summary.pogit | R Documentation |
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 class 'pogit' summary(object, IAT = FALSE, printRes = FALSE, ...) ## S3 method for class 'summary.pogit' print(x, ...)
object |
an object of class |
IAT |
if |
printRes |
if |
... |
further arguments passed to or from other methods (not used) |
x |
a |
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/τ, where τ is the IAT and M is the number of MCMC iterations after the burn-in phase. IAT is computed as τ = 1 + 2 ∑_{k=1}^K ρ(k) using the initial monotone sequence estimator (Geyer, 1992) for K and ρ(k) is the empirical autocorrelation at lag k.
an object of class summary.pogit
Michaela Dvorzak <m.dvorzak@gmx.at>
Geyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science, 7, 473-483.
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