summary.mcmcSAR {PartialNetwork}

Summarizing Bayesian SAR Model

Description

Summary and print methods for the class mcmcSAR.

Usage

## S3 method for class 'mcmcSAR'
summary(object, alpha = 0.95, plot.type = NULL, burnin = NULL, ...)

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

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

Arguments

object

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

alpha

(optional, default = 0.95), the significance level of parameter.

plot.type

(optional) a character that indicate if the simulations from the posterior distribution should be printed (if plot.type = "sim") or if the posterior distribution densities should be plotted (plot.type = "dens"). The plots can also done using the method plot.

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.

...

further arguments passed to or from other methods.

x

an object of class "summary.mcmcSAR" or "mcmcSAR, output of the functions summary.mcmcSAR and print.summary.mcmcSAR.

Details

The function is smart and allows all the possible arguments with the functions summary, plot, par... such as col, lty, mfrow... summary.mcmcSAR, print.summary.mcmcSAR and print.mcmcSAR can be called by summary or print.

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

matrix (or list of matrices) containing the simulations.

hyperparms

return value of hyperparms.

accept.rate

acceptance rate of zeta.

prop.net

proportion of observed network data.

method.net

network formation model specification.

formula

input value of formula.

alpha

significance level of parameter.

ctrl.mcmc

return value of ctrl.mcmc.

...

arguments passed to methods.