Covariate effect on self-loops
This term adds one covariate to the model, for which x[i,i]=attrname(i) and x[i,j]=0 for i!=j. This term only makes sense if the network has self-loops.
Important: This term works in list("latentnet")'s ergmm() only. Using it in ergm() will result in an error.
# binary: loopcov(attrname, mean=0, var=9) # valued: loopcov(attrname, mean=0, var=9)
attrname: a character string giving the name of a numeric (not categorical) attribute in the network's vertex attribute list.mean, var: prior mean and variance.ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "loopcov", "subsection")