eigenmodel-package {eigenmodel} | R Documentation |
This package estimates the parameters of 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 accomodated, 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.
Package: | eigenmodel |
Type: | Package |
Version: | 1.0 |
Date: | 2007-06-25 |
License: | GPL Version 2 |
Peter Hoff <hoff@stat.washington.edu>
Hoff (2007) ``Modeling homophily and stochastic equivalence in symmetric relational data''
data(YX_Friend) fit<-eigenmodel_mcmc(Y=YX_Friend$Y,X=YX_Friend$X,R=2,S=750,burn=250) # in general you should run the Markov chain longer than 750 scans plot(fit) # people familiar with MCMC might want to implement # their own Markov chains: Y<-YX_Friend$Y X<-YX_Friend$X eigenmodel_setup(R=2) for(s in 1:100) { # you should run your chain longer than 100 scans Z<-rZ_fc() UL<-rUL_fc() b<-rb_fc() } #fit_Gen<-eigenmodel_mcmc(Y=Y_Gen,R=3,S=10000) #fit_Pro<-eigenmodel_mcmc(Y=Y_Pro,R=3,S=10000)