Illustration of some of coda's criterions on the noisy squared AR model

Description

This function illustrates some of coda's criterions on the noisy squared AR model, using a Metro-polis-Has-tings algorithm based on a random walk. Depending on the value of the boolean multies, those criterions are either the geweke.diag and heidel.diag diagnostics, along with a Kolmo-gorov-Smir-nov test of our own, or plot(mcmc.list()) if several parallel chains are produced together.

Usage

sqar(T = 10^4, multies = FALSE, outsave = FALSE, npara = 5)

Arguments

Value

This function produces plots and, if outsave is TRUE, it produces a list with value the MMC sample(s).

Author(s)

Christian P. Robert and George Casella

References

Chapter 8 of EnteR Monte Carlo Statistical Methods

See Also

sqaradap

Examples

ousqar=sqar(outsave=TRUE)

Results