is.stationary {LaplacesDemon}  R Documentation 
Logical Check of Stationarity
Description
This function returns TRUE
if the object is stationary
according to the Geweke.Diagnostic
function, and
FALSE
otherwise.
Usage
is.stationary(x)
Arguments
x 
This is a vector, matrix, or object of class

Details
Stationarity, here, refers to the limiting distribution in a Markov chain. A series of samples from a Markov chain, in which each sample is the result of an iteration of a Markov chain Monte Carlo (MCMC) algorithm, is analyzed for stationarity, meaning whether or not the samples trend or its moments change across iterations. A stationary posterior distribution is an equilibrium distribution, and assessing stationarity is an important diagnostic toward inferring Markov chain convergence.
In the cases of a matrix or an object of class demonoid
, all
Markov chains (as column vectors) must be stationary for
is.stationary
to return TRUE
.
Alternative ways to assess stationarity of chains are to use the
BMK.Diagnostic
or Heidelberger.Diagnostic
functions.
Value
is.stationary
returns a logical value indicating whether or not
the supplied object is stationary according to the
Geweke.Diagnostic
function.
Author(s)
Statisticat, LLC. software@bayesianinference.com
See Also
BMK.Diagnostic
,
Geweke.Diagnostic
,
Heidelberger.Diagnostic
, and
LaplacesDemon
.
Examples
library(LaplacesDemon)
is.stationary(rnorm(100))
is.stationary(matrix(rnorm(100),10,10))