BayesMallows (version 1.1.0)

smc_mallows_new_users_complete: SMC-Mallows New Users Complete

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_complete(
  R_obs,
  n_items,
  metric,
  leap_size,
  N,
  Time,
  mcmc_kernel_app,
  num_new_obs,
  alpha_prop_sd,
  lambda,
  alpha_max,
  logz_estimate = NULL,
  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

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 specifying the standard deviation of the lognormal proposal distribution used for \(\alpha\) in the Metropolis-Hastings algorithm. Defaults to 0.1.

lambda

Strictly positive numeric value specifying the rate parameter of the truncated exponential prior distribution of \(\alpha\). Defaults to 0.1. When n_cluster > 1, each mixture component \(\alpha_{c}\) has the same prior distribution.

alpha_max

Maximum value of alpha in the truncated exponential prior distribution.

logz_estimate

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

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

Examples

# NOT RUN {
# Generate basic elements
data <- sushi_rankings[1:100, ]
n_items <- ncol(sushi_rankings)
metric <- "footrule"
num_new_obs <- 10
logz_estimate <- estimate_partition_function(
	method = "importance_sampling",
	alpha_vector = seq(from = 0, to = 15, by = 0.1),
	n_items = n_items, metric = metric, nmc = 1e2, degree = 10
)

# Calculating rho and alpha samples
samples <- smc_mallows_new_users_complete(
	R_obs = data, n_items = n_items, metric = metric,
	leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs,
	mcmc_kernel_app = 5, logz_estimate = logz_estimate,
	alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6,
	num_new_obs = num_new_obs, verbose = TRUE
)

# Studying the structure of the output
str(samples)
# }