Description Usage Arguments Value Examples
Runs the MetropoliswithinGibbs sampler on the given Dynamic Bipartite Latent Position Model (dblpm network).
1  dblpm_mcmc(network, niter, burnin, thin, x_var, w_var, gamma_var, beta_var, verbose = T)

network 
A list identifying a dblpm network. 
niter 
Number of iterations after thinning and burnin. 
burnin 
Number of iterations to be discaded before starting the count for niter. The burnin iterations are not thinned. 
thin 
After burnin, keep one sampled observation every 
x_var 
Proposal variance for the positions of sender nodes. 
w_var 
Proposal variance for the positions of receiver nodes. 
gamma_var 
Proposal variance for the intercept gamma. 
beta_var 
Proposal variance for the intercept beta. 
verbose 

computing_time 
Number of seconds required for the sampling process. 
samples 
Sampled values for each of the model parameters. 
tail 
dblpm network sampled in the last iteration. 
1 2 3 4 5 6  data(IrishDirectoratesFit)
IrishDirectoratesFit < dblpm_mcmc(network = IrishDirectoratesFit$tail,
niter = 3, burnin = 6, thin = 3,
x_var = 4.75, w_var = 0.25, gamma_var = 1.825, beta_var = 0.2175,
verbose = TRUE)
# to replicate the results of the paper: niter = 10000, burnin = 500000, thin = 50

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