| mcmcsae-package {mcmcsae} | R Documentation |
Markov Chain Monte Carlo Small Area Estimation
Description
Fit multi-level models with possibly correlated random effects using MCMC.
Details
Functions to fit multi-level models with Gaussian, binomial, multinomial, negative binomial or Poisson likelihoods using MCMC. Models with a linear predictor consisting of various possibly correlated random effects are supported, allowing flexible modeling of temporal, spatial or other kinds of dependence structures. For Gaussian models the variance can be modeled too. By modeling variances at the unit level the marginal distribution can be changed to a Student-t or Laplace distribution, which may account better for outliers. The package has been developed with applications to small area estimation in official statistics in mind. The posterior samples for the model parameters can be passed to a prediction function to generate samples from the posterior predictive distribution for user-defined quantities such as finite population domain means. For model assessment, posterior predictive checks and DIC/WAIC criteria can easily be computed.