PAMA.F: This function implements Maximum Likelihood estimation of...

In PAMA: Rank Aggregation with Partition Mallows Model

Description

This function implements Maximum Likelihood estimation of PAMA model.

Usage

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

Arguments

datfile	A matrix or dataframe. This is the data where our algorithm will work on. Each row denotes a ranker's ranking. The data should be in entity-based format.
nRe	A number. Number of relevant entities.
threshold	A number (positive). The stopping threshold in determining convergence of MLE. If the two consecutive iterations of log-likelihood is smaller than the threshold, then the convergence is satisfied.
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 MLE of all the parameters and log-likelihood. We use an iterative procedure to find the MLEs, so there are several values for each parameter until convergence.

1. I.mat: samples of I

2. phi.mat: samples of phi

3. smlgamma.mat: samples of gamma

4. l.mat: 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

a=NBANFL()
PAMA.F(a$NBA,nRe=10,threshold=0.1,iter=10)

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