The function calculates the minimum effective sample size required for a specified relative tolerance level. This function can also calculate the relative precision in estimation for a given estimated effective sample size.
minESS(p, alpha = .05, eps = .05, ess = NULL)dimension of the estimation problem.
confidence level
tolerance level. The eps value is ignored is ess is not NULL
Estimated effective sample size. Usually the output value from multiESS.
By default function returns the minimum effective sample required for a given eps tolerance. If ess is specified, then the value returned is the eps corresponding to that ess.
The minimum effective samples required when estimating a vector of length p, with 100(1-\(\alpha\))% confidence and tolerance of \(\epsilon\) is
$$\mbox{mESS} \geq \frac{2^{2/p} \pi}{(p \Gamma(p/2))^{2/p}} \frac{\chi^2_{1-\alpha, p}}{\epsilon^2} $$
The above equality can also be used to get \(\epsilon\) from an already obtained estimate of mESS.
Gong, L., and Flegal, J. M. A practical sequential stopping rule for high-dimensional Markov chain Monte Carlo. Journal of Computational and Graphical Statistics (to appear).
Vats, D., Flegal, J. M., and, Jones, G. L Multivariate Output Analysis for Markov chain Monte Carlo, arXiv preprint arXiv:1512.07713 (2015).
multiESS, which calculates multivariate effective sample size using a Markov chain and a function g.
ess which calculates univariate effective sample size using a Markov chain and a function g.