This term serves as an intercept term, is included by
default (though, as in lm, it can be excluded by
adding +0 or -1 into the model formula). It adds
one covariate to the model, for which x[i,j]=1 for all
i and j.
It can be used explicitly to set prior mean and variance for the intercept term.
This term differs from the ergm's
edges-ergmTerm term if the network has self-loops.
Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.
# binary: 1(mean=0, var=9)# binary: Intercept(mean=0, var=9)
# binary: intercept(mean=0, var=9)
# valued: 1(mean=0, var=9)
# valued: Intercept(mean=0, var=9)
# valued: intercept(mean=0, var=9)
prior mean and variance.
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "Intercept", "subsection")