R: Logical Check of Stationarity

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 `demonoid`.

### 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@bayesian-inference.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))

[Package LaplacesDemon version 16.1.1 Index]