continuous, marginal posterior distribution. ESS is one of the criteria in the Consort function, where stopping the MCMC updates is not recommended until ESS ≥ 100. Although the need for precision of each modeler differs with each model, it is often a good goal to obtain ESS = 1000.

ESS is related to the integrated autocorrelation time (see IAT for more information).

ESS is usually defined as

ESS(theta) = S / (1 + 2 sum[k] rho[k] (theta)),

where S is the number of posterior samples, rho[k] is the autocorrelation at lag k, and theta is the vector of marginal posterior samples. The infinite sum is often truncated at lag k when rho[k](theta) < 0.05. Just as with the effectiveSize function in the coda package, the AIC argument in the ar function is used to estimate the order.

ESS is a measure of how well each continuous chain is mixing, and a continuous chain mixes better when in the target distribution. This does not imply that a poorly mixing chain still searching for its target distribution will suddenly mix well after finding it, though mixing should improve. A poorly mixing continuous chain does not necessarily indicate problems. A smaller ESS is often due to correlated parameters, and is commonly found with scale parameters. Posterior correlation may be obtained from the PosteriorChecks function, and plotted with the plotMatrix function. Common remedies for poor mixing include re-parameterizing the model or trying a different MCMC algorithm that better handles correlated parameters. Slow mixing is indicative of an inefficiency in which a continuous chain takes longer to find its target distribution, and once found, takes longer to explore it. Therefore, slow mixing results in a longer required run-time to find and adequately represent the continuous target distribution, and increases the chance that the user may make inferences from a less than adequate representation of the continuous target distribution.

There are many methods of re-parameterization to improve mixing. It is helpful when predictors are centered and scaled, such as with the CenterScale function. Parameters for predictors are often assigned prior distributions that are independent per parameter, in which case an exchangeable prior distribution or a multivariate prior distribution may help. If a parameter with poor mixing is bounded with the interval function, then transforming it to the real line (such as with a log transformation for a scale parameter) is often helpful, since constraining a parameter to an interval often reduces ESS. Another method is to re-parameterize so that one or more latent variables represent the process that results in slow mixing. Such re-parameterization uses data augmentation.

This is numerically the same as the effectiveSize function in the coda package, but programmed to accept a simple vector or matrix so it does not require an mcmc or mcmc.list object, and the result is bound to be less than or equal to the original number of samples.


A vector is returned, and each element is the effective sample size (ESS) for a corresponding column of x, after autocorrelation has been taken into account.


Kass, R.E., Carlin, B.P., Gelman, A., and Neal, R. (1998). "Markov Chain Monte Carlo in Practice: A Roundtable Discussion". The American Statistician, 52, p. 93–100.

See Also

CenterScale, Consort, IAT, interval, LaplacesDemon, plotMatrix, and PosteriorChecks.

LaplacesDemon documentation built on July 9, 2021, 5:07 p.m.