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.
latentcov can be called more than once, to model the
effects of multiple covariates. Note that some covariates can be
more conveniently specified using the following terms.
Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.
# binary: latentcov(x, attrname=NULL, mean=0, var=9)# valued: latentcov(x, attrname=NULL, mean=0, var=9)
either a matrix of covariates on each pair of vertices, a network, or an edge attribute.
optional argument to provide the name of the edge attribute.
prior mean and variance.
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "latentcov", "subsection")