minESS {mcmcse} | R Documentation |
Minimum effective sample size required for stable estimation as described in Vats et al. (2015)
Description
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.
Usage
minESS(p, alpha = .05, eps = .05, ess = NULL)
Arguments
p |
dimension of the estimation problem. |
alpha |
Confidence level. |
eps |
Tolerance level. The eps value is ignored is |
ess |
Estimated effective sample size. Usually the output value from |
Details
The minimum effective samples required when estimating a vector of length p
, with confidence and tolerance of
is
The above equality can also be used to get from an already obtained estimate of
mESS.
Value
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
.
References
Gong, L., and Flegal, J. M. A practical sequential stopping rule for high-dimensional Markov chain Monte Carlo. Journal of Computational and Graphical Statistics, 25, 684–-700.
Vats, D., Flegal, J. M., and, Jones, G. L Multivariate output analysis for Markov chain Monte Carlo, Biometrika, 106, 321–-337.
See Also
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.
Examples
minESS(p = 5)