bbnam.bf: Estimate Bayes Factors for the bbnam

Description

This function uses monte carlo integration to estimate the BFs, and tests the fixed probability, pooled, and pooled by actor models. (See bbnam for details.)

Usage

bbnam.bf(dat, nprior=matrix(rep(0.5, dim(dat)[1]^2), 
    nrow = dim(dat)[1], ncol = dim(dat)[1]), em.fp=0.5, ep.fp=0.5, 
    emprior.pooled=c(1, 1), epprior.pooled=c(1, 1), 
    emprior.actor=cbind(rep(1, dim(dat)[1]), rep(1, dim(dat)[1])), 
    epprior.actor=cbind(rep(1, dim(dat)[1]), rep(1, dim(dat)[1])), 
    diag=FALSE, mode="digraph", reps=1000)

Arguments

dat

Data array to be analyzed. This array must be of dimension m x n x n, where n is |V(G)|, the first dimension indexes the observer, the second indexes the sender of the relation, and the third dimension indexes the recipient of the relation. (E.g.,

nprior

Network prior matrix. This must be a matrix of dimension n x n, containing the arc/edge priors for the criterion network. (E.g., nprior[i,j] gives the prior probability of i sending the relation to j in the criterion graph.) If no network

em.fp

Probability of false negatives for the fixed probability model

ep.fp

Probability of false positives for the fixed probability model

emprior.pooled

(alpha,beta) pairs for the (beta) false negative prior under the pooled model

epprior.pooled

(alpha,beta) pairs for the (beta) false positive prior under the pooled model

emprior.actor

Matrix of per observer (alpha,beta) pairs for the (beta) false negative prior under the per observer/actor model

epprior.actor

Matrix of per observer (alpha,beta) pairs for the (beta) false negative prior under the per observer/actor model

diag

Boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the criterion graph can contain loops. Diag is false by default.

mode

String indicating the type of graph being evaluated. "Digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected. Mode is set to "digraph" by default.

reps

Number of monte carlo draws to take

Value

An object of class bayes.factor.

Details

The bbnam model (detailed in the bbnam function help) is a fairly simple model for integrating informant reports regarding social network data. bbnam.bf computes Bayes Factors (integrated likelihood ratios) for the three error submodels of the bbnam: fixed error probabilities, pooled error probabilities, and per observer/actor error probabilities.

References

Butts, C.T. (1999). ``Network Inference, Error, and Informant (In)Accuracy: A Bayesian Approach.'' CASOS Working Paper, Carnegie Mellon University.