logLik.CARBayesST

This function takes a CARBayesST object and returns the estimated loglikelihood
 (posterior means).

Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.

Duncan Lee

CARBayesST

Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
Data

logLik.CARBayesST function

<dl> <dt>object</dt>
<dd>A CARBayesST fitted model object.</dd> <dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Author

Extract the estimated loglikelihood from a fitted model. — logLik.CARBayesST

<dl>

 <dt>object</dt>
<dd>A CARBayesST fitted model object.</dd>

 <dt>...</dt>
<dd>Ignored.</dd>

</dl>

logLik.CARBayesST: Extract the estimated loglikelihood from a fitted model.

Description

Usage

Arguments

Author