Performs a probabilistic rank analysis based on an almost uniform sample of possible rankings that preserve a partial ranking.
mcmc_rank_prob(P, rp = nrow(P)^3)
Integer indicating the number of samples to be drawn.
Estimated expected ranks of nodes
Matrix containing estimated relative rank probabilities:
relative.rank[u,v] is the probability that u is ranked lower than v.
This function can be used instead of exact_rank_prob
if the number of elements in
P is too large for an exact computation. As a rule of thumb,
the number of samples should be at least cubic in the number of elements in
vignette("benchmarks",package="netrankr") for guidelines and benchmark results.
Bubley, R. and Dyer, M., 1999. Faster random generation of linear extensions. Discrete Mathematics, 201(1):81-88