Univariate estimate effective sample size (ESS) as described in Gong and Felgal (2015).
Estimate effective sample size (ESS) as described in Gong and Flegal (2015).
ess(x, g = NULL, ...)
x |
a matrix or data frame of Markov chain output. Number of rows is the Monte Carlo sample size. |
... |
arguments passed on to the |
g |
a function that represents features of interest. g is applied to each row of |
ESS is the size of an iid sample with the same variance as the current sample. ESS is given by
ESS = n λ^2/σ^2,
where
λ^2 is the sample variance and σ^2 is an estimate of the variance in the CLT. This is by default the lugsail batch means estimator, but the default can be changed with the method argument.
The function returns the estimated effective sample size.
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.
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.
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