plot.KbMCMC {EntropyMCMC} | R Documentation |
Plot sequences of estimates of Kullback distance or Entropy against iterations
Description
This S3 method for plot
plots by default sequences of estimates of
the Kullback distance
between the (estimated) pdf of the MCMC algorithm at time
,
, and the target density
,
for
up to the number of iterations that have been provided/computed.
It can also plot the first term in the Kullback distance, i.e.
the Entropy
.
Its argument is an object of class
KbMCMC
such as the one returned by, e.g., EntropyMCMC
.
Usage
## S3 method for class 'KbMCMC'
plot(x, Kullback = TRUE, lim = NULL, ylim = NULL,
new.plot = TRUE, title = NULL, ...)
Arguments
x |
An object of class |
Kullback |
|
lim |
for zooming over |
ylim |
|
new.plot |
set to |
title |
The title; if |
... |
Further parameters passed to |
Value
The graphic to plot.
Author(s)
Didier Chauveau.
References
Chauveau, D. and Vandekerkhove, P. (2012), Smoothness of Metropolis-Hastings algorithm and application to entropy estimation. ESAIM: Probability and Statistics, 17, (2013) 419–431. DOI: http://dx.doi.org/10.1051/ps/2012004
Chauveau D. and Vandekerkhove, P. (2014), Simulation Based Nearest Neighbor Entropy Estimation for (Adaptive) MCMC Evaluation, In JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. 2816–2827.
Chauveau D. and Vandekerkhove, P. (2014), The Nearest Neighbor entropy estimate: an adequate tool for adaptive MCMC evaluation. Preprint HAL http://hal.archives-ouvertes.fr/hal-01068081.
See Also
Examples
## See the EntropyMCMC Examples.