heidel.diag: Heidelberger and Welch's convergence diagnostic
In coda: Output Analysis and Diagnostics for MCMC

Description Usage Arguments Details Theory Note References

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.

1	heidel.diag(x, eps=0.1, pvalue=0.05)

`x`	An `mcmc` object
`eps`	Target value for ratio of halfwidth to sample mean
`pvalue`	significance level to use

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.

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.

If the half-width test fails then the run should be extended. In order to avoid problems caused by sequential testing, the test should not be repeated too frequently. Heidelberger and Welch (1981) suggest increasing the run length by a factor I > 1.5, each time, so that estimate has the same, reasonably large, proportion of new data.

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).

coda documentation built on Oct. 24, 2020, 9:10 a.m.