Description Usage Arguments Value Examples
Runs the Metropolis-within-Gibbs 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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