summary.pogit: Summary for posterior of a 'pogit' object
In airbornemint/pogit: Bayesian Variable Selection for a Poisson-Logistic Model

Description Usage Arguments Details Value Author(s) References

Returns 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 `pogit`
`IAT`	if `TRUE`, integrated autocorrelation times (IAT) and effective samples sizes (ESS) of the MCMC samples are computed (see details); defaults to `FALSE`.
`printRes`	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)
`x`	a `summary.pogit` object produced by `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/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.

airbornemint/pogit documentation built on May 31, 2019, 1:49 a.m.