Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudolikelihood augmentation.
smc_mallows_new_item_rank(
n_items,
R_obs,
metric,
leap_size,
N,
Time,
logz_estimate,
mcmc_kernel_app,
alpha_prop_sd,
lambda,
alpha_max,
aug_method,
verbose = FALSE
)Integer is the number of items in a ranking
3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps
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 Integer specifying the step size of the leap-and-shift proposal distribution
Integer specifying the number of particles
Integer specifying the number of time steps in the SMC algorithm
Estimate of the partition function, computed with
estimate_partition_function in the BayesMallow R package estimate_partition_function.
Integer value for the number of applications we apply the MCMC move kernel
Numeric value of the standard deviation of the prior distribution for alpha
Strictly positive numeric value specifying the rate parameter of the truncated exponential prior distribution of alpha.
Maximum value of alpha in the truncated exponential prior distribution.
A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"
Logical specifying whether to print out the progress of the
SMC-Mallows algorithm. Defaults to FALSE.
a 3d matrix containing the samples of rho and alpha from the SMC algorithm