deburn: De-Burn

In LaplacesDemon: Complete Environment for Bayesian Inference

Description

The deburn function discards or removes a user-specified number of burn-in iterations from an object of class demonoid.

Usage

deburn(x, BurnIn=0)

Arguments

x	This is an object of class demonoid.
BurnIn	This argument defaults to BurnIn=0, and accepts an integer that indicates the number of iterations to discard as burn-in.

Details

Documentation for the burnin function provides an introduction to the concept of burn-in as it relates to Markov chains.

The deburn function discards a number of the first posterior samples, as specified by the BurnIn argument. Stationarity is not checked, because it is assumed the user has a reason for using the deburn function, rather than using the results from the object of class demonoid. Therefore, the posterior samples in Posterior1 and Posterior2 are identical, as are Summary1 and Summary2.

Value

The deburn function returns an object of class demonoid.

Author(s)

Statisticat, LLC. [e-mail]

See Also

burnin and LaplacesDemon.

Examples

### Assuming the user has Fit which is an object of class demonoid:
#library(LaplacesDemon)
#Fit2 <- deburn(Fit, BurnIn=100)

LaplacesDemon documentation built on July 9, 2021, 5:07 p.m.