mtergm-class function

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

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

## 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.
  

See Also

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

Author(s)

Philip Leifeld