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Keywords:

  • adjacency matrix;
  • dependent binary data;
  • GLMM;
  • IGLS;
  • logistic regression;
  • p1 model;
  • random effects;
  • social network analysis

A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node. Parameter estimates are proposed that are based on an iterated generalized least-squares procedure. An application is presented to a data set on friendship relations between American lawyers.