Analysis of Geostatistical Data using Bayes and Empirical Bayes Methods
Plot the estimated Bayes factors
Reverse logistic regression estimation
Spatial correlation used in the geoBayes package
Models used in the geoBayes package
Subset MCMC chain
Approximate log-likelihood calculation
Log-likelihood approximation
Log-likelihood maximisation
Computation of Bayes factors at the skeleton points
Compute the Bayes factors at new points
Empirical Bayes estimator
Empirical Bayes standard errors
Batch means, Bayes factors standard errors
The geoBayes package
Calculate the link function for exponential families
Convert to an mcmc object
MCMC samples from the Spatial GLMM
MCMC samples from the Spatial GLMM
MCMC samples from the transformed Gaussian model
MCMC samples from the transformed Gaussian model
Make prediction grid
Simulation from a spatial model
Selection of multiple importance sampling distributions
Spatial variance-covariance matrix
Spatial log likelihood
Spatial log likelihood
Combine data.frames
Functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.