ergm-terms {ergm.count}R Documentation

Terms used in Exponential Family Random Graph Models Specific to Counts

Description

This page describes the possible terms (and hence network statistics) included in the ergm.count package.

See the ergm-terms documentation in the ergm package for a complete description of what ERGM terms are and how they are used.

Terms to represent network statistics included in the ergm.count pacakge

All terms listed are valued.

CMP

Conway-Maxwell-Poisson Distribution: This term adds one statistic to the model, of the form ∑_{i,j}\log(y_{i,j}!). This turns a Poisson- or a geometric-reference ERGM into a Conway-Maxwell-Poisson-reference ERGM, allowing it to represent a broad range of disperson values. In particular, combined with a Poisson-reference ERGM, a negative coefficient on this term induces underdispersion and a positive coefficient induces overdispersion. (This behavior is different from 3.1.1, when the negation of this value was used.)

Note that its current implementation may not perform well if the data are overdispersed relative to geometric.

CMB(trials, coupled = TRUE)

Conway-Maxwell-Binomial Distribution: If couple==TRUE, this term adds one statistic to the model, of the form ∑_{i,j}\log(y_{i,j}!) + \log(t-y_{i,j}!). This turns a Binomial- or a discrete-uniform-reference ERGM into a Conway-Maxwell-Binomial-reference ERGM, allowing it to represent a broad range of disperson values. In particular, combined with a Binomial-reference ERGM, a negative coefficient on this term induces underdispersion and a positive coefficient induces overdispersion.

If coupled==FALSE the two summands above are added as their own statistic (each with its own free parameter).

References

See Also

ergm-terms (from the ergm package), ergm, network, %v%, %n%


[Package ergm.count version 4.0.2 Index]