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tbergm-class

An S4 class to represent a fitted TERGM using Bayesian estimation


Description

An S4 class to represent a fitted TERGM using Bayesian estimation.

Usage

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

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

timesteps.tbergm(object)

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

Arguments

object

A tbergm object.

...

Further arguments for the summary function in the Bergm package.

Details

tbergm objects result from Bayesian estimation of a TERGM using the tbergm function. They contain the original bergm object and some additional information.

Functions

  • show,tbergm-method: Show the coefficients of a tbergm object.

  • nobs,tbergm-method: Return the number of observations saved in a tbergm object.

  • timesteps.tbergm: Return the number of time steps saved in a tbergm object.

  • summary,tbergm-method: Summary of a fitted tbergm object.

Slots

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: "bergm" for Bayesian estimation.

bergm

The original bergm object as estimated by the bergm function in the Bergm package.

nvertices

Number of vertices.

data

The data after processing by the tergmprepare function.

Author(s)

Philip Leifeld

See Also


btergm

Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

v1.10.3
GPL (>= 2)
Authors
Philip Leifeld [aut, cre], Skyler J. Cranmer [ctb], Bruce A. Desmarais [ctb]
Initial release
2021-06-24

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