mtergm-class: An S4 Class to represent a fitted TERGM by MCMC-MLE

In leifeld/btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

An S4 Class to represent a fitted TERGM by MCMC-MLE

Description

An S4 class to represent a fitted TERGM by MCMC-MLE.

Usage

## S4 method for signature 'mtergm'
show(object)

## S4 method for signature 'mtergm'
coef(object, invlogit = FALSE, ...)

## S4 method for signature 'mtergm'
nobs(object)

timesteps.mtergm(object)

## S4 method for signature 'mtergm'
summary(object, ...)

Arguments

object	An mtergm object.
invlogit	Apply inverse logit transformation to the estimates and/or confidence intervals? That is, \frac{1}{1 + \exp(-x)}, where x is the respective value.
...	Currently not in use.

Details

mtergm objects result from MCMC-MLE-based estimation of a TERGM via the mtergm function. They contain the coefficients, standard errors, and p-values, among other details.

Functions

• show(mtergm): Show the coefficients of an mtergm object.

• coef(mtergm): Return the coefficients of an mtergm object.

• nobs(mtergm): Return the coefficients of an mtergm object.

• timesteps.mtergm(): Return the number of time steps saved in an mtergm object.

• summary(mtergm): Return the coefficients of an mtergm object.

Slots

coef

Object of class "numeric". The coefficients.

se

Object of class "numeric". The standard errors.

pval

Object of class "numeric". The p-values.

nobs

Object of class "numeric". Number of observations.

time.steps

Object of class "numeric". Number of time steps.

formula

Object of class "formula". The original model formula (without indices for the time steps).

formula2

The revised formula with the object references after applying the tergmprepare function.

auto.adjust

Object of class "logical". Indicates whether automatic adjustment of dimensions was done before estimation.

offset

Object of class "logical". Indicates whether an offset matrix with structural zeros was used.

directed

Object of class "logical". Are the dependent networks directed?

bipartite

Object of class "logical". Are the dependent networks bipartite?

estimate

Estimate: either MLE or MPLE.

loglik

Log likelihood of the MLE.

aic

Akaike's Information Criterion.

bic

Bayesian Information Criterion.

ergm

The original ergm object as estimated by the ergm function in the ergm package.

nvertices

Number of vertices.

data

The data after processing by the tergmprepare function.

Author(s)

Philip Leifeld

See Also

Other tergm-classes: btergm-class, createBtergm(), createMtergm(), createTbergm(), tbergm-class

leifeld/btergm documentation built on June 14, 2025, 7:45 p.m.