BayesMallows (version 1.1.0)

smc_mallows_new_users_partial: SMC-Mallows new users partial

Description

Function to perform resample-move SMC algorithm where we receive new users with complete rankings at each time step

Usage

smc_mallows_new_users_partial(
  R_obs,
  n_items,
  metric,
  leap_size,
  N,
  Time,
  logz_estimate,
  mcmc_kernel_app,
  num_new_obs,
  alpha_prop_sd,
  lambda,
  alpha_max,
  aug_method,
  verbose = FALSE
)

Arguments

R_obs

Matrix containing the full set of observed rankings of size n_assessors by n_items

n_items

Integer is the number of items in a ranking

metric

A character string specifying the distance metric to use in the Bayesian Mallows Model. Available options are "footrule", "spearman", "cayley", "hamming", "kendall", and "ulam".

leap_size

leap_size Integer specifying the step size of the leap-and-shift proposal distribution

N

Integer specifying the number of particles

Time

Integer specifying the number of time steps in the SMC algorithm

logz_estimate

Estimate of the partition function, computed with estimate_partition_function in the BayesMallow R package estimate_partition_function.

mcmc_kernel_app

Integer value for the number of applications we apply the MCMC move kernel

num_new_obs

Integer value for the number of new observations (complete rankings) for each time step

alpha_prop_sd

Numeric value of the standard deviation of the prior distribution for alpha

lambda

Strictly positive numeric value specifying the rate parameter of the truncated exponential prior distribution of alpha.

alpha_max

Maximum value of alpha in the truncated exponential prior distribution.

aug_method

A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"

verbose

Logical specifying whether to print out the progress of the SMC-Mallows algorithm. Defaults to FALSE.

Value

a set of particles each containing a value of rho and alpha