R_hat: Compute Gelman-Rubin statistic

In RprobitB: Bayesian Probit Choice Modeling

Compute Gelman-Rubin statistic

Description

This function computes the Gelman-Rubin statistic R_hat.

Usage

R_hat(samples, parts = 2)

Arguments

samples	[numeric() | matrix] Samples from a Markov chain. If it is a matrix, each column gives the samples for a separate chain.
parts	[integer(1)] The number of parts to divide each chain into sub-chains.

Details

NA values in samples are ignored. The degenerate case is indicated by NA. The Gelman-Rubin statistic is bounded by 1 from below. Values close to 1 indicate reasonable convergence.

Value

The Gelman-Rubin statistic.

Examples

no_chains <- 2
length_chains <- 1e3
samples <- matrix(NA_real_, length_chains, no_chains)
samples[1, ] <- 1
Gamma <- matrix(c(0.8, 0.1, 0.2, 0.9), 2, 2)
for (c in 1:no_chains) {
  for (t in 2:length_chains) {
    samples[t, c] <- sample(1:2, 1, prob = Gamma[samples[t - 1, c], ])
  }
}
R_hat(samples)

RprobitB documentation built on Aug. 26, 2025, 1:08 a.m.