tbergm {btergm}  R Documentation 
Estimate a TERGM using Bayesian estimation
Description
Estimate a TERGM using Bayesian estimation.
Usage
tbergm(formula, returndata = FALSE, verbose = TRUE, ...)
Arguments
formula 
Formula for the TERGM. Model construction works like in the
ergm package with the same model terms etc. (for a list of terms, see
help("ergmterms") ). The networks to be modeled on the
lefthand side of the equation must be given either as a list of network
objects with more recent networks last (i.e., chronological order) or as a
list of matrices with more recent matrices at the end. dyadcov and
edgecov terms accept timeindependent covariates (as network
or matrix objects) or timevarying covariates (as a list of networks
or matrices with the same length as the list of networks to be modeled).

returndata 
Return the processed input data instead of estimating and
returning the model? In the btergm case, this will return a data
frame with the dyads of the dependent variable/network and the change
statistics for all covariates. In the mtergm case, this will return
a list object with the blockdiagonal network object for the dependent
variable and blockdiagonal matrices for all dyadic covariates and the
offset matrix for the structural zeros.

verbose 
Print details about data preprocessing and estimation
settings.

... 
Further arguments to be handed over to the
bergm function in the Bergm package.

Details
The tbergm
function computes TERGMs by Bayesian estimation via
blockdiagonal matrices and structural zeros. It acts as a wrapper for the
bergm
function in the Bergm package.
Author(s)
Philip Leifeld
References
Caimo, Alberto and Nial Friel (2012): Bergm: Bayesian Exponential
Random Graphs in R. Journal of Statistical Software 61(2): 125.
doi: 10.18637/jss.v061.i02.
See Also
btergm
mtergm
[Package
btergm version 1.10.3
Index]