plot.mcmcSAR: Plotting estimation of Bayesian SAR model

In ahoundetoungan/PartialNetwork: Estimating Peer Effects Using Partial Network Data

Plotting estimation of Bayesian SAR model

Description

Plotting the simulation from the posterior distribution as well as the density functions of Bayesian SAR model parameter. For more details about the graphical parameter arguments, see par.

Usage

## S3 method for class 'mcmcSAR'
plot(x, plot.type = "sim", burnin = NULL, which.parms = "theta", ...)

## S3 method for class 'plot.mcmcSAR'
print(x, ...)

Arguments

x	object of class "mcmcSAR", output of the function mcmcSAR or object of class "plot.mcmcSAR", output of the function plot.mcmcSAR.
plot.type	character indicating the type of plot: "sim" for plotting the simulation from the posterior distribution or "dens" for plotting the posterior density functions.
burnin	number of MCMC steps which will be considered as burn-in iterations. If NULL (default value), the 50% first MCMC steps performed are used as burn-in iterations.
which.parms	character indicating the parameters whose the posterior distribution will be plotted: "theta" for the parameters of the outcome model and "rho" for the parameters of the network formation model.
...	arguments to be passed to methods, such as par.

Value

A list consisting of:

n.group	number of groups.
N	vector of each group size.
iteration	number of MCMC steps performed.
burnin	number of MCMC steps which will be considered as burn-in iterations.
posterior	summary of the posterior distribution to be plotted.
hyperparms	return value of hyperparms.
accept.rate	acceptance rate of zeta.
propG0.obs	proportion of observed network data.
method.net	network formation model specification.
formula	input value of formula.
ctrl.mcmc	return value of ctrl.mcmc.
which.parms	return value of which.parms.
plot.type	type of the plot.
...	arguments passed to methods.

ahoundetoungan/PartialNetwork documentation built on March 15, 2024, 4:35 p.m.