Effective sample size calculation

Calculates an estimate of the adjusted MCMC sample size per parameter adjusted for autocorrelation.

# S3 method for hmclearn
neff(object, burnin = NULL, lagmax = NULL, ...)

Arguments

object	an object of class hmclearn, usually a result of a call to mh or hmc
burnin	optional numeric parameter for the number of initial MCMC samples to omit from the summary
lagmax	maximum lag to extract for determining effective sample sizes
...	currently unused

Value

Numeric vector with effective sample sizes for each parameter in the model

References

Gelman, A., et. al. (2013) Bayesian Data Analysis. Chapman and Hall/CRC. Section 11.5

Examples

# poisson regression example
set.seed(7363)
X <- cbind(1, matrix(rnorm(40), ncol=2))
betavals <- c(0.8, -0.5, 1.1)
lmu <- X %*% betavals
y <- sapply(exp(lmu), FUN = rpois, n=1)

f <- hmc(N = 1000,
          theta.init = rep(0, 3),
          epsilon = c(0.03, 0.02, 0.015),
          L = 10,
          logPOSTERIOR = poisson_posterior,
          glogPOSTERIOR = g_poisson_posterior,
          varnames = paste0("beta", 0:2),
          param = list(y=y, X=X),
          parallel=FALSE, chains=2)

neff(f, burnin=100)
#> [1]  877 2310  797