mcsm (version 1.0)

sqar: 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

T

Number of MCMC iterations

multies

Boolean variable determining whether or not parallel chains are simulated

outsave

Boolean variable determining whether or not the MCMC output is saved

npara

Number of parallel chains

Value

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

References

Chapter 8 of EnteR Monte Carlo Statistical Methods

See Also

sqaradap

Examples

ousqar=sqar(outsave=TRUE)