Semiparametric Factor and Regression Models for Symmetric Relational Data
Adds lines between nodes to an existing plot of nodes
Semiparametric Factor and Regression Models for Symmetric Relational D...
Approximate the posterior distribution of parameters in an eigenmodel
Setup constants and starting values for an eigenmodel fit
Plot the output of an eigenmodel fit
Sample from the full conditional distribution of the regression coeffi...
Sample from the multivariate normal distribution
Sample UL from its full conditional distribution
Sample from the full conditional distribution of the probit latent var...
Computes a matrix from its eigenvalue decomposition
Computes a sociomatrix of regression effects
Impute missing values of a sociomatrix
Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <arXiv:0711.1146> for details on the model.