Provides tools for computing Monte Carlo standard
errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
computation for expectation and quantile estimators is
supported. The package also provides functions for computing
effective sample size and for plotting Monte Carlo estimates
versus sample size.
Details
Package:
mcmcse
Type:
Package
Version:
1.1-2
Date:
2015-08-17
License:
GPL (>= 2)
Author(s)
James M. Flegal <jflegal@ucr.edu>,
John Hughes <hughesj@umn.edu> and
Dootika Vats <vatsx007@umn.edu>
Maintainer: James M. Flegal <jflegal@ucr.edu>
References
Flegal, J. M. (2012) Applicability of subsampling
bootstrap methods in Markov chain Monte Carlo. In
Wozniakowski, H. and Plaskota, L., editors, Monte
Carlo and Quasi-Monte Carlo Methods 2010 (to appear).
Springer-Verlag.
Flegal, J. M. and Jones, G. L. (2010) Batch means and
spectral variance estimators in Markov chain Monte Carlo.
The Annals of Statistics, 38, 1034–1070.
Flegal, J. M. and Jones, G. L. (2011) Implementing Markov
chain Monte Carlo: Estimating with confidence. In Brooks,
S., Gelman, A., Jones, G. L., and Meng, X., editors,
Handbook of Markov Chain Monte Carlo, pages
175–197. Chapman & Hall/CRC Press.
Flegal, J. M., Jones, G. L., and Neath, R. (2012) Markov
chain Monte Carlo estimation of quantiles.
University of California, Riverside, Technical
Report.
Gong, L., and Flegal, J. M. A practical sequential stopping rule for high-dimensional Markov chain Monte Carlo. Journal of Computational and Graphical Statistics (to appear).
Jones, G. L., Haran, M., Caffo, B. S. and Neath, R.
(2006) Fixed-width output analysis for Markov chain Monte
Carlo. Journal of the American Statistical
Association, 101, 1537–1547.
Vats, D., Flegal, J. M., and, Jones, G. L Multivariate Output Analysis for Markov chain Monte Carlo, arXiv preprint arXiv:1512.07713 (2015).
Examples
library(mAr)
p <- 3
n <- 1e3
omega <- 5*diag(1,p)
## Making correlation matrix var(1) model
set.seed(100)
foo <- matrix(rnorm(p^2), nrow = p)
foo <- foo %*% t(foo)
phi <- foo / (max(eigen(foo)$values) + 1)
out <- as.matrix(mAr.sim(rep(0,p), phi, omega, N = n))
mcse(out[,1], method = "bart")
mcse.bm <- mcse.multi(x = out)
mcse.tuk <- mcse.multi(x = out, method = "tukey")