BayesMallowsSMC2 (version 0.2.1)

set_hyperparameters: Set hyperparameters

Description

Set the hyperparameters for the Bayesian Mallows model. This function creates a list of hyperparameter values that can be passed to compute_sequentially().

Usage

set_hyperparameters(
  n_items,
  alpha_shape = 1,
  alpha_rate = 0.5,
  cluster_concentration = 10,
  n_clusters = 1
)

Value

A list with components n_items, alpha_shape, alpha_rate, cluster_concentration, and n_clusters.

Arguments

n_items

Integer defining the number of items.

alpha_shape

Shape parameter of the gamma prior distribution for the scale parameter alpha. Defaults to 1.

alpha_rate

Rate parameter of the gamma prior distribution for the scale parameter alpha. Defaults to 0.5.

cluster_concentration

Concentration parameter of the Dirichlet distribution for cluster probabilities. Only used when n_clusters > 1. Defaults to 10.

n_clusters

Integer defining the number of clusters. Defaults to 1.

Examples

# Example: Set hyperparameters and use them with partial rankings
# Set hyperparameters with default values
hyperparams1 <- set_hyperparameters(n_items = 5)

# Set hyperparameters with custom prior for alpha
# A larger alpha_shape and smaller alpha_rate increases the prior mean
hyperparams2 <- set_hyperparameters(
  n_items = 5, 
  alpha_shape = 2, 
  alpha_rate = 1
)

# Use the hyperparameters with compute_sequentially
# This example uses partial rankings with a small number of particles
# for fast execution suitable for CRAN checks
set.seed(123)
mod <- compute_sequentially(
  partial_rankings,
  hyperparameters = hyperparams2,
  smc_options = set_smc_options(
    n_particles = 20, 
    n_particle_filters = 4,
    max_rejuvenation_steps = 3
  )
)