pogit objectReturns basic information about the model and the priors, MCMC details and (model averaged) posterior means with 95%-HPD intervals for the regression effects and estimated posterior inclusion probabilities.
# S3 method for pogit
summary(object, IAT = FALSE, printRes = FALSE, ...)# S3 method for summary.pogit
print(x, ...)
an object of class pogit
if TRUE, integrated autocorrelation times (IAT) and
effective samples sizes (ESS) of the MCMC samples are computed (see
details); defaults to FALSE.
if TRUE, model averaged posterior means for the
reporting probabilities and risks are computed for the Pogit model;
defaults to FALSE.
further arguments passed to or from other methods (not used)
a summary.pogit object produced by summary.pogit()
an object of class summary.pogit
To assess mixing and efficiency of MCMC sampling, the effective sample size (ESS) and the integrated autocorrelation time (IAT) are computed. ESS estimates the equivalent number of independent draws corresponding to the dependent MCMC draws and is defined as ESS = \(M\)/\(\tau\), where \(\tau\) is the IAT and \(M\) is the number of MCMC iterations after the burn-in phase. IAT is computed as \(\tau = 1 + 2 \sum_{k=1}^K \rho(k)\) using the initial monotone sequence estimator (Geyer, 1992) for K and \(\rho(k)\) is the empirical autocorrelation at lag \(k\).
Geyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science, 7, 473-483.