assess_convergence: Trace Plots from Metropolis-Hastings Algorithm

In BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

Trace Plots from Metropolis-Hastings Algorithm

Description

assess_convergence provides trace plots for the parameters of the Mallows Rank model, in order to study the convergence of the Metropolis-Hastings algorithm.

Usage

assess_convergence(model_fit, ...)

## S3 method for class 'BayesMallows'
assess_convergence(
  model_fit,
  parameter = c("alpha", "rho", "Rtilde", "cluster_probs", "theta"),
  items = NULL,
  assessors = NULL,
  ...
)

## S3 method for class 'BayesMallowsMixtures'
assess_convergence(
  model_fit,
  parameter = c("alpha", "cluster_probs"),
  items = NULL,
  assessors = NULL,
  ...
)

Arguments

model_fit	A fitted model object of class BayesMallows returned from compute_mallows() or an object of class BayesMallowsMixtures returned from compute_mallows_mixtures().
...	Other arguments passed on to other methods. Currently not used.
parameter	Character string specifying which parameter to plot. Available options are "alpha", "rho", "Rtilde", "cluster_probs", or "theta".
items	The items to study in the diagnostic plot for rho. Either a vector of item names, corresponding to model_fit$data$items or a vector of indices. If NULL, five items are selected randomly. Only used when parameter = "rho" or parameter = "Rtilde".
assessors	Numeric vector specifying the assessors to study in the diagnostic plot for "Rtilde".

Examples

set.seed(1)
# Fit a model on the potato_visual data
mod <- compute_mallows(setup_rank_data(potato_visual))
# Check for convergence
assess_convergence(mod)
assess_convergence(mod, parameter = "rho", items = 1:20)

BayesMallows documentation built on Sept. 11, 2024, 5:31 p.m.