When diff=FALSE , this term adds one network statistic
to the model, which counts the number of edges \((i,j)\) for which
attr(i)==attr(j) . This is also called “uniform homophily”, because each group is assumed to have the same propensity for within-group ties. When multiple attribute names are given, the
statistic counts only ties for which all of the attributes
match. When diff=TRUE , \(p\) network statistics are added
to the model, where \(p\) is the number of unique values of the
attr attribute. The \(k\) th such statistic counts the
number of edges \((i,j)\) for which attr(i) == attr(j) == value(k) , where value(k) is the \(k\) th
smallest unique value of the attr attribute. This is also called “differential homophily”, because each group is allowed to have a unique propensity for within-group ties. Note that a statistical test of uniform vs. differential homophily should be conducted using the ANOVA function.
By default, matches on all levels \(k\) are
counted. This works for both
diff=TRUE and diff=FALSE .
# binary: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL)# valued: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum")
# valued: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum")
a vertex attribute specification (see Specifying Vertex attributes and Levels (?nodal_attributes) for details.)
specify if the term has uniform or differential homophily
deprecated
this optional argument controls which levels of the attribute
attributes and Levels (?nodal_attributes) for details.)
character how to aggregate tie values in a valued ERGM
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "nodematch", "subsection")