run_mcmc_with_ordering: Run the BTUN MCMC algorithm with ordering constraints

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

Description

This function runs the BTUN mcmc algorithm with ordering constraints. The constraints are included using a list of sets.

Usage

run_mcmc_with_ordering(
  n.iter,
  delta,
  k.mean,
  k.chol,
  win.matrix,
  f.initial,
  S,
  alpha = FALSE
)

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
win.matrix	A matrix, where w_ij give the number of times area i beat j
f.initial	A vector of the intial esitmate for f
S	A list of ordering constraints. There are four elements in each set, the label of the two areas, the value of the constaint, and the confidence parameter.
alpha	A boolean if inference for alpha should be carried out

Value

A list of MCMC output

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

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

• accpetance.rate - The acceptance rate for f

• 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)
comparisons <- data.frame("winner" = c(1, 3, 2, 2), "loser" = c(3, 1, 1, 3))
win.matrix <- comparisons_to_matrix(3, comparisons)
f.initial <- c(0, 0, 0)
S <- list()
S[[1]] <- c(1, 3, -1, 3)
S[[2]] <- c(1, 2, -1, 3)
mcmc.output <- run_mcmc_with_ordering(n.iter, delta, k.mean, k.chol, win.matrix, f.initial, S)

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