run_gender_mcmc: Run the BTUN with Gender Effect MCMC algorithm

In jasa-btun-anon/BTUN: BTUN: Bradley--Terry for Urban Networks

Description

This function runs the BTUN with Gender Effect MCMC algorithm

Usage

run_gender_mcmc(
  n.iter,
  delta,
  k.mean,
  k.chol,
  male.win.matrix,
  female.win.matrix,
  f.initial,
  g.initial
)

Arguments

n.iter	The number of iterations to be run
delta	The underrlaxed tuning parameter must be in (0, 1)
k.mean	The GP prior mean vector
k.chol	The cholesky decomposition of the GP prior covariance matrix, alpha must be set to 1 when constructing ths
male.win.matrix	A matrix, where w_ij give the number of times area i beat j when judged by men
female.win.matrix	A matrix, where w_ij give the number of times area i beat j when judged by women
f.initial	A vector of the intial esitmate for f, the male function
g.initial	A vector of the intial esitmate for g, the discrepancy functon

Value

A list of MCMC output

• f.matrix - A matrix containing the each iteration of f

• g.matrix - A matrix containing the each iteration of g

• alpha.sq - A matrix containing the iterations of alpha^2

• accpetance.rate - The acceptance rate for f and g

• time.taken - Time tkane to run the MCMC algorithm in seconds

Examples

n.iter <- 10
delta <- 0.1
k.mean <- c(0, 0, 0)
k.chol <- diag(3)
men.comparisons <- data.frame("winner" = c(1, 3, 2, 2), "loser" = c(3, 1, 1, 3))
women.comparisons <- data.frame("winner" = c(1, 2, 1, 2), "loser" = c(3, 1, 3, 3))
men.win.matrix <- comparisons_to_matrix(3, men.comparisons)
women.win.matrix <- comparisons_to_matrix(3, women.comparisons)
f.initial <- c(0, 0, 0)
g.initial <- c(0, 0, 0)

mcmc.output <- run_gender_mcmc(n.iter, delta, k.mean, k.chol, men.win.matrix,
    women.win.matrix, f.initial, g.initial)

jasa-btun-anon/BTUN documentation built on Sept. 12, 2020, 12:54 a.m.