Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a probabilistic rank analysis based on an almost uniform sample of possible rankings that preserve a partial ranking.
1  mcmc_rank_prob(P, rp = nrow(P)^3)

P 
P A partial ranking as matrix object calculated with neighborhood_inclusion or positional_dominance. 
rp 
Integer indicating the number of samples to be drawn. 
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 P
.
See vignette("benchmarks",package="netrankr")
for guidelines and benchmark results.
expected.rank 
Estimated expected ranks of nodes 
relative.rank 
Matrix containing estimated relative rank probabilities:

David Schoch
Bubley, R. and Dyer, M., 1999. Faster random generation of linear extensions. Discrete Mathematics, 201(1):8188
exact_rank_prob, approx_rank_relative, approx_rank_expected
1 2 3 4 5 6 7 8 9 10  ## Not run:
data("florentine_m")
P < neighborhood_inclusion(florentine_m)
res < exact_rank_prob(P)
mcmc < mcmc_rank_prob(P, rp = vcount(g)^3)
# mean absolute error (expected ranks)
mean(abs(res$expected.rank  mcmc$expected.rank))
## End(Not run)

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