heidel.diag is a run length control diagnostic based on a criterion
of relative accuracy for the estimate of the mean. The default setting
corresponds to a relative accuracy of two significant digits.
heidel.diag also implements a convergence diagnostic, and removes
up to half the chain in order to ensure that the means are estimated
from a chain that has converged.
heidel.diag(x, eps=0.1, pvalue=0.05)An mcmc object
Target value for ratio of halfwidth to sample mean
significance level to use
The heidel.diag diagnostic is based on the work of Heidelberger
and Welch (1983), who combined their earlier work on simulation run
length control (Heidelberger and Welch, 1981) with the work of Schruben
(1982) on detecting initial transients using Brownian bridge theory.
The convergence test uses the Cramer-von-Mises statistic to test the null hypothesis that the sampled values come from a stationary distribution. The test is successively applied, firstly to the whole chain, then after discarding the first 10%, 20%, … of the chain until either the null hypothesis is accepted, or 50% of the chain has been discarded. The latter outcome constitutes `failure' of the stationarity test and indicates that a longer MCMC run is needed. If the stationarity test is passed, the number of iterations to keep and the number to discard are reported.
The half-width test calculates a 95% confidence interval for the
mean, using the portion of the chain which passed the stationarity test.
Half the width of this interval is compared with the estimate of the mean.
If the ratio between the half-width and the mean is lower than eps,
the halfwidth test is passed. Otherwise the length of the sample is
deemed not long enough to estimate the mean with sufficient accuracy.
Heidelberger P and Welch PD. A spectral method for confidence interval generation and run length control in simulations. Comm. ACM. 24, 233-245 (1981)
Heidelberger P and Welch PD. Simulation run length control in the presence of an initial transient. Opns Res., 31, 1109-44 (1983)
Schruben LW. Detecting initialization bias in simulation experiments. Opns. Res., 30, 569-590 (1982).