plot.netcmc

This function takes a netcmc object of samples from the posterior 
distribution of a parameter(s) and returns a visual convergence diaagnostics in 
the form of a density plot, trace plot, and ACF plot.

Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).

George Gerogiannis

netcmc

Spatio-Network Generalised Linear Mixed Models for Areal Unit
and Network Data

plot.netcmc function

<dl>
 <dt>x</dt>
<dd>A netcmc object of samples from the posterior 
 distribution of a parameter(s).</dd> 
 <dt>...</dt>
<dd>Ignored.s</dd> 
</dl>

Arguments

Author

A function that plots visual MCMC diagnostics of the fitted model. — plot.netcmc

<dl>


 <dt>x</dt>
<dd>A netcmc object of samples from the posterior 
 distribution of a parameter(s).</dd>

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

 
</dl>

plot.netcmc: A function that plots visual MCMC diagnostics of the fitted model.

Description

Usage

Value

Arguments

Author