inst/examples/sample_mallows_example.R

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

# Sample 100 random rankings from a Mallows distribution with footrule distance
set.seed(1)
# Number of items
n_items <- 15
# Set the consensus ranking
rho0 <- seq(from = 1, to = n_items, by = 1)
# Set the scale
alpha0 <- 10
# Number of samples
n_samples <- 100
# We first do a diagnostic run, to find the thinning and burnin to use
# We set n_samples to 1000, in order to run 1000 diagnostic iterations.
test <- sample_mallows(rho0 = rho0, alpha0 = alpha0, diagnostic = TRUE,
                       n_samples = 1000, burnin = 1, thinning = 1)
# When items_to_plot is not set, 5 items are picked at random. We can change this.
# We can also reduce the number of lags computed in the autocorrelation plots
test <- sample_mallows(rho0 = rho0, alpha0 = alpha0, diagnostic = TRUE,
                       n_samples = 1000, burnin = 1, thinning = 1,
                       items_to_plot = c(1:3, 10, 15), max_lag = 500)
# From the autocorrelation plot, it looks like we should use
# a thinning of at least 200. We set thinning = 1000 to be safe,
# since the algorithm in any case is fast. The Markov Chain
# seems to mix quickly, but we set the burnin to 1000 to be safe.
# We now run sample_mallows again, to get the 100 samples we want:
samples <- sample_mallows(rho0 = rho0, alpha0 = alpha0, n_samples = 100,
                          burnin = 1000, thinning = 1000)
# The samples matrix now contains 100 rows with rankings of 15 items.
# A good diagnostic, in order to confirm that burnin and thinning are set high
# enough, is to run compute_mallows on the samples
model_fit <- compute_mallows(
  setup_rank_data(samples),
  compute_options = set_compute_options(nmc = 10000))
# The highest posterior density interval covers alpha0 = 10.
burnin(model_fit) <- 2000
compute_posterior_intervals(model_fit, parameter = "alpha")