edgeprob is a convenience function that creates a data frame with
all dyads in the ERGM or TERGM along with their edge probabilities and
their predictor values (i.e., change statistics). This is useful for
creating marginal effects plots or contrasting multiple groups of
dyads. This function works faster than the interpret function.
See also the interpret help page.
edgeprob(object, verbose = FALSE)An ergm, btergm, or mtergm object.
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The first variable in the resulting data frame contains the edge value (i.e., the dependent variable, which is usually binary). The next variables contain all the predictors from the ERGM or TERGM (i.e., the change statistics). The next five variables contain the indices of the sender (i), the receiver (j), the time step (t), the vertex id of i (i.name), and the vertex id of j (j.name). These five variables serve to identify the dyad. The last variable contains the computed edge probabilities.
Czarna, Anna Z., Philip Leifeld, Magdalena Smieja, Michael Dufner and Peter Salovey (2016): Do Narcissism and Emotional Intelligence Win Us Friends? Modeling Dynamics of Peer Popularity Using Inferential Network Analysis. Personality and Social Psychology Bulletin 42(11): 1588--1599.
Desmarais, Bruce A. and Skyler J. Cranmer (2012): Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks. The Policy Studies Journal 40(3): 402--434.
Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2017): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1-36. http://dx.doi.org/10.18637/jss.v083.i06.