summary.smmSAR: Summarizing SMM Estimation of SAR model

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

Summarizing SMM Estimation of SAR model

Description

Summary and print methods for the class smmSAR.

Usage

## S3 method for class 'smmSAR'
summary(object, .fun, .args, sim = 30, ncores = 1, dnetwork, data, ...)

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

## S3 method for class 'smmSAR'
print(x, dnetwork, .fun, .args, sim = NULL, ncores = 1, data, ...)

Arguments

object	an object of class "smmSAR", output of the function smmSAR.
.fun, .args	are used to simulate from the distribution of dnetwork. .fun is the simulator function where .args is a list of its arguments. Typically do.call(.fun, .args) is supposed to simulate one dnetwork from the distribution.
sim	the number of simulations of dnetwork.
ncores	the number of cores to be used for the simulation. Use a lot of cores for fast simulations.
dnetwork	a list, where the m-th elements is the matrix of link probability in the m-th sub-network.
data	optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If missing, the variables are taken from environment(formula), typically the environment from which smmSAR is called.
...	further arguments passed to or from other methods.
x	an object of class "summary.smmSAR" or "smmSAR", output of the functions summary.smmSAR or smmSAR.

Value

A list consisting of:

n.group	number of groups.
N	vector of each group size.
estimates	vector of estimated parameters.
formula	input value of formula.
contextual	input value of contextual.
fixed.effects	input value of fixed.effects.
smm.ctr	input value of smm.ctr.
details	other details of the model.

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