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Lecture #4: Bayesian analysis of mapped data

Lecture #4: Bayesian analysis of mapped data. Spatial statistics in practice Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden. Topics for today’s lecture. Frequentist versus Bayesian perspectives. Implementing random effects models in GeoBUGS.

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Lecture #4: Bayesian analysis of mapped data

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  1. Lecture #4:Bayesian analysis of mapped data Spatial statistics in practice Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden

  2. Topics for today’s lecture • Frequentist versus Bayesian perspectives. • Implementing random effects models in GeoBUGS. • Spatially structured and unstructured random effects: the CAR, the ICAR, and the spatial filter specifications

  3. LMM & GLMM move in the direction of Bayesian modeling • Fixed effects are in keeping with a frequentist viewpoint―individual unknown parameters • Random effects are distributions of parameters, and are in keeping with a Bayesian viewpoint • The model specifications tend to be the same, with estimation methods tending to differ

  4. Sampling distribution-based inference: the reported p values • Frequentism: assigns probabilities to random events only according to their relative frequencies of occurrence, rejecting degree-of-belief interpretations of mathematical probability theory. • The frequentist approach considers  to be an unknown constant. Allowing for the possibility that  can take on a range of values, frequentist inference is based on a hypothetical repeated sampling principle that obtains desirable and (often) physically interpretable properties (e.g., CIs). • Frequentist statistics typically is limited to posing questions in terms of a null hypothesis (i.e., H0) that a parameter takes on a single value.

  5. What is Bayesian analysis? • Bayesianism: contends that mathematical probability theory pertains to degree of plausibility/belief; when used with Bayes theorem (which casts analysis in conditional terms), it becomes Bayesian inference. • The Bayesian approach considers  as the realized value of a random variable Θ with a probability density (mass) function called the prior distribution (a marginal probability distribution that is interpreted as a description of what is known about a variable in the absence of empirical/theoretical evidence). • Bayesian statistics furnishes a generic tool for inferring model parameter probability distribution functions from data: a parameter has a distribution, not a single value.

  6. The Bayesian interpretation of probability allows (proper prior) probabilities to be assigned subjectively to random events, in accordance with a researcher’s beliefs. • Proper prior: a probability distribution that is normalized to one, and asymptotically dominated by the likelihood function • improper prior: a handy analytic form (such as a constant or logarithmic distribution) that, when integrated over a parameter space, tends to 1 and so is not normalizable. • Noninformative prior: because no prior information is available, the selected prior contains vague/general information about a parameter, having minimal influence on inferences.

  7. conjugate prior: a prior probability distribution naturally connected with the likelihood function that, when combined with the likelihood and normalized, produces a posterior probability distribution that is of the same type as the prior, making the analytical mathematics of integration easy. (NOTE: MCMC techniques relaxes the need for this type of specification) • The hierarchical Bayes model is called “hierarchical” because it has two levels. At the higher level, hyper-parameter distributions are described by multivariate priors. Such distributions are characterized by vectors of means and covariance matrices; spatial autocorrelation is captured here. At the lower level, individuals’ behavior is described by probabilities of achieving some outcome that are governed by a particular model specification.

  8. posterior distribution: a conditional probability distribution for a parameter taking empirical evidence into account  computed with Bayes’ theorem as a normalized product of the likelihood function and the prior distributions  that supports Bayesian inference about the parameter • Bayesian statistics assumes that the probability distribution in question is known, and hence involves integration to get the normalizing constant. This integration might be tricky, and in many cases there is no analytical solution. With the advent of computers and various integration techniques, this problem can partially be overcome. In many Bayesian statistics applications a prior is tabulated and then sophisticated numerical integration techniques (e.g., MCMC) are used to derive posterior distributions.

  9. The controversy • A frequentist generally has no objection to the use of the Bayes formula, but when prior probabilities are lacking s/he deplores the Bayesians’ tendency to make them up out of thin air. • Does a parameter have one or many values? • For many (but not all) of the simpler problems where frequentist methodology seems to give satisfying answers, the Bayesian approach yields basically the same answers, provided noninformative proper priors are used.

  10. Impact of sample size prior distribution As the sample size increases, a prior distri-bution has less and less impact on results; BUT likelihood distribution effective sample size for spatially autocorrelated data

  11. What is BUGS? Bayesian inference Using Gibbs Sampling • is a piece of computer software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. • It grew from a statistical research project at the MRC BIOSTATISTICAL UNIT in Cambridge, but now is developed jointly with the Imperial College School of Medicine at St Mary’s, London.

  12. Classic BUGS WinBUGS (Windows Version) BUGS GeoBUGS (spatial models) PKBUGS (pharmokinetic modelling) • The Classic BUGS program uses text-based model description and a command-line interface, and versions are available for major computer platforms (e.g., Sparc, Dos). However, it is not being further developed.

  13. What is WinBUGS? • WinBUGS, a windows program with an option of a graphical user interface, the standard ‘point-and-click’ windows interface, and on-line monitoring and convergence diagnostics. It also supports Batch-mode running (version 1.4). • GeoBUGS, an add-on to WinBUGS that fits spatial models and produces a range of maps as output. • PKBUGS, an efficient and user-friendly interface for specifying complex population pharmacokinetic and pharmacodynamic (PK/PD) models within the WinBUGS software.

  14. What is GeoBUGS? • Available via http://www.mrc-bsu.cam.ac.uk/ bugs/winbugs/geobugs.shtml • Bayesian inference is used to spatially smooth the standardized incidence ratios using Markov chain Monte Carlo (MCMC) methods. GeoBUGS implements models for data that are collected within discrete regions (not at the individual level), and smoothing is done based on Markov random field models for the neighborhood structure of the regions relative to each other.

  15. Choice of Priors • Can effect • model convergence • execution speed • computational overflow errors • Consider • Conjugate prior • Sensible prior for the range of a parameter • e.g. Poisson mean must be positive; hence, a normal distribution is not a good prior. • Truncated Prior • useful if it is known that sensible or useful parameter values are within a particular range

  16. Parameter Initialization • Initialize important parameters. • Parameters distributed according to a non-informative prior must be initialized (otherwise an overflow error is very likely). GeoBUGs • Regions are numbered sequentially from 1 to n • Each region is defined as a polygon in a map file • Each region is associated with a unique index • BUGs can import map files from Arcinfo, Epimap, SPLUS.

  17. Review: What is MCMC? MCMC is used to simulate from some distribution p known only up to a constant factor, C: pi = Cqi where qi is known but C is unknown and too horrible to calculate. MCMC begins with conditional (marginal) distributions, and MCMC sampling outputs a sample of parameters drawn from their joint (posterior) distribution.

  18. GeoBugs:spatial Poisson with a map model; { for (i in 1:M){ for (j in 1:N){ #Poisson with a constant but unknown mean y[i,j] ~ dpois(l) #GeoBUGS numbers regions in a sequence #This statement turns a 2-D array into a 1-D array for GeoBUGS mapy[j + (i-1)N] <- y[I,j] } } #priors #Noninformative conjugate priors for Poisson mean. l~dgamma(0.001,0.001) }

  19. Displaying a map with GeoBugs • Compile model, load data, load initial conditions • Set sample monitor on desired variables • Set trace • Set sample monitor on map variable OR set summary monitor on map variable • Run chain • Activate map tool, load appropriate map • Set cut points, colour spectrum as desired.

  20. Poisson mixture on a spatial lattice Poisson mean varies x[i,j] in (0,1) x[i,j] independent binomials Map the hidden lattice and the Poisson means E(mu) = a/b; var(mu) = a/b2 a b

  21. Introducing a covariate Poisson mean varies with location Log link function Normal prior for covariate parameter

  22. Adding a random effect Random effect

  23. GeoBUGS Example NOTE: Possible mappings for the Scottish lip cancer data include b – area-specific random effects term mu – area-specific means RR – area-specific relative risks O – observed values E – expected values

  24. The Scottish lip cancer example Open a window in WinBUGS and insert the following items: - model - data - initial values #MODEL model { for (i in 1 : N) { O[i] ~ dpois(mu[i]) log(mu[i]) <- log(E[i]) + alpha0 + alpha1 * X[i]/10 + b[i] # Area-specific relative risk (for maps) RR[i] <- exp(alpha0 + alpha1 * X[i]/10 + b[i])} # CAR prior distribution for random effects: b[1:N] ~ car.normal(adj[], weights[], num[], tau) for(k in 1:sumNumNeigh) { weights[k] <- 1 } # Other priors: alpha0 ~ dflat() alpha1 ~ dnorm(0.0, 1.0E-5) tau ~ dgamma(0.5, 0.0005) # prior on precision sigma <- sqrt(1 / tau) # standard deviation }

  25. #DATA list(N = 56, O = c( 9, 39, 11, 9, 15, 8, 26, 7, 6, 20, 13, 5, 3, 8, 17, 9, 2, 7, 9, 7, 16, 31, 11, 7, 19, 15, 7, 10, 16, 11, 5, 3, 7, 8, 11, 9, 11, 8, 6, 4, 10, 8, 2, 6, 19, 3, 2, 3, 28, 6, 1, 1, 1, 1, 0, 0), E = c( 1.4, 8.7, 3.0, 2.5, 4.3, 2.4, 8.1, 2.3, 2.0, 6.6, 4.4, 1.8, 1.1, 3.3, 7.8, 4.6, 1.1, 4.2, 5.5, 4.4, 10.5, 22.7, 8.8, 5.6, 15.5, 12.5, 6.0, 9.0, 14.4, 10.2, 4.8, 2.9, 7.0, 8.5, 12.3, 10.1, 12.7, 9.4, 7.2, 5.3, 18.8, 15.8, 4.3, 14.6, 50.7, 8.2, 5.6, 9.3, 88.7, 19.6, 3.4, 3.6, 5.7, 7.0, 4.2, 1.8), X = c(16, 16, 10, 24, 10, 24, 10, 7, 7, 16, 7, 16, 10, 24, 7, 16, 10, 7, 7, 10, 7, 16, 10, 7, 1, 1, 7, 7, 10, 10, 7, 24, 10, 7, 7, 0, 10, 1, 16, 0, 1, 16, 16, 0, 1, 7, 1, 1, 0, 1, 1, 0, 1, 1, 16, 10),

  26. 43, 29, 25, 56, 32, 31, 24, 45, 33, 18, 4, 50, 43, 34, 26, 25, 23, 21, 17, 16, 15, 9, 55, 45, 44, 42, 38, 24, 47, 46, 35, 32, 27, 24, 14, 31, 27, 14, 55, 45, 28, 18, 54, 52, 51, 43, 42, 40, 39, 29, 23, 46, 37, 31, 14, 41, 37, 46, 41, 36, 35, 54, 51, 49, 44, 42, 30, 40, 34, 23, 52, 49, 39, 34, 53, 49, 46, 37, 36, 51, 43, 38, 34, 30, 42, 34, 29, 26, 49, 48, 38, 30, 24, 55, 33, 30, 28, 53, 47, 41, 37, 35, 31, 53, 49, 48, 46, 31, 24, 49, 47, 44, 24, 54, 53, 52, 48, 47, 44, 41, 40, 38, 29, 21, 54, 42, 38, 34, 54, 49, 40, 34, 49, 47, 46, 41, 52, 51, 49, 38, 34, 56, 45, 33, 30, 24, 18, 55, 27, 24, 20, 18 ), sumNumNeigh = 234) num = c(3, 2, 1, 3, 3, 0, 5, 0, 5, 4, 0, 2, 3, 3, 2, 6, 6, 6, 5, 3, 3, 2, 4, 8, 3, 3, 4, 4, 11, 6, 7, 3, 4, 9, 4, 2, 4, 6, 3, 4, 5, 5, 4, 5, 4, 6, 6, 4, 9, 2, 4, 4, 4, 5, 6, 5 ), adj = c( 19, 9, 5, 10, 7, 12, 28, 20, 18, 19, 12, 1, 17, 16, 13, 10, 2, 29, 23, 19, 17, 1, 22, 16, 7, 2, 5, 3, 19, 17, 7, 35, 32, 31, 29, 25, 29, 22, 21, 17, 10, 7, 29, 19, 16, 13, 9, 7, 56, 55, 33, 28, 20, 4, 17, 13, 9, 5, 1, 56, 18, 4, 50, 29, 16, 16, 10, 39, 34, 29, 9, 56, 55, 48, 47, 44, 31, 30, 27, 29, 26, 15,

  27. #INITIAL VALUES list(tau = 1, alpha0 = 0, alpha1 = 0, b=c(0,0,0,0,0,NA,0,NA,0,0, NA,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0))

  28. GeoBUGS Execution Instructions Step 1: open a new window in WinBUGS (this will be referred to as the user window) Step 2: enter the model syntax, data, and initial values, using the WinBUGS format, in the user window Step 3: select “Specification” in the “Model” pull down window Step 4: highlight “model” in the user window, appearing at the beginning of the model syntax, and click once on the “check model” button in the “Specification Tool” window NOTE: feedback from the program appears in the lower left-hand corner of the WinBUGS program window, and should be monitored

  29. Step 5: highlight “list” in the user window, appearing at the beginning of the data, and click once on the “load data” button in the “Specification Tool” window Step 6: insert the number of chains to be run (the default number is 1) in the “Specification Tool” window Step 7: click once on the “compile” button in the “Specification Tool” window Step 8: highlight “list” in the user window, appearing at the beginning of the initial values, and click once on the “load inits” button in the “Specification Tool” window (one set is needed for each chain to be run; clicking the “gen inits” button can be dangerous for sound analysis)

  30. Step 9: close the “Specification Tool” window Step 10: select “Samples” in the “Inference” pull down menu Step 11: in the “Sample Monitor Tool” window: type in the name (as it appears in the model syntax) of the 1st parameter to be monitored, and click once on the “set” button; type in the name of the 2nd parameter to be monitored, and click once on the “set” button; …; type in the name of the pth parameter to be monitored, and click once on the “set” button Step 12: close the “Sample Monitor Tool” window

  31. Step 13: select “Update” in the “Model” pull down menu Step 14: the default value in the “updates” box is 1000this value most likely needs to be substantially increased (say, to 50,000; once any desired changes are made, click once on the “updates” button Step 15: once the number appearing in the “iteration” box equals the number in the “updates” box, close the “Update Tool” window Step 16: select “Samples” in the “Inference” pull down menu Step 17: click on the down arrow to the right of the “node” box, and select the parameter to be monitored

  32. Step 18: click once of the “history” button to view the time-series plot for all iterations; click once on the “trace” plot to view the time-series plot for the last iterations; click once on the “auto cor” button to view the time-series correlogram (you may wish to enlarge the graphic in this window); click once on the “stats” button to view a parameter estimate’s statistics Step 19: close the “Sample Monitor Tool” window Step 20: select “Mapping Tool” from the “Map” pull down menu Step 21: select the appropriate map from the list appearing for the “Map” box when the down arrow to its right is clicked once (hint: your map that you imported from an Arc shapefile should appear here) Step 22: type the name of the variable (exactly as it appears in the model syntax) to be mapped in the “variable” box, and click once on the “plot” box

  33. Puerto Rico Binomial with Spatial Autocorrelation Example #MODEL model { for (i in 1 : N) { U[i] ~ dbin(p[i], T[i]) p[i] <- exp(alpha0 + b[i])/(1+exp(alpha0 + b[i])) } # CAR prior distribution for random effects: b[1:N] ~ car.normal(adj[], weights[], num[], tau) for(k in 1:sumNumNeigh) { weights[k] <- 1 } # Other priors: alpha0 ~ dflat() tau ~ dgamma(0.5, 0.0005) # prior on precision sigma <- sqrt(1 / tau) # standard deviation }

  34. #DATA list(N = 76, U = c(42527, 11048, 46236, 35859, 21330, 25584, 3792, 32126, 32389, 18664, 41997, 2839, 11787, 95880, 37713, 27605, 21499, 29709, 17200, 14688, 23829, 178792, 24196, 14767, 50242, 23848, 28462, 34650, 434374, 35130, 38583, 17412, 63929, 94085, 75728, 23852, 36971, 59572, 23364, 37238, 40919, 11062, 42042, 64685, 27305, 23077, 25387, 91593, 18346, 22105, 27850, 224044, 40875, 139445, 30886, 42467, 185703, 30071, 43707, 16671, 14262, 40457, 29802, 16800, 35270, 33421, 39958, 10176, 20682, 40395, 21087, 99850, 35476, 36201, 16472, 58848 ), T = c(44444, 17318, 50531, 36452, 26261, 34415, 11061, 34485, 32537, 19817, 45409, 6449, 12741, 98434, 39697, 29965, 23753, 29709, 23844, 20152, 26719, 186475, 25450, 14767, 52362, 25935, 31113, 37105, 434374, 40997, 44204, 21665, 63929, 94085, 75728, 35336, 37910, 61929, 27913, 39246, 46384, 19143, 42042, 64685, 29032, 26493, 28348, 100131, 19117, 22322, 28909, 224044, 46911, 140502, 35244, 43335, 186076, 30071, 47370, 18004, 19811, 42753, 37597, 20002, 36867, 34017, 40712, 12367, 21888, 44301, 23072, 100053, 36743, 38925, 16614, 59035 ), num = c(4, 3, 5, 3, 4, 4, 3, 3, 5, 5, 3, 4, 5, 5, 3, 3, 6, 4, 7, 6, 4, 5, 4, 6, 7, 3, 2, 4, 6, 2, 7, 6, 8, 7, 5, 6, 7, 5, 6, 5, 4, 4, 8, 5, 5, 7, 6, 5, 6, 7, 6, 7, 6, 3, 5, 3, 7, 6, 5, 6, 7, 4, 3, 6, 5, 3, 3, 5, 6, 4, 5, 4, 2, 3, 2, 3 ),

  35. 9, 11, 12, 19, 32, 35, 1, 6, 7, 14, 33, 36, 44, 20, 27, 41, 26, 41, 13, 17, 37, 38, 4, 9, 10, 31, 32, 43, 21, 36, 3, 10, 23, 29, 34, 40, 43, 9, 24, 29, 35, 43, 50, 5, 6, 25, 34, 44, 49, 53, 60, 3, 5, 31, 33, 40, 49, 59, 19, 24, 32, 45, 50, 14, 21, 25, 30, 44, 47, 13, 28, 38, 39, 51, 52, 61, 17, 28, 37, 48, 52, 13, 18, 19, 37, 45, 51, 31, 34, 43, 59, 64, 20, 26, 27, 42, 20, 41, 46, 54, 29, 31, 32, 40, 50, 56, 57, 64, 25, 33, 36, 47, 53, 19, 35, 39, 50, 51, 20, 22, 42, 48, 52, 54, 68, 36, 44, 53, 58, 62, 63, 17, 22, 38, 46, 52, 33, 34, 59, 60, 66, 67, 32, 35, 43, 45, 51, 55, 57, 37, 39, 45, 50, 55, 61, 37, 38, 46, 48, 61, 68, 69, 33, 44, 47, 58, 60, 65, 42, 46, 68, 50, 51, 57, 61, 71, 43, 57, 64, 43, 50, 55, 56, 64, 71, 74, 47, 53, 62, 63, 65, 72, 34, 40, 49, 64, 67, 33, 49, 53, 65, 66, 76, 37, 51, 52, 55, 69, 70, 71, 47, 58, 63, 72, 47, 58, 62, 40, 43, 56, 57, 59, 74, 53, 58, 60, 72, 76, 49, 60, 67, 49, 59, 66, 46, 52, 54, 69, 73, 52, 61, 68, 70, 73, 75, 61, 69, 71, 75, 55, 57, 61, 70, 74, 58, 62, 65, 76, 68, 69, 57, 64, 71, 69, 70, 60, 65, 72 ), sumNumNeigh = 366) #INITIAL VALUES list(tau = 1, alpha0 = 0, b=c(0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0) ) queen’s connectivity definition adj = c( 2, 7, 14, 25, 1, 14, 21, 5, 8, 23, 31, 34, 9, 10, 29, 3, 6, 33, 34, 5, 7, 25, 33, 1, 6, 25, 3, 10, 23, 4, 11, 24, 29, 32, 4, 8, 23, 29, 31, 9, 12, 24, 11, 16, 19, 24, 17, 18, 28, 37, 39, 1, 2, 21, 25, 36, 17, 20, 22, 12, 18, 19, 13, 15, 22, 28, 38, 48, 13, 16, 19, 39, 12, 16, 18, 24, 35, 39, 45, 15, 22, 26, 41, 42, 46, 2, 14, 30, 36, 15, 17, 20, 46, 48, 3, 8, 10, 31, 9, 11, 12, 19, 32, 35, 1, 6, 7, 14, 33, 36, 44, 20, 27, 41, 26, 41, 13, 17, 37, 38, 4, 9, 10, 31, 32, 43, 21, 36, 3, 10, 23, 29, 34, 40, 43, 9, 24, 29, 35, 43, 50, 5, 6, 25, 34, 44, 49, 53, 60, 3, 5, 31, 33, 40, 49, 59, 19, 24, 32, 45, 50, 14, 21, 25, 30, 44, 47, 13, 28, 38, 39, 51, 52, 61, 17, 28, 37, 48, 52, 13, 18, 19, 37, 45, 51, 31, 34, 43, 59, 64, 20, 26, 27, 42, 20, 41, 46, 54, 29, 31, 32, 40, 50, 56, 57, 64, 25, 33, 36, 47, 53, 19, 35, 39, 50, 51, 20, 22, 42, 48, 52, 54, 68, 36, 44, 53, 58, 62, 63, 17, 22, 38, 46, 52, 33, 34, 59, 60, 66, 67, 32, 35, 43, 45, 51, 55, 57, 37, 39, 45, 50, 55, 61, 37, 38, 46, 48, 61, 68, 69, 33, 44, 47, 58, 60, 65, 42, 46, 68, 50, 51, 57, 61, 71, 43, 57, 64, 43, 50, 55, 56, 64, 71, 74, 47, 53, 62, 63, 65, 72, 34, 40, 49, 64, 67, 33, 49, 53, 65, 66, 76, 37, 51, 52, 55, 69, 70, 71, 47, 58, 63, 72, 47, 58, 62, 40, 43, 56, 57, 59, 74, 53, 58, 60, 72, 76, 49, 60, 67, 49, 59, 66, 46, 52, 54, 69, 73, 52, 61, 68, 70, 73, 75, 61, 69, 71, 75, 55, 57, 61, 70, 74, 58, 62, 65, 76, 68, 69, 57, 64, 71, 69, 70, 60, 65, 72, 2, 7, 14, 25, 1, 14, 21, 5, 8, 23, 31, 34, 9, 10, 29, 3, 6, 33, 34, 5, 7, 25, 33, 1, 6, 25, 3, 10, 23, 4, 11, 24, 29, 32, 4, 8, 23, 29, 31, 9, 12, 24, 11, 16, 19, 24, 17, 18, 28, 37, 39, 1, 2, 21, 25, 36, 17, 20, 22, 12, 18, 19, 13, 15, 22, 28, 38, 48, 13, 16, 19, 39, 12, 16, 18, 24, 35, 39, 45, 15, 22, 26, 41, 42, 46, 2, 14, 30, 36, 15, 17, 20, 46, 48, 3, 8, 10, 31,

  36. Model { for (i in 1:N) {m[i] <- 1/num[i]} cumsum[1] <- 0 for(i in 2:(N+1)) {cumsum[i] <- sum(num[1:(i-1)])} for(k in 1 : sumNumNeigh) {for(i in 1:N){pick[k,i] <- step(k - cumsum[i] - epsilon)*step(cumsum[i+1] - k) # pick[k,i] = 1 if cumsum[i] < k <= cumsum[i=1]; otherwise, pick[k,i] = 0} C[k] <- sqrt(num[adj[k]]/inprod(num[], pick[k,])) # weight for each pair of neighbours} epsilon <- 0.0001 for (i in 1 : N) { U[i] ~ dbin(p[i], T[i]) p[i] <- exp(S[i])/(1+exp(S[i])) theta[i] <- alpha} # proper CAR prior distribution for random effects: S[1:N] ~ car.proper(theta[],C[],adj[],num[],m[],prec,rho) for(k in 1:sumNumNeigh) {weights[k] <- 1} # Other priors: alpha ~ dnorm(0,0.0001) prec ~ dgamma(0.5,0.0005) # prior on precision sigma <- sqrt(1/prec) # standard deviation rho.min <- min.bound(C[],adj[],num[],m[]) rho.max <- max.bound(C[],adj[],num[],m[]) rho ~ dunif(rho.min,rho.max) }

  37. node mean sd MC error 2.5% median 97.5% start samplerhoCAR 0.9378 0.05255 5.995E-4 0.8047 0.9514 0.9973 50001 10000 MCMC iteration time series plot MCMC iteration correlogram

  38. GeoBUGS demonstration: % urban population in Puerto Rico – no random effect #MODEL model { for (i in 1 : N) { U[i] ~ dbin(p[i], T[i]) p[i] <- exp(alpha)/(1+exp(alpha )) } # priors: alpha ~ dflat() }

  39. #DATA list(N = 73, U = c(11062, 42042, 64685, 27305, 23077, 25387, 91593, 18346, 32281, 27850, 254115, 40875, 139445, 30886, 185703, 43707, 16671, 14262, 40457, 29802, 16800, 35270, 33421, 39958, 20682, 40395, 21087, 99850, 35476, 36201, 16472, 58848, 42527, 11048, 46236, 35859, 21330, 25584, 3792, 32126, 74856, 18664, 41997, 2839, 11787, 95880, 37713, 27605, 21499, 29709, 17200, 14688, 23829, 178792, 24196, 14767, 50242, 23848, 28462, 34650, 434374, 35130, 38583, 17412, 63929, 94085, 75728, 23852, 36971, 59572, 23364, 37238, 40919 ), T = c(19143, 42042, 64685, 29032, 26493, 28348, 100131, 19117, 34689, 28909, 254115, 46911, 140502, 35244, 186076, 47370, 18004, 19811, 42753, 37597, 20002, 36867, 34017, 40712, 21888, 44301, 23072, 100053, 36743, 38925, 16614, 59035, 44444, 17318, 50531, 36452, 26261, 34415, 11061, 34485, 75872, 19817, 45409, 6449, 12741, 98434, 39697, 29965, 23753, 29709, 23844, 20152, 26719, 186475, 25450, 14767, 52362, 25935, 31113, 37105, 434374, 40997, 44204, 21665, 63929, 94085, 75728, 35336, 37910, 61929, 27913, 39246, 46384 ), ) #INITIAL VALUES list(alpha=-3)

  40. The German labor market revisited: frequentist and Bayesian random effects models

  41. Frequentist random intercept term The spatial filter contains 27 (of 98) eigenvectors, with R2 = 0.4542, P(S-Wresiduals) < 0.0001.

  42. MCMC results from GeoBUGS 1000 weeded the proper CAR model 25,000 burn-in

  43. Bayesian model estimation computer execution time required to estimate is prohibitive

  44. The ICAR alternative • #MODEL • model • {for (i in 1 : N) { • W[i] ~ dpois(mu[i]) • log(mu[i]) <- alpha + S[i] + U[i] + log(A[i]) • U[i] ~ dnorm(0,tau.U) } • # improper CAR prior distribution for random effects: • S[1:N] ~ car.normal(adj[],weights[],num[],tau.S) • for(k in 1:sumNumNeigh) {weights[k] <- 1} • # Other priors: • alpha ~ dflat() • tau.S ~ dgamma(0.5, 0.0005) • sigma2.S <- 1/tau.S • tau.U ~ dgamma(0.5, 0.0005) • sigma2.U <- 1/tau.U } offset

  45. attribute variables • #DATA • list(N = 439, • W = c( • 25335, 74162, 64273, 25080, 39848, 59407, 49685, 60880, 101563, 39206, 83512, • … • 32999, 26534, 45267, 35232, 35788, 41961, 36129 • ), • A = c( • 23.51, 43.81, 87.14, 27.53, 558.57, 512.17, 855.22, 578.86, 261.45, 447.18, 877.26, • … • 369.50, 382.46, 452.02, 350.84, 219.97 • ), • num = c(1, 2, 4, 3, 4, 7, 2, 4, 4, 5, • … • 8, 7, 8, 4, 7, 7, 6, 8, 6 • ), • adj = c( • 12, • 11, 10, • 359, 15, 8, 6, • … • 438, 400, 390, 375, 372, 368 • ), • sumNumNeigh = 2314) number of neighbors This can be generated by GeoBUGS; the queen’s definition of adjacency is used lists of neighbors

  46. #INITIAL VALUES • list(tau.U=1, tau.S=1, alpha=0, • S=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, • … • 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), • U=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, • … • 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0) • )

  47. intercept Convergence has not been achieved (10,000 burn in; + 25,000/100)

  48. Random effects: spatially structured and unstructured SA: 2.67/(30.39+2.67) = 0.08 (8% of variance)

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