PAMA (version 0.1.1)

PAMA.Cov: This function implements Bayesian inference of PAMA model with covariates.

Description

This function implements Bayesian inference of PAMA model with covariates.

Usage

PAMA.Cov(datfile, Covdatfile, nRe, iter)

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.

Covdatfile

A matrix or dataframe. Each column denotes a covariate.

nRe

A number. Number of relevant entities

iter

A number. Numner of iterations of MCMC. Defaulted as 1000.

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.

5. theta.mat: posterior samples of coefficients of covariates.

Details

The covariates are incoporated in the PAMA framework as indicators of groupmember. That is covariates are associated to group members via a logistic regression.

Examples

# NOT RUN {
a=NBANFL()
PAMA.Cov(t(a$NFLdata),a$NFLcov,nRe=10,iter=10)
# }
# NOT RUN {
PAMA.Cov(t(a$NFLdata),a$NFLcov,nRe=10,iter=1000)
# }