gbme - R-functions for statistical analysis of social network data

NEW: gbme.asym.r This is a hacked version of gbme.r, providing asymmetric inner-product effects u'v instead of z'z. The u's and v's are sender- and receiver-specific multiplicative effects, respectively. This is a more general model, and is more appropriate for data where there might be an aversion to transitivity. Take a look at Ward and Hoff(2005) for an example application.

• Usage: gbme(Y,Xd,Xs,Xr,...)
• Data arguments:
  • Y is a square (nxn) sociomatrix. Missing (at random) data is allowed.
  • Xd is an nxnxrd array giving dyad-specific covariates.
  • Xs is an nxrs matrix of sender-specific covariates.
  • Xr is an nxrr matrix of receiver-specific covariates.
• Model specification arguments (defaults in parentheses):
  • family ("gaussian") : can be "gaussian", "poisson" or "binomial"
  • directed (T) : logical denoting if the relation is directed or not. If directed=F, the algorithm assumes Y and Xd are symmetric.
  • k (0) : an integer denoting the dimension of the inner-product interaction
• Prior probability arguments: empirical Bayes priors are used if nothing is provided
• Starting values: MCMC starts from an approximate IRLS solution if nothing is provided
• Output: By default, samples of the parameters and latent characteristics are output to files "OUT" and "Z". Each row of "OUT" is as follows:
  • k (length 1)
  • the mcmc iteration number (length 1)
  • the (saturated) log-likelihood (length 1)
  • the dyadic-level regression coefficients (length rd)
  • the intercept (length 1)
  • the sender-level regression coefficients (length rs)
  • the receiver-level regression coefficients (length rr)
  • entries [1,1], [1,2], [2,2] of the covariance matrix of a's and b's
  • variance of the epsilons (length 1)
  • correlation of e_ij and e_ji (length 1)
  • variances of the k components of the z vectors (length k)
  The file "Z" consists of NS/odens matrices of dimension nxk put end to end, where NS/odens is the number of saved values of Z (default is 100000/50).

source("gbme.r")

Alternatively, if you don't want to download the file, just type

source("http://www.stat.washington.edu/hoff/Code/GBME/gbme.r")

Running the MCMC may take a long time, so you might want to do it in batch mode. Some ideas for a simple analysis of the output is given in gbme.postana.r. You might want to look at the following examples:

• International relations example (Poisson regression)
• Hansell's classroom data (logistic regression)

email: hoff@stat.washington.edu