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Calculate the Effective Sample Size for a marginal posterior sample obtained via MCMC
ess(x, ignoreBurnin = FALSE, burninProportion = 0.1)Returns the estimated effective sample size for the last (1-burninProportion) samples in x.
a numeric vector of length N assumed to be samples from a Markov chain
logical indictating whether or not the first burninProportion of vector x should be ignored
if ignoreBurnin == TRUE, the first burninProportion*length(x) samples are removed from x before the ess is calculated
David Welch david.welch@auckland.ac.nz, Chris Groendyke cgroendyke@gmail.com
Calculates the effective sample size of x based on an estimate of the lag autocorrelation function. Details of the method are in Section 11.5 of Bayesian Data Analysis, Third Edition, 2013, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2013 Bayesian Data Analysis, Third Edition, (Section 11.5), Boca Raton, Florida: CRC Press.
set.seed(8)
x <- runif(1000)
# expect ESS of close to 900 as samples are iid
ess(x, ignoreBurnin = TRUE)
# no burnin to ignore so ess is actually close to 1000
ess(x, ignoreBurnin = FALSE)
# ESS is a rough measure at best
ess(1:1000,ignoreBurnin = FALSE)
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