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R2WinBUGS: Using R for Bayesian Analysis

R2WinBUGS: Using R for Bayesian Analysis. Matthew Russell Rongxia Li 2 November 2010 2010 Northeastern Mensurationists Meeting. Bayesian ideologies 8 (aka what UMaine students needed to learn to pass Bill Halteman’s MAT500 course) . P( H| Y ), not P( Y |H )

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R2WinBUGS: Using R for Bayesian Analysis

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  1. R2WinBUGS: Using R for Bayesian Analysis Matthew Russell Rongxia Li 2 November 2010 2010 Northeastern Mensurationists Meeting

  2. Bayesian ideologies8(aka what UMaine students needed to learn to pass Bill Halteman’s MAT500 course) • P(H|Y), not P(Y|H) • Probability is the likelihood of an event occurring • Prior knowledge can be incorporated • Model parameters are random variables

  3. Benefits • Posterior distributions generated for model parameters • Statistics can be computed (e.g. mean, median, mode) • A formal distribution does not need to be assumed • Uncertainty of model parameters can be directly assessed • Models easily updated with new data • Consider old model as a prior

  4. bayes* AND [forestry OR silvic*] Web of Science results:

  5. WinBUGS • Software for Bayesian analysis using Markov chain Monte Carlo methods • Standard GUI • http://www.mrc-bsu.cam.ac.uk/bugs/ • OpenBUGS • Open source version of BUGS • Future development will be with OpenBUGS • www.openbugs.info

  6. R packages • R2WinBUGS • Writes data and scripts in R and calls WinBUGS • Useful for: • running multiple datasets • changing model specifications • Results can be provided in R • BRugs • Collection of functions that allow graphical analysis

  7. Example: Penobscot Experimental Forest snag data n=1,009

  8. Snag survival • Probability of snag survival a function of time since tree death (Garber et al. 2005) • Seven species examined • BF, RS, EH, WS, WC, PB, RM

  9. .bug file model{ for(i in 1:3) {b[i]~dnorm(0,1.0E-6)} prec~dgamma(0.001,0.001) for(i in 1:numTrees) { preds[i]<-1/(b[1]+b[2]*pow((t[i]),b[3])) Psurv[i]~dnorm(preds[i],prec) } } Specify priors Loop through trees

  10. samplesHistory (‘*’)

  11. samplesDensity(‘*’)

  12. samplesStats (‘*’)

  13. Assessing results from BUGS (from McCarthy 2007) • samplesHistory • If it is not white noise, it might be autocorrelation • Are samplesDensity truncated? • Priors might be inadequate • Bumpy samplesDensity? • Consider more samples • Specify different initial values… do you get the same results? • Consider Gelman-Rubin statistic • Measures the influence of specifying initial values

  14. Other OS and other programs • WinBUGS can be run on Linux/Unix/Mac through Wine • But JAGS (Just Another Gibbs Sampler) might work better for Linux users • http://www-fis.airc.fr/~martyn/software/jags/ • Macros available for SAS/Excel • Matlab-WinBUGS GUI available

  15. Sources http://www.mrc-bsu.cam.ac.uk/bugs/ WinBUGS R2WinBUGS OpenBUGS BRugs JAGS McCarthy, M.A. 2007. Bayesian methods for ecology. Cambridge Univ. Press. 296 pp. Gelman et al. 2004. Bayesian data analysis. Chapman Hall/CRC. 696 pp. Albert, J. 2009. Bayesian computation with R. Springer. 300 pp.

  16. Summary • Plenty of tools available for Bayesian analysis with R • All are open source • Coding is not terribly complex • Lots of measures for assessing results • Graphical • Empirical • R2WinBUGS as a learning tool for Bayesian methods

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