sim_post_jeffreys: Obtain posterior simulations using Jeffreys' prior

In carlislerainey/separation: Using informative priors to deal with separation in logistic regression models

Description

sim_post_jeffreys() uses a Metropolis algorithm to obtain posterior simulations using Jeffreys' prior.

Usage

sim_post_jeffreys(formula, data, n_sims = 1000, n_burnin = n_sims/2,
  n_chains = 3, n_thin = 1, n_cores = n_chains, tune = 1)

Arguments

formula	A logistic regression model.
data	A data frame.
n_sims	The number of simulations after the burn-in period.
n_burnin	The number of burn-in iterations for the sample.
n_chains	The number of MCMC chains being run.
n_thin	The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.
n_cores	The number of MCMC cores. Defaults to the number of chains.
tune	The tuning parameter for the Metropolis sampling. Can be either a positive scalar or a (k+1)-vector, where k is the number of variables in the model. Presently passed to MCMCmetrop1R.

References

Firth, David. 1993. "Bias Reduction of Maximum Likelihood Estimates." Biometrika 80(1):27–38.

Heinze, Georg, and Michael Schemper. 2002. "A Solution to the Problem of Separation in Logistic Regression." Statistics in Medicine 21(16):2409–2419.

Jeffreys, H. 1946. "An Invariant Form of the Prior Probability in Estimation Problems." Proceedings of the Royal Society of London, Series A 186(1007):453–461.

Poirier, Dale. 1994. "Jeffreys’ Prior for Logit Models." Journal of Econometrics 63(2):327–339.

Zorn, Christopher. 2005. "A Solution to Separation in Binary Response Models." Political Analysis 13(2):157–170.

carlislerainey/separation documentation built on May 13, 2019, 12:45 p.m.