PAMA.B: This function implements Bayesian inference of PAMA model.

In PAMA: Rank Aggregation with Partition Mallows Model

Description

This function implements Bayesian inference of PAMA model.

Usage

PAMA.B(datfile, nRe, iter = 1000, init = "EMM")

Arguments

datfile	A matrix or dataframe. This is the data where our algorithm will work on. Each colomn denotes a ranker's ranking. The data should be in entity-based format.
nRe	A number. Number of relevant entities
iter	A number. Numner of iterations of MCMC
init	A string. This indicates which method is used to initiate the starting point of the aggregated ranking list. "mean" uses the sample mean. "EMM" uses the method from R package 'ExtMallows'.

Value

List. It contains Bayesian posterior samples of all the parameters and log-likelihood.

1. I.mat: posterior samples of I

2. phi.mat: posterior samples of phi

3. smlgamma.mat: posterior samples of gamma

4. l.mat: posterior samples of log-likelihood

Author(s)

Wanchuang Zhu, Yingkai Jiang, Jun S. Liu, Ke Deng

References

Wanchuang Zhu, Yingkai Jiang, Jun S. Liu, Ke Deng (2021) Partition-Mallows Model and Its Inference for Rank Aggregation. Journal of the American Statistical Association

Examples

dat=t(PerMallows::rmm(10,1:20,0.5))
PAMA.B(dat,10,iter=10)
PAMA.B(dat,10,iter=1000)

PAMA documentation built on May 6, 2021, 5:09 p.m.