neff.hmclearn function

Effective sample size calculation

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

## S3 method for class '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

Returns

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

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)

References

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