plot.mcmcSAR: Plotting estimation of Bayesian SAR model

Description Usage Arguments Value Examples

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

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

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

Arguments

x

an object of class "plot.mcmcSAR", output of the function plot.mcmcSAR.

plot.type

a character that indicate the type of plot: "sim" for plotting the simulation from the posterior distribution or "dens" for plotting the posterior density functions.

burnin

is the 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.

...

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.

hyperparms

return value of 'hyperparms'.

accept.rate

acceptance rate of zeta.

formula

input value of 'formula'.

plot.type

type of the plot.

ctrl.mcmc

return value of 'ctrl.mcmc'.

...

arguments passed to methods.

Examples

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## Not run: 
# Let us consider the example in mcmcSAR function
out1          <- mcmcSAR(y ~ X | X, hyperparms = hyperparms, ctrl.mcmc = ctrl)
# Print the summary
plot(out)
# Print summary with plot and change significance level 
plot(out, plot.type = "den", col = "blue")
# Print summary with plot and change significance level and the layout of the plot
plot(out, plot.type = "dens", col = "blue", mfrow = c(4, 4))
# All the possible argument with the function summary and the function plot can be passed

## End(Not run)

ahoundetoungan/PartialNetwork documentation built on Oct. 6, 2020, 1:51 a.m.