Deprecated for networks without self-loops. Use
nodecov-ergmTerm or
nodefactor-ergmTerm instead.
If the attribute is numeric, this term adds one covariate to the
model equaling attrname(i). If the attribute is not
numeric or force.factor==TRUE, this term adds \(p-1\)
covariates to the model, where \(p\) is the number of unique
values of attrname. The \(k\)th such covariate has the
value attrname(i) == value(k+1), where value(k) is
the \(k\)th smallest unique value of the attrname
attribute. This term only makes sense if the network is directed.
Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.
# binary: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)# valued: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)
a character string giving the name of an attribute in the network's vertex attribute list.
logical, indicating if attrname's value
should be interpreted as categorical even if numeric.
prior mean and variance.
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "socialitycov", "subsection")