epinet (version 2.1.11)

ess: Calculate the Effective Sample Size

Description

Calculate the Effective Sample Size for a marginal posterior sample obtained via MCMC

Usage

ess(x, ignoreBurnin = FALSE, burninProportion = 0.1)

Value

Returns the estimated effective sample size for the last (1-burninProportion) samples in x.

Arguments

x

a numeric vector of length N assumed to be samples from a Markov chain

ignoreBurnin

logical indictating whether or not the first burninProportion of vector x should be ignored

burninProportion

if ignoreBurnin == TRUE, the first burninProportion*length(x) samples are removed from x before the ess is calculated

Author

David Welch , Chris Groendyke

Details

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.

References

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.

Examples

	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)