Plotting for bbnam Objects
Generates various plots of posterior draws from the bbnam model.
## S3 method for class 'bbnam' plot(x, mode="density", intlines=TRUE, ...)
x |
A |
mode |
“density” for kernel density estimators of posterior marginals; otherwise, histograms are used |
intlines |
Plot lines for the 0.9 central posterior probability intervals? |
... |
Additional arguments to |
plot.bbnam provides plots of the estimated posterior marginals for the criterion graph and error parameters (as appropriate). Plotting may run into difficulties when dealing with large graphs, due to the problem of getting all of the various plots on the page; the routine handles these issues reasonably intelligently, but there is doubtless room for improvement.
None
Carter T. Butts buttsc@uci.edu
Butts, C.T. (1999). “Informant (In)Accuracy and Network Estimation: A Bayesian Approach.” CASOS Working Paper, Carnegie Mellon University.
#Create some random data
g<-rgraph(5)
g.p<-0.8*g+0.2*(1-g)
dat<-rgraph(5,5,tprob=g.p)
#Define a network prior
pnet<-matrix(ncol=5,nrow=5)
pnet[,]<-0.5
#Define em and ep priors
pem<-matrix(nrow=5,ncol=2)
pem[,1]<-3
pem[,2]<-5
pep<-matrix(nrow=5,ncol=2)
pep[,1]<-3
pep[,2]<-5
#Draw from the posterior
b<-bbnam(dat,model="actor",nprior=pnet,emprior=pem,epprior=pep,
burntime=100,draws=100)
#Print a summary of the posterior draws
summary(b)
#Plot the result
plot(b)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.