summary.hmclearn function

Summarizing HMC Model Fits

summary method for class hmclearn

## S3 method for class 'hmclearn'
summary(
  object,
  burnin = NULL,
  probs = c(0.025, 0.05, 0.25, 0.5, 0.75, 0.95, 0.975),
  ...
)

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
• probs: quantiles to summarize the posterior distribution
• ...: additional arguments to pass to quantile

Returns

Returns a matrix with posterior quantiles and the posterior scale reduction factor statistic for each parameter.

Examples

# Linear regression example
set.seed(521)
X <- cbind(1, matrix(rnorm(300), ncol=3))
betavals <- c(0.5, -1, 2, -3)
y <- X%*%betavals + rnorm(100, sd=.2)

f1 <- hmc(N = 500,
          theta.init = c(rep(0, 4), 1),
          epsilon = 0.01,
          L = 10,
          logPOSTERIOR = linear_posterior,
          glogPOSTERIOR = g_linear_posterior,
          varnames = c(paste0("beta", 0:3), "log_sigma_sq"),
          param=list(y=y, X=X), parallel=FALSE, chains=1)

summary(f1)

References

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

Gelman, A. and Rubin, D. (1992) Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 7(4) 457-472.