residuals.CARBayesST

This function takes a CARBayesST object and returns a set of residuals. 
 The allowable types of residual are "response" (raw), and "pearson" (the 
 default). In each case the fitted values are based on 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

residuals.CARBayesST function

<dl> <dt>object</dt>
<dd>A CARBayesST fitted model object.</dd> <dt>type</dt>
<dd>A text string and one of c("response", "pearson"). If this
 argument is omitted the default is "pearson".</dd> <dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Author

Extract the residuals from a model. — residuals.CARBayesST

<dl>

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

 <dt>type</dt>
<dd>A text string and one of c("response", "pearson"). If this
 argument is omitted the default is "pearson".</dd>

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

</dl>

residuals.CARBayesST: Extract the residuals from a model.

Description

Usage

Arguments

Author