set_hyperparameters.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set_hyperparameters.R
\name{set_hyperparameters}
\alias{set_hyperparameters}
\title{Set hyperparameters}
\usage{
set_hyperparameters(
  n_items,
  alpha_shape = 1,
  alpha_rate = 0.5,
  cluster_concentration = 10,
  n_clusters = 1
)
}
\arguments{
\item{n_items}{Integer defining the number of items.}

\item{alpha_shape}{Shape parameter of the gamma prior distribution for the
scale parameter alpha. Defaults to 1.}

\item{alpha_rate}{Rate parameter of the gamma prior distribution for the
scale parameter alpha. Defaults to 0.5.}

\item{cluster_concentration}{Concentration parameter of the Dirichlet
distribution for cluster probabilities. Only used when \code{n_clusters > 1}.
Defaults to 10.}

\item{n_clusters}{Integer defining the number of clusters. Defaults to 1.}
}
\value{
A list with components \code{n_items}, \code{alpha_shape}, \code{alpha_rate},
\code{cluster_concentration}, and \code{n_clusters}.
}
\description{
Set the hyperparameters for the Bayesian Mallows model. This function
creates a list of hyperparameter values that can be passed to
\code{\link[=compute_sequentially]{compute_sequentially()}}.
}
\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
  )
)
}