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("ergm-terms") ). The networks to be modeled on the
left-hand 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 time-independent covariates (as network
or matrix objects) or time-varying 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): 1-25.
doi:10.18637/jss.v061.i02.
See Also
btergm
mtergm
[Package
btergm version 1.10.12
Index]