run_mcmc: Worker function for computing the posterior distribution.

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Worker function for computing the posterior distribution.

Description

Worker function for computing the posterior distribution.

Usage

run_mcmc(
  rankings,
  obs_freq,
  nmc,
  constraints,
  cardinalities,
  logz_estimate,
  rho_init,
  metric = "footrule",
  error_model = "none",
  Lswap = 1L,
  n_clusters = 1L,
  include_wcd = FALSE,
  leap_size = 1L,
  alpha_prop_sd = 0.5,
  alpha_init = 5,
  alpha_jump = 1L,
  lambda = 0.1,
  alpha_max = 1e+06,
  psi = 10L,
  rho_thinning = 1L,
  aug_thinning = 1L,
  clus_thin = 1L,
  save_aug = FALSE,
  verbose = FALSE,
  kappa_1 = 1,
  kappa_2 = 1,
  save_ind_clus = FALSE
)

Arguments

rankings	A set of complete rankings, with one sample per column. With n_assessors samples and n_items items, rankings is n_items x n_assessors.
obs_freq	A vector of observation frequencies (weights) to apply to the rankings.
nmc	Number of Monte Carlo samples.
constraints	List of lists of lists, returned from 'generate_constraints'.
cardinalities	Used when metric equals "footrule" or "spearman" for computing the partition function. Defaults to R_NilValue.
logz_estimate	Estimate of the log partition function.
metric	The distance metric to use. One of "spearman", "footrule", "kendall", "cayley", or "hamming".
error_model	Error model to use.
Lswap	Swap parameter used by Swap proposal for proposing rank augmentations in the case of non-transitive pairwise comparisons.
n_clusters	Number of clusters. Defaults to 1.
include_wcd	Boolean defining whether or not to store the within-cluster distance.
leap_size	Leap-and-shift step size.
alpha_prop_sd	Standard deviation of proposal distribution for alpha.
alpha_init	Initial value of alpha.
alpha_jump	How many times should we sample rho between each time we sample alpha. Setting alpha_jump to a high number can significantly speed up computation time, since we then do not have to do expensive computation of the partition function.
lambda	Parameter of the prior distribution.
alpha_max	Maximum value of alpha, used for truncating the exponential prior distribution.
psi	Hyperparameter for the Dirichlet prior distribution used in clustering.
rho_thinning	Thinning parameter. Keep only every rho_thinning rank sample from the posterior distribution.
aug_thinning	Integer specifying the thinning for data augmentation.
clus_thin	Integer specifying the thinning for saving cluster assignments.
save_aug	Whether or not to save the augmented data every aug_thinningth iteration.
verbose	Logical specifying whether to print out the progress of the Metropolis-Hastings algorithm. If TRUE, a notification is printed every 1000th iteration.
kappa_1	Hyperparameter for theta in the Bernoulli error model. Defaults to 1.0.
kappa_2	Hyperparameter for theta in the Bernoulli error model. Defaults to 1.0.
save_ind_clus	Whether or not to save the individual cluster probabilities in each step, thinned as specified in argument clus_thin. This results in csv files cluster_probs1.csv, cluster_probs2.csv, ..., being saved in the calling directory. This option may slow down the code considerably, but is necessary for detecting label switching using Stephen's algorithm.

BayesMallows documentation built on Nov. 25, 2023, 5:09 p.m.