Download
bayesian data analysis 1 using bugs 2 and r 3 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Bayesian data analysis 1 using Bugs 2 and R 3 PowerPoint Presentation
Download Presentation
Bayesian data analysis 1 using Bugs 2 and R 3

Bayesian data analysis 1 using Bugs 2 and R 3

537 Views Download Presentation
Download Presentation

Bayesian data analysis 1 using Bugs 2 and R 3

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Bayesian data analysis1 using Bugs2 and R3 1Fit complicated models 2clearly 3display and understand results

  2. Bayesian data analysisusing BUGS and R Prof. Andrew Gelman Dept. of Statistics and Political Science Columbia University Joint Program in Survey Methodology, University of Maryland, College Park January 17-18, 2008

  3. Introduction to the course

  4. Goals • Should be able to… • Fit multilevel models and display and understand the results • Use Bugs from R • Theoretical • Connection between classical linear models and GLM, multilevel models, and Bayesian inference • Practical • Dealing with data and graphing in R • Reformatting models in Bugs to get them to work

  5. Realistic goals • You won’t be able to go out and fit Bayesian models • But . . . you’ll have a sense of what Bayesian data analysis “feels like”: • Model building • Model fitting • Model checking

  6. Overview • What is Bayesian data analysis? Why Bayes? • Why R and Bugs [schools example] • Hierarchical linear regression [radon example] • Bayesian data analysis: what it is and what it is not [several examples] • Model checking [several examples] • Hierarchical Poisson regression [police stops example] • Understanding the Gibbs sampler and Metropolis algorithm • Troubleshooting: simulations don’t work, run too slowly, make no sense • Advanced computation [social networks example] • Accounting for design of surveys and experiments [polling example] • Prior distributions [logistic-regression separation example] • If there is time: hierarchical logistic regression with varying intercepts and slopes [red/blue states example] • Loose ends

  7. Structure of this course • Main narrative on PowerPoint • Formulas on blackboard • On my computer and yours: • Bugs, R, and a text editor • Data, Bugs models, and R scripts for the examples • We do computer demos together • Pause to work on your own to play with the models—we then discuss together • Lecturing for motivating examples • Interrupt to ask questions

  8. Structure of this course • Several examples. For each: • Set up the Bugs model • Use R to set up the data and initial values, and to run Bugs • Use R to understand the inferences • Sometimes fit model directly in R • Play with variations on your own computer • Understanding how Bugs works and how to work with Bugs • More examples • Working around problems in Bugs • Using the inferences to answer real questions • Not much theory. If you want more theory, let me know! • Emphasis on general methods for model construction, expansion, patching, and understanding

  9. Introduction to Bayes

  10. What is Bayesian data analysis? Why Bayes? • Effective and flexible • Modular (“Tinkertoy”) approach • Combines information from different sources • Estimate inferences without overfitting for models with large number of parameters • Examples from sample surveys and public health include: • Small-area estimation, longitudinal data analysis, and multilevel regression

  11. What are BUGS and R? • Bugs • Fit Bayesian statistical models • No programming required • R • Open source language for statistical computing and graphics • Bugs from R • Offers flexibility in data manipulation before the analysis and display of inferences after • Avoids tedious issues of working with Bugs directly [Open R: schools example]

  12. Begin 8-schools example

  13. Open R: schools example • Open R: • setwd (“c:/bugs.course/schools”) • Open RWinEdt • Open file “schools/schools.R” • Open file “schools.bug” • Click on Window, Tile Horizontally • Copy the commands from schools.R and paste them into the R window

  14. 8 schools example • Motivates hierarchical (multilevel) modeling • Easy to do using Bugs • Background: educational testing experiments • Classical analyses (load data into R) • No pooling • Complete pooling • Hierarchical analysis using Bugs • Talk through the 8-schools model • Fit the model from R • Look at the inferences, compare to classical

  15. Playing with the 8 schools • Try it with different n.chains, n.iter • Different starting values • Changing the data values y • Changing the data variances sigma.y • Changing J=#schools • Changing the model

  16. End 8-schools example

  17. A brief prehistory of Bayesian data analysis • Bayes (1763) • Links statistics to probability • Laplace (1800) • Normal distribution • Many applications, including census [sampling models] • Gauss (1800) • Least squares • Applications to astronomy [measurement error models] • Keynes, von Neumann, Savage (1920’s-1950’s) • Link Bayesian statistics to decision theory • Applied statisticians (1950’s-1970’s) • Hierarchical linear models • Applications to animal breeding, education [data in groups]

  18. A brief history of Bayesian data analysis, Bugs, and R • “Empirical Bayes” (1950’s-1970’s) • Estimate prior distributions from data • Hierarchical Bayes (from 1970) • Include hyperparameters as part of the full model • Markov chain simulation (from 1940’s [physics] and 1980’s [statistics]) • Computation with general probability models • Iterative algorithms that give posterior simulations (not point estimates) • Bugs (from 1994) • Bayesian inference Using Gibbs Sampling (David Spiegelhalter et al.) • Can fit general models with modular structure • S (from 1980’s) • Statistical language with modeling, graphics, and programming • S-Plus (commercial version) • R (open-source) • lme() and lmer() functions by Doug Bates for fitting hierarchical linear and generalized linear models

  19. My own experiences with Bayes, Bugs, and R • Bayesian methods really work, especially for models with lots of parameters • Use R for data manipulations and various preprogrammed models • Use Bugs (from R) as first try in fitting complex models • Use R to make graphs to check that fitted model makes sense • When Bugs doesn’t work, program in R directly

  20. Bayesian regression using Bugs

  21. R and Bugs for classical inference • Radon example • Fitting a linear regression in Bugs • Displaying the results in R • Complete-pooling and no-pooling models • Model extensions [open R: radon example]

  22. Begin radon example

  23. Open R: radon example • In RWinEdt • Open file “radon/radon_setup.R” • We’ll talk through the code • In R: • setwd (“c:/bugs.course/radon”) • source (“radon_setup.R”) • Regression output will appear on the R console and graphics windows

  24. Fitting a linear regression in R and Bugs • The Bugs model • Setting up data and initial values in R • Running Bugs and checking convergence • Displaying the fitted line and residuals in R

  25. Radon example in R(complete pooling) • In RWinEdt • Open file “radon/radon.classical1.R” • In other window, open file “radon.classical.bug” • Copy the commands (one paragraph at a time) from radon.classical1.R and paste them into the R window • Fit the model • Plot the data and estimates • Plot the residuals (two versions) • Label the plot

  26. Simple extensions of the radon model • Separate variances for houses with and without basements • t instead of normal errors • Fitting each model, then understanding it

  27. Radon regression with 2 variance parameters • Separate variances for houses with and without basements • Bugs model • Setting it up in R • Using the posterior inferences to compare the two variance parameters

  28. Radon example in R (regression with 2 variance parameters) • In RWinEdt • Open file “radon/radon.extend.1.R” • Other window, open file “radon.classical.2vars.bug” • Copy from radon.extend.1.R and paste into the R window • Fit the model • Display a posterior inference • Compute a posterior probability • STOP

  29. Robust regression for radon data • t instead of normal errors • Issues with the degrees-of-freedom parameter • Looking at the posterior simulations and comparing to the prior distribution • Interpreting the scale parameter

  30. Radon example in R (robust regression using the t model) • In RWinEdt • Stay with “radon/radon.extend.1.R” • Other window: “radon.classical.t.bug” • Copy rest of radon.extend.1.R and paste into the R window • Fit the t model • Run again with n.iter=1000 iterations • Make some plots

  31. Fit your own model to the radon data • Alter the Bugs model • Change the setup in R • Run the model and check convergence • Display the posterior inferences in R [Suggestions of alternative models?]

  32. Fitting several regressions in R and Bugs • Back to the radon example • Complete pooling [we just did] • No pooling [do now: see next slide] • Bugs model is unchanged • Fit separate model in each county and save them as a list • Displaying data and fitted lines from 11 counties • Next step: hierarchical regression

  33. Radon example in R (no pooling) • In RWinEdt • Open file “radon/radon.classical2.R” • In other window, open file “radon.classical.bug” • Copy from radon.classical2.R and paste into the R window • Fit the model (looping through n.county=11 counties) • Plot the data and estimates • Plot the residuals (two versions) • Label the plot

  34. Hierarchical linear regression: models [Show the models on blackboard] • Generalizing the Bugs model • Varying intercepts • Varying intercepts, varying slopes • Adding predictors at the group level • Also write each model using algebra • More than one way to write (or program) each model

  35. Hierarchical linear regression: fitting and understanding [working on blackboard] • Displaying results in R • Comparing models • Interpreting coefficients and variance parameters • Going beyond R-squared

  36. Hierarchical linear regression: varying intercepts • Example: county-level variation for radon • Recall classical models • Complete pooling • No pooling (separate regressions) • Including county effects hierarchically • Bugs model • Written version • More than one way to write the model • Fitting and understanding • Interpreting the parameters • Displaying results in R

  37. Radon example in Bugs and R (varying intercepts) • In RWinEdt • “radon/radon.multilevel.1.R” • Other window: “radon.multilevel.1.bug” • Copy from radon.multilevel.1.R and paste into the R window • Fit the model (debug=TRUE) • Close the Bugs window • Run for 100, then 1000 iterations • Plot the data and estimates for 11 counties • Plot all 11 regression lines at once

  38. Fitting the radon model using lmer() • lmer(): an R function for multilevel (hierarchical) regression and glm • Not as flexible as Bugs • Has problems when group-level variance parameters are near zero (as in 8-schools example) • But . . . is easy to use and pretty fast • (Similar routines available in Stata and SAS)

  39. Radon example using lmer() (varying intercepts) • In RWinEdt • “radon/radon.multilevel.lmer.1.R” • Copy from radon.multilevel.lmer.1.R and paste into the R window • Fit the model using lmer() • Display the results • Pull out the intercepts and slopes • Plot the data and estimates for 11 counties • Get posterior simulations and compare to Bugs

  40. Hierarchical linear regression: varying intercepts, varying slopes • The model • Bugs model • Written version • Setup in R • Running Bugs, checking convergence • Displaying the fitted model in R • Understanding the new parameters

  41. Radon example in R (varying intercepts and slopes) • In RWinEdt • “radon/radon.multilevel.2.R” • Other window: “radon.multilevel.2.bug” • Copy from radon.multilevel.2.R and paste into the R window • Fit the model (debug=TRUE) • Close the Bugs window • Run for 500 iterations • Plot the data and estimates for 11 counties • Plot all 11 regression lines at once • Plot slopes vs. intercepts

  42. Radon example using lmer() (varying intercepts and slopes) • In RWinEdt • “radon/radon.multilevel.lmer.2.R” • Copy from radon.multilevel.lmer.2.R and paste into the R window • Fit the model using lmer() • Display the results • Play around with centering and rescaling of x • Pull out the intercepts and slopes • Plot the data and estimates for 11 counties • Get posterior simulations and compare to Bugs

  43. Structure of lmer() • Data are grouped, can have nonnested groups (see book for examples) • Coefficients can be “fixed” (not varying by group) or “random” (varying by group) • lmer (y ~ fixed + (random | group)) • Also glm version: lmer (y ~ fixed + (random | group), family=binomial (link=“logistic”))

  44. Simple lmer() examples • From R window • ?mcsamp • Scroll to the bottom of the help window and paste the example into the R window • Simulate some fake multilevel data • Fit and display a varying-intercept model • Varying-intercept, varying-slope model • Logistic model: varying intercept • Logistic: varying-intercept, varying-slope • Non-nested varying-intercept, varying-slope model

  45. Playing with hierarchical modeling for the radon example • Uranium level as a county-level predictor • Changing the sample sizes • Perturbing the data • Fitting nonlinear models

  46. Radon example in R (adding a county-level predictor) • In RWinEdt • radon/radon.multilevel.3.R • Other window: radon.multilevel.3.bug • Copy from radon.multilevel.3.R and paste into the R window • Fit the model • Plot the data and estimates for 11 counties • Plot all 11 regression lines at once • Plot county parameters vs. county uranium level

  47. Varying intercepts and slopes with correlated group-level errors • The model • Statistical notation • Bugs model • Show on blackboard • Correlation and group-level predictors • More in chapters 13 and 17 of Gelman and Hill (2006)

  48. End radon example

  49. Bayesian data analysis: what it is and what it is not

  50. Bayesian data analysis: what it is and what it is not • Popular view of Bayesian statistics • Subjective probability • Elicited prior distributions • Bayesian data analysis as we do it • Hierarchical modeling • Many applications • Conceptual framework • Fit a probability model to data • Check fit, ride the model as far as it will take you • Improve/expand/extend model