ess {mcmcse} | R Documentation |
Univariate effective sample size (ESS) as described in Gong and Flgal (2015).
Description
Estimate the effective sample size (ESS) of a Markov chain as described in Gong and Flegal (2015).
Usage
ess(x, g = NULL, ...)
Arguments
x |
a matrix or data frame of Markov chain output. Number of rows is the Monte Carlo sample size. |
g |
a function that represents features of interest. |
... |
arguments passed on to the |
Details
ESS is the size of an iid sample with the same variance as the current sample for estimating the expectation of g. ESS is given by
where is the sample variance and
is an estimate of the variance in the Markov chain central limit theorem. The denominator by default
is a batch means estimator, but the default can be changed with the 'method' argument.
Value
The function returns the estimated effective sample size for each component of g
.
References
Gong, L. and Flegal, J. M. (2015) A practical sequential stopping rule for high-dimensional Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, 25, 684—700.
See Also
minESS
, which calculates the minimum effective samples required for the problem.
multiESS
, which calculates multivariate effective sample size using a Markov chain
and a function g
.