Factor attribute effect on self-loops
This term adds multiple covariates to the model, one for each of (a subset of) the unique values of the attrname attribute (or each combination of the attributes given). Each of these covariates has x[i,i]=1 if attrname(i)==l, where l is that covariate's level, and x[i,j]=0 otherwise. To include all attribute values se base=0 -- because the sum of all such statistics equals twice the number of self-loops and hence a linear dependency would arise in any model also including loops. Thus, the base argument tells which value(s) (numbered in order according to the sort function) should be omitted. The default value, base=1, means that the smallest (i.e., first in sorted order) attribute value is omitted. For example, if the fruit factor has levels orange , apple , banana , and pear , then to add just two terms, one for apple and one for pear , then set banana and orange to the base (remember to sort the values first) by using nodefactor("fruit", base=2:3). For an analogous term for quantitative vertex attributes, see nodecov.attrname is a character string giving the name of a numeric (not categorical) attribute in the network's vertex attribute list. 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: loopfactor(attrname, mean=0, var=9) # valued: loopfactor(attrname, mean=0, var=9)
attrname: a character vector giving one or more names of categorical attributes 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", "loopfactor", "subsection")