R version 3.6.3 (2020-02-29) -- "Holding the Windsock" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ############################################################################ > # MLwiN MCMC Manual > # > # 7 Using the WinBUGS Interface in MLwiN . . . . . . . . . . . . . . . .83 > # > # Browne, W.J. (2009) MCMC Estimation in MLwiN, v2.13. Centre for > # Multilevel Modelling, University of Bristol. > ############################################################################ > # R script to replicate all analyses using R2MLwiN > # > # Zhang, Z., Charlton, C., Parker, R, Leckie, G., and Browne, W.J. > # Centre for Multilevel Modelling, 2012 > # http://www.bristol.ac.uk/cmm/software/R2MLwiN/ > ############################################################################ > > # 7.1 Variance components models in WinBUGS . . . . . . . . . . . . . . . 84 > > library(R2MLwiN) R2MLwiN: A package to run models implemented in MLwiN from R Copyright 2013-2017 Zhengzheng Zhang, Christopher M. J. Charlton, Richard M. A. Parker, William J. Browne and George Leckie Support provided by the Economic and Social Research Council (ESRC) (Grants RES-149-25-1084, RES-576-25-0032 and ES/K007246/1) To cite R2MLwiN in publications use: Zhengzheng Zhang, Richard M. A. Parker, Christopher M. J. Charlton, George Leckie, William J. Browne (2016). R2MLwiN: A Package to Run MLwiN from within R. Journal of Statistical Software, 72(10), 1-43. doi:10.18637/jss.v072.i10 A BibTeX entry for LaTeX users is @Article{, title = {{R2MLwiN}: A Package to Run {MLwiN} from within {R}}, author = {Zhengzheng Zhang and Richard M. A. Parker and Christopher M. J. Charlton and George Leckie and William J. Browne}, journal = {Journal of Statistical Software}, year = {2016}, volume = {72}, number = {10}, pages = {1--43}, doi = {10.18637/jss.v072.i10}, } The MLwiN_path option is currently set to C:/Program Files/MLwiN v3.05/ To change this use: options(MLwiN_path="") > # MLwiN folder > mlwin <- getOption("MLwiN_path") > while (!file.access(mlwin, mode = 1) == 0) { + cat("Please specify the root MLwiN folder or the full path to the MLwiN executable:\n") + mlwin <- scan(what = character(0), sep = "\n") + mlwin <- gsub("\\", "/", mlwin, fixed = TRUE) + } > options(MLwiN_path = mlwin) > > ## Read tutorial data > data(tutorial, package = "R2MLwiN") > > ## The highest level comes first, then the second highest and so on > ## Uses the results from IGLS to create initial values for bugs > ## Fit the model by calling openbugs using the R2WinBUGS package > mymodel1 <- runMLwiN(normexam ~ 1 + standlrt + (1 | school) + (1 | student), estoptions = list(EstM = 1, show.file = TRUE), + BUGO = c(version = 4, n.chains = 1, debug = FALSE, seed = 1, OpenBugs = TRUE), data = tutorial) MLwiN is running, please wait...... /nogui option ignored ECHO 0 Echoing is ON BATC 1 Batch mode is ON MAXI 2 STAR iteration 0 iteration 1 Convergence not achieved TOLE 2 MAXI 20 BATC 1 Batch mode is ON NEXT iteration 2 iteration 3 Convergence achieved ECHO 0 Execution completed Loading required namespace: BRugs Welcome to BRugs connected to OpenBUGS version 3.2.3 model is syntactically correct data loaded model compiled Initializing chain 1: model is initialized model is already initialized Sampling has been started ... 500 updates took 0 s deviance set monitor set for variable 'beta' monitor set for variable 'sigma2' monitor set for variable 'u2' monitor set for variable 'sigma2.u2' monitor set for variable 'deviance' 5000 updates took 10 s > > summary(mymodel1) Iterations = 501:5500 Thinning interval = 1 Number of chains = 1 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta[1] 4.153e-03 0.03886 0.0005496 0.0024982 beta[2] 5.635e-01 0.01251 0.0001770 0.0001986 deviance 9.209e+03 11.93859 0.1688371 0.1773754 sigma2 5.665e-01 0.01272 0.0001800 0.0001800 sigma2.u2 9.661e-02 0.02033 0.0002874 0.0003631 u2[1] 3.719e-01 0.09168 0.0012965 0.0025614 u2[10] -3.410e-01 0.10808 0.0015284 0.0024586 u2[11] 1.775e-01 0.09765 0.0013809 0.0028450 u2[12] -6.347e-02 0.10479 0.0014819 0.0025330 u2[13] -1.520e-01 0.09713 0.0013736 0.0024681 u2[14] -1.680e-01 0.06561 0.0009279 0.0025548 u2[15] -1.852e-01 0.08509 0.0012033 0.0024174 u2[16] -4.117e-01 0.08615 0.0012183 0.0023037 u2[17] -1.751e-01 0.07566 0.0010699 0.0025540 u2[18] -8.481e-02 0.07750 0.0010961 0.0023982 u2[19] -1.598e-02 0.10214 0.0014445 0.0027280 u2[2] 5.019e-01 0.10346 0.0014631 0.0024497 u2[20] 2.158e-01 0.11812 0.0016704 0.0027561 u2[21] 2.441e-01 0.09245 0.0013074 0.0023721 u2[22] -4.379e-01 0.08404 0.0011885 0.0024248 u2[23] -4.924e-01 0.13564 0.0019182 0.0027408 u2[24] 2.077e-01 0.11971 0.0016929 0.0021945 u2[25] -2.325e-01 0.09142 0.0012928 0.0025982 u2[26] -2.574e-02 0.09029 0.0012768 0.0024269 u2[27] 2.187e-02 0.11814 0.0016708 0.0024794 u2[28] -6.128e-01 0.10190 0.0014411 0.0021463 u2[29] 2.367e-01 0.09060 0.0012812 0.0026304 u2[3] 5.007e-01 0.10776 0.0015239 0.0025242 u2[30] 1.546e-01 0.11444 0.0016185 0.0026351 u2[31] 3.337e-02 0.10674 0.0015096 0.0024175 u2[32] -8.583e-03 0.11330 0.0016022 0.0021816 u2[33] 2.871e-02 0.08998 0.0012725 0.0024802 u2[34] -1.389e-01 0.13743 0.0019435 0.0027089 u2[35] 1.268e-01 0.11843 0.0016749 0.0025395 u2[36] -1.860e-01 0.09333 0.0013198 0.0024271 u2[37] -1.917e-01 0.14603 0.0020652 0.0027079 u2[38] -1.541e-01 0.10202 0.0014428 0.0023468 u2[39] 1.280e-01 0.10793 0.0015264 0.0021961 u2[4] 1.610e-02 0.08936 0.0012637 0.0023717 u2[40] -2.364e-01 0.09222 0.0013042 0.0024792 u2[41] 2.119e-01 0.09882 0.0013975 0.0025637 u2[42] 9.046e-02 0.10067 0.0014237 0.0023759 u2[43] -9.006e-02 0.09750 0.0013789 0.0023646 u2[44] -2.497e-01 0.13163 0.0018615 0.0024539 u2[45] -1.125e-01 0.10328 0.0014606 0.0026293 u2[46] -3.553e-01 0.08749 0.0012373 0.0025947 u2[47] -4.387e-02 0.08717 0.0012328 0.0022480 u2[48] -4.284e-02 0.26600 0.0037618 0.0040375 u2[49] 3.955e-02 0.07840 0.0011088 0.0024538 u2[5] 2.384e-01 0.12228 0.0017293 0.0026788 u2[50] -3.024e-01 0.09247 0.0013077 0.0023567 u2[51] -5.494e-02 0.09989 0.0014126 0.0025082 u2[52] 3.799e-01 0.09794 0.0013851 0.0025892 u2[53] 7.207e-01 0.09659 0.0013660 0.0028079 u2[54] -5.580e-01 0.20835 0.0029465 0.0031467 u2[55] 5.034e-01 0.10685 0.0015110 0.0026524 u2[56] 9.349e-03 0.11806 0.0016696 0.0026145 u2[57] 2.965e-02 0.09701 0.0013720 0.0024616 u2[58] 1.395e-01 0.12063 0.0017060 0.0025223 u2[59] -6.606e-01 0.11150 0.0015769 0.0029353 u2[6] 5.407e-01 0.08953 0.0012662 0.0025348 u2[60] 2.246e-01 0.08877 0.0012554 0.0028076 u2[61] -4.249e-02 0.09655 0.0013654 0.0022542 u2[62] -5.523e-02 0.09211 0.0013026 0.0025390 u2[63] 5.348e-01 0.13310 0.0018823 0.0029429 u2[64] 8.338e-02 0.09938 0.0014055 0.0024036 u2[65] -1.671e-01 0.08808 0.0012456 0.0023927 u2[7] 3.784e-01 0.08716 0.0012326 0.0026957 u2[8] -2.861e-02 0.08094 0.0011447 0.0022464 u2[9] -1.385e-01 0.12346 0.0017460 0.0024948 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta[1] -7.077e-02 -0.02172 3.443e-03 2.935e-02 8.461e-02 beta[2] 5.386e-01 0.55510 5.635e-01 5.720e-01 5.881e-01 deviance 9.188e+03 9200.68091 9.209e+03 9.216e+03 9.235e+03 sigma2 5.420e-01 0.55775 5.662e-01 5.750e-01 5.920e-01 sigma2.u2 6.448e-02 0.08219 9.413e-02 1.080e-01 1.437e-01 u2[1] 1.956e-01 0.31035 3.700e-01 4.349e-01 5.531e-01 u2[10] -5.567e-01 -0.41332 -3.407e-01 -2.678e-01 -1.307e-01 u2[11] -1.924e-02 0.11382 1.780e-01 2.451e-01 3.638e-01 u2[12] -2.695e-01 -0.13460 -6.151e-02 7.704e-03 1.414e-01 u2[13] -3.458e-01 -0.21675 -1.507e-01 -8.769e-02 3.868e-02 u2[14] -2.979e-01 -0.21104 -1.685e-01 -1.242e-01 -4.026e-02 u2[15] -3.470e-01 -0.24305 -1.852e-01 -1.279e-01 -1.984e-02 u2[16] -5.808e-01 -0.46853 -4.111e-01 -3.542e-01 -2.443e-01 u2[17] -3.263e-01 -0.22500 -1.733e-01 -1.246e-01 -2.609e-02 u2[18] -2.366e-01 -0.13574 -8.554e-02 -3.183e-02 6.653e-02 u2[19] -2.195e-01 -0.08409 -1.544e-02 5.342e-02 1.801e-01 u2[2] 3.002e-01 0.43365 5.030e-01 5.725e-01 6.988e-01 u2[20] -1.339e-02 0.13755 2.151e-01 2.945e-01 4.481e-01 u2[21] 6.441e-02 0.18079 2.441e-01 3.079e-01 4.278e-01 u2[22] -6.080e-01 -0.49377 -4.376e-01 -3.811e-01 -2.742e-01 u2[23] -7.584e-01 -0.58545 -4.925e-01 -3.982e-01 -2.293e-01 u2[24] -2.385e-02 0.12419 2.079e-01 2.910e-01 4.389e-01 u2[25] -4.130e-01 -0.29223 -2.334e-01 -1.723e-01 -5.344e-02 u2[26] -2.019e-01 -0.08632 -2.644e-02 3.506e-02 1.494e-01 u2[27] -2.101e-01 -0.05798 2.294e-02 1.001e-01 2.502e-01 u2[28] -8.121e-01 -0.68271 -6.121e-01 -5.438e-01 -4.137e-01 u2[29] 5.583e-02 0.17510 2.378e-01 2.983e-01 4.127e-01 u2[3] 2.939e-01 0.42710 5.007e-01 5.738e-01 7.095e-01 u2[30] -7.176e-02 0.07700 1.543e-01 2.311e-01 3.758e-01 u2[31] -1.763e-01 -0.03758 3.338e-02 1.050e-01 2.421e-01 u2[32] -2.268e-01 -0.08579 -8.878e-03 6.721e-02 2.142e-01 u2[33] -1.507e-01 -0.02973 2.930e-02 8.898e-02 2.012e-01 u2[34] -4.025e-01 -0.23285 -1.397e-01 -4.531e-02 1.227e-01 u2[35] -1.083e-01 0.04956 1.275e-01 2.075e-01 3.659e-01 u2[36] -3.697e-01 -0.24982 -1.842e-01 -1.222e-01 -5.621e-03 u2[37] -4.779e-01 -0.28828 -1.927e-01 -9.295e-02 9.370e-02 u2[38] -3.549e-01 -0.22334 -1.545e-01 -8.452e-02 4.369e-02 u2[39] -8.498e-02 0.05423 1.267e-01 2.014e-01 3.427e-01 u2[4] -1.575e-01 -0.04275 1.580e-02 7.654e-02 1.911e-01 u2[40] -4.187e-01 -0.29751 -2.354e-01 -1.740e-01 -5.979e-02 u2[41] 1.806e-02 0.14623 2.117e-01 2.770e-01 4.099e-01 u2[42] -1.045e-01 0.02317 9.105e-02 1.597e-01 2.833e-01 u2[43] -2.850e-01 -0.15361 -8.973e-02 -2.527e-02 1.039e-01 u2[44] -5.058e-01 -0.34009 -2.501e-01 -1.610e-01 5.718e-03 u2[45] -3.136e-01 -0.18068 -1.116e-01 -4.374e-02 8.560e-02 u2[46] -5.244e-01 -0.41537 -3.554e-01 -2.956e-01 -1.802e-01 u2[47] -2.160e-01 -0.10177 -4.418e-02 1.411e-02 1.240e-01 u2[48] -5.711e-01 -0.22138 -4.105e-02 1.357e-01 4.745e-01 u2[49] -1.161e-01 -0.01415 3.979e-02 9.108e-02 1.936e-01 u2[5] 2.418e-03 0.15608 2.383e-01 3.214e-01 4.796e-01 u2[50] -4.804e-01 -0.36519 -3.015e-01 -2.379e-01 -1.210e-01 u2[51] -2.471e-01 -0.12348 -5.610e-02 1.189e-02 1.400e-01 u2[52] 1.855e-01 0.31430 3.811e-01 4.461e-01 5.732e-01 u2[53] 5.348e-01 0.65461 7.198e-01 7.863e-01 9.090e-01 u2[54] -9.821e-01 -0.69344 -5.539e-01 -4.125e-01 -1.632e-01 u2[55] 2.947e-01 0.43212 5.013e-01 5.742e-01 7.148e-01 u2[56] -2.166e-01 -0.06949 8.965e-03 8.694e-02 2.408e-01 u2[57] -1.590e-01 -0.03566 2.850e-02 9.574e-02 2.188e-01 u2[58] -9.771e-02 0.05955 1.413e-01 2.219e-01 3.754e-01 u2[59] -8.850e-01 -0.73465 -6.595e-01 -5.856e-01 -4.445e-01 u2[6] 3.661e-01 0.47971 5.406e-01 6.018e-01 7.157e-01 u2[60] 5.461e-02 0.16464 2.243e-01 2.848e-01 3.989e-01 u2[61] -2.335e-01 -0.10641 -4.227e-02 2.342e-02 1.447e-01 u2[62] -2.341e-01 -0.11784 -5.477e-02 5.279e-03 1.222e-01 u2[63] 2.712e-01 0.44429 5.363e-01 6.232e-01 7.924e-01 u2[64] -1.089e-01 0.01525 8.239e-02 1.514e-01 2.787e-01 u2[65] -3.371e-01 -0.22564 -1.683e-01 -1.072e-01 9.316e-04 u2[7] 2.075e-01 0.31964 3.787e-01 4.374e-01 5.493e-01 u2[8] -1.871e-01 -0.08284 -2.833e-02 2.689e-02 1.362e-01 u2[9] -3.810e-01 -0.22006 -1.391e-01 -5.585e-02 9.812e-02 > summary(mymodel1[, "beta[2]"]) Iterations = 501:5500 Thinning interval = 1 Number of chains = 1 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 0.5635180 0.0125131 0.0001770 0.0001986 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 0.5386 0.5551 0.5635 0.5720 0.5881 > sixway(mymodel1[, "beta[2]", drop = FALSE]) > > # 7.2 So why have a WinBUGS interface ? . . . . . . . . . . . . . . . . . 92 > # 7.3 t distributed school residuals . . . . . . . . . . . . . . . . . . .92 > > ## Download the model, initial, data files > modelfile <- paste0(tempdir(), "/tutorial1_model.txt") > download.file("http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_model.txt", modelfile, method = "auto") trying URL 'http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_model.txt' Content type 'text/plain' length 557 bytes ================================================== downloaded 557 bytes > file.show(modelfile) > > initfile <- paste0(tempdir(), "/tutorial1_inits.txt") > download.file("http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_inits.txt", initfile, method = "auto") trying URL 'http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_inits.txt' Content type 'text/plain' length 780 bytes ================================================== downloaded 780 bytes > file.show(initfile) > > datafile <- paste0(tempdir(), "/tutorial1_data.txt") > download.file("http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_data.txt", datafile, method = "auto") trying URL 'http://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_data.txt' Content type 'text/plain' length 128091 bytes (125 KB) ================================================== downloaded 125 KB > > bugEst <- paste0(tempdir(), "/tutorial1_log.txt") > > > chains.bugs1 <- mlwin2bugs(D = "t", levID = c("school", "student"), datafile, initfile, modelfile, bugEst, fact = NULL, + addmore = c("sigma2", "df"), n.chains = 1, n.iter = 5500, n.burnin = 500, n.thin = 1, debug = TRUE, bugsWorkingDir = tempdir(), + OpenBugs = TRUE) model is syntactically correct data loaded model compiled Initializing chain 1: model is initialized model is already initialized Sampling has been started ... 500 updates took 0 s deviance set monitor set for variable 'beta' monitor set for variable 'u2' monitor set for variable 'sigma2.u2' monitor set for variable 'sigma2' monitor set for variable 'df' monitor set for variable 'deviance' 5000 updates took 9 s > ## Close winbugs manually > summary(chains.bugs1) Iterations = 501:5500 Thinning interval = 1 Number of chains = 1 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta[1] 2.717e-03 0.04075 0.0005762 0.0028484 beta[2] 5.631e-01 0.01267 0.0001792 0.0002002 deviance 9.209e+03 12.16343 0.1720169 0.1830647 df 1.085e+02 55.41011 0.7836173 4.8287956 sigma2 5.663e-01 0.01281 0.0001812 0.0001812 sigma2.u2 9.380e-02 0.02036 0.0002879 0.0005751 u2[1] 3.704e-01 0.09268 0.0013108 0.0029810 u2[10] -3.367e-01 0.10397 0.0014704 0.0023876 u2[11] 1.801e-01 0.09888 0.0013983 0.0024701 u2[12] -6.269e-02 0.10962 0.0015503 0.0031496 u2[13] -1.488e-01 0.09662 0.0013664 0.0028147 u2[14] -1.654e-01 0.06544 0.0009255 0.0029728 u2[15] -1.829e-01 0.08668 0.0012259 0.0028994 u2[16] -4.091e-01 0.08759 0.0012387 0.0028116 u2[17] -1.734e-01 0.07664 0.0010838 0.0028738 u2[18] -8.599e-02 0.07717 0.0010913 0.0029892 u2[19] -1.091e-02 0.10362 0.0014654 0.0027068 u2[2] 5.022e-01 0.10551 0.0014922 0.0029869 u2[20] 2.130e-01 0.11885 0.0016808 0.0028155 u2[21] 2.420e-01 0.09390 0.0013280 0.0029439 u2[22] -4.378e-01 0.08537 0.0012073 0.0025465 u2[23] -4.906e-01 0.13506 0.0019101 0.0026863 u2[24] 2.087e-01 0.12006 0.0016978 0.0028805 u2[25] -2.304e-01 0.09269 0.0013109 0.0026022 u2[26] -2.524e-02 0.09120 0.0012898 0.0026176 u2[27] 2.121e-02 0.11755 0.0016624 0.0030627 u2[28] -6.118e-01 0.10235 0.0014475 0.0027891 u2[29] 2.399e-01 0.08881 0.0012560 0.0028421 u2[3] 5.060e-01 0.10563 0.0014939 0.0029665 u2[30] 1.592e-01 0.11474 0.0016226 0.0030656 u2[31] 3.397e-02 0.10624 0.0015024 0.0030044 u2[32] -6.905e-03 0.11552 0.0016337 0.0028509 u2[33] 3.091e-02 0.09172 0.0012972 0.0029141 u2[34] -1.399e-01 0.13861 0.0019602 0.0024838 u2[35] 1.290e-01 0.11795 0.0016681 0.0030016 u2[36] -1.798e-01 0.09415 0.0013315 0.0025039 u2[37] -1.860e-01 0.14611 0.0020663 0.0027530 u2[38] -1.535e-01 0.10151 0.0014355 0.0023769 u2[39] 1.292e-01 0.10923 0.0015448 0.0030835 u2[4] 1.646e-02 0.08803 0.0012450 0.0026560 u2[40] -2.331e-01 0.09339 0.0013207 0.0028746 u2[41] 2.132e-01 0.10063 0.0014231 0.0028351 u2[42] 8.996e-02 0.09971 0.0014102 0.0028348 u2[43] -8.865e-02 0.09951 0.0014073 0.0027851 u2[44] -2.483e-01 0.13587 0.0019215 0.0027381 u2[45] -1.106e-01 0.10412 0.0014724 0.0027772 u2[46] -3.523e-01 0.08826 0.0012481 0.0025901 u2[47] -4.251e-02 0.08747 0.0012371 0.0028660 u2[48] -4.172e-02 0.26845 0.0037964 0.0039506 u2[49] 4.089e-02 0.08051 0.0011385 0.0028513 u2[5] 2.382e-01 0.12197 0.0017249 0.0029751 u2[50] -3.031e-01 0.09371 0.0013253 0.0025584 u2[51] -5.472e-02 0.10013 0.0014160 0.0027751 u2[52] 3.807e-01 0.09872 0.0013961 0.0029146 u2[53] 7.258e-01 0.09799 0.0013857 0.0029537 u2[54] -5.615e-01 0.21868 0.0030926 0.0037486 u2[55] 5.032e-01 0.10734 0.0015180 0.0027379 u2[56] 1.072e-02 0.11924 0.0016862 0.0026682 u2[57] 3.149e-02 0.09995 0.0014134 0.0031510 u2[58] 1.392e-01 0.11676 0.0016513 0.0026839 u2[59] -6.645e-01 0.11272 0.0015940 0.0025736 u2[6] 5.415e-01 0.08969 0.0012685 0.0027371 u2[60] 2.235e-01 0.09010 0.0012742 0.0029205 u2[61] -3.968e-02 0.09846 0.0013924 0.0028057 u2[62] -5.375e-02 0.09364 0.0013242 0.0028476 u2[63] 5.400e-01 0.13338 0.0018863 0.0028765 u2[64] 8.865e-02 0.10054 0.0014218 0.0029364 u2[65] -1.670e-01 0.09051 0.0012800 0.0029070 u2[7] 3.789e-01 0.08601 0.0012164 0.0027556 u2[8] -2.718e-02 0.08191 0.0011584 0.0025152 u2[9] -1.367e-01 0.12335 0.0017444 0.0029032 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta[1] -0.07739 -0.02463 3.221e-03 0.03065 7.919e-02 beta[2] 0.53799 0.55445 5.629e-01 0.57187 5.872e-01 deviance 9187.79172 9200.51294 9.209e+03 9216.81104 9.235e+03 df 9.88860 63.26934 1.129e+02 155.38561 1.956e+02 sigma2 0.54173 0.55756 5.664e-01 0.57470 5.920e-01 sigma2.u2 0.05978 0.07938 9.161e-02 0.10565 1.398e-01 u2[1] 0.19057 0.30752 3.700e-01 0.43041 5.591e-01 u2[10] -0.53744 -0.40907 -3.380e-01 -0.26532 -1.313e-01 u2[11] -0.01029 0.11273 1.805e-01 0.24544 3.744e-01 u2[12] -0.28540 -0.13522 -6.089e-02 0.01140 1.482e-01 u2[13] -0.33471 -0.21359 -1.493e-01 -0.08376 4.203e-02 u2[14] -0.29416 -0.20858 -1.657e-01 -0.12151 -3.544e-02 u2[15] -0.35252 -0.24208 -1.830e-01 -0.12221 -1.699e-02 u2[16] -0.57961 -0.46849 -4.084e-01 -0.34890 -2.407e-01 u2[17] -0.32205 -0.22590 -1.734e-01 -0.12197 -2.131e-02 u2[18] -0.23664 -0.13771 -8.553e-02 -0.03327 6.505e-02 u2[19] -0.21043 -0.08326 -9.346e-03 0.05734 1.978e-01 u2[2] 0.29715 0.43137 5.029e-01 0.57358 7.066e-01 u2[20] -0.01842 0.13268 2.125e-01 0.29407 4.460e-01 u2[21] 0.05888 0.17820 2.420e-01 0.30465 4.286e-01 u2[22] -0.60571 -0.49669 -4.367e-01 -0.37870 -2.735e-01 u2[23] -0.75553 -0.57958 -4.893e-01 -0.39961 -2.311e-01 u2[24] -0.02901 0.12752 2.101e-01 0.28984 4.478e-01 u2[25] -0.41187 -0.29366 -2.304e-01 -0.16619 -5.172e-02 u2[26] -0.20619 -0.08768 -2.529e-02 0.03589 1.492e-01 u2[27] -0.21086 -0.06034 2.173e-02 0.10064 2.497e-01 u2[28] -0.81057 -0.68230 -6.115e-01 -0.54129 -4.133e-01 u2[29] 0.07012 0.17881 2.397e-01 0.29972 4.174e-01 u2[3] 0.29753 0.43473 5.066e-01 0.57971 7.045e-01 u2[30] -0.06500 0.08304 1.597e-01 0.23724 3.814e-01 u2[31] -0.17858 -0.03764 3.417e-02 0.10482 2.415e-01 u2[32] -0.22888 -0.08571 -5.606e-03 0.07103 2.183e-01 u2[33] -0.14638 -0.03119 3.029e-02 0.09118 2.125e-01 u2[34] -0.41742 -0.23218 -1.395e-01 -0.04906 1.354e-01 u2[35] -0.10391 0.04822 1.321e-01 0.20827 3.535e-01 u2[36] -0.35970 -0.24281 -1.786e-01 -0.11773 7.821e-03 u2[37] -0.46780 -0.28668 -1.873e-01 -0.08754 1.096e-01 u2[38] -0.34841 -0.22121 -1.536e-01 -0.08414 4.529e-02 u2[39] -0.08333 0.05501 1.281e-01 0.20220 3.439e-01 u2[4] -0.16274 -0.04247 1.644e-02 0.07553 1.855e-01 u2[40] -0.41164 -0.29636 -2.345e-01 -0.16973 -4.998e-02 u2[41] 0.02006 0.14570 2.112e-01 0.28210 4.089e-01 u2[42] -0.10843 0.02282 8.984e-02 0.15736 2.813e-01 u2[43] -0.28251 -0.15660 -8.837e-02 -0.02120 1.056e-01 u2[44] -0.51180 -0.34055 -2.483e-01 -0.15677 1.338e-02 u2[45] -0.31860 -0.17807 -1.091e-01 -0.04204 9.228e-02 u2[46] -0.52543 -0.41123 -3.511e-01 -0.29242 -1.793e-01 u2[47] -0.21363 -0.10252 -4.378e-02 0.01884 1.252e-01 u2[48] -0.55048 -0.22272 -3.930e-02 0.13722 4.918e-01 u2[49] -0.11731 -0.01287 4.105e-02 0.09495 1.966e-01 u2[5] -0.00691 0.15908 2.393e-01 0.32064 4.709e-01 u2[50] -0.48839 -0.36678 -3.038e-01 -0.24053 -1.182e-01 u2[51] -0.24612 -0.12213 -5.609e-02 0.01499 1.338e-01 u2[52] 0.18816 0.31405 3.804e-01 0.44679 5.783e-01 u2[53] 0.53183 0.66066 7.252e-01 0.78954 9.200e-01 u2[54] -1.00178 -0.70238 -5.583e-01 -0.41310 -1.298e-01 u2[55] 0.29243 0.43079 5.034e-01 0.57536 7.165e-01 u2[56] -0.22395 -0.07035 1.123e-02 0.09030 2.505e-01 u2[57] -0.16432 -0.03608 3.016e-02 0.09924 2.318e-01 u2[58] -0.08910 0.06045 1.401e-01 0.21667 3.720e-01 u2[59] -0.88544 -0.74082 -6.620e-01 -0.58779 -4.435e-01 u2[6] 0.36715 0.48211 5.413e-01 0.60197 7.169e-01 u2[60] 0.04991 0.16342 2.233e-01 0.28271 4.004e-01 u2[61] -0.22960 -0.10649 -4.017e-02 0.02906 1.496e-01 u2[62] -0.23900 -0.11565 -5.425e-02 0.01042 1.287e-01 u2[63] 0.27629 0.45130 5.415e-01 0.62940 8.043e-01 u2[64] -0.11140 0.02141 8.997e-02 0.15588 2.836e-01 u2[65] -0.34591 -0.22788 -1.663e-01 -0.10567 8.141e-03 u2[7] 0.21536 0.32092 3.774e-01 0.43564 5.534e-01 u2[8] -0.18612 -0.08318 -2.636e-02 0.02743 1.335e-01 u2[9] -0.38160 -0.21792 -1.353e-01 -0.05498 1.081e-01 > sixway(chains.bugs1[, "df", drop = FALSE]) > > chains.bugs2 <- mlwin2bugs(D = "t", levID = c("school", "student"), datafile, initfile, modelfile, bugEst, fact = NULL, + addmore = c("sigma2", "df"), n.chains = 1, n.iter = 12000, n.burnin = 2000, n.thin = 1, debug = TRUE, bugsWorkingDir = tempdir(), + OpenBugs = TRUE) model is syntactically correct data loaded model compiled Initializing chain 1: model is initialized model is already initialized Sampling has been started ... 2000 updates took 1 s deviance set monitor set for variable 'beta' monitor set for variable 'u2' monitor set for variable 'sigma2.u2' monitor set for variable 'sigma2' monitor set for variable 'df' monitor set for variable 'deviance' 10000 updates took 19 s > ## Close winbugs manually > summary(chains.bugs2) Iterations = 2001:12000 Thinning interval = 1 Number of chains = 1 Sample size per chain = 10000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta[1] -1.530e-03 0.04117 0.0004117 0.0019987 beta[2] 5.632e-01 0.01243 0.0001243 0.0001373 deviance 9.209e+03 11.88862 0.1188862 0.1224548 df 1.098e+02 53.56133 0.5356133 2.8728472 sigma2 5.663e-01 0.01272 0.0001272 0.0001272 sigma2.u2 9.417e-02 0.02020 0.0002020 0.0003761 u2[1] 3.768e-01 0.09330 0.0009330 0.0020602 u2[10] -3.332e-01 0.10578 0.0010578 0.0018676 u2[11] 1.838e-01 0.09916 0.0009916 0.0021441 u2[12] -5.852e-02 0.10958 0.0010958 0.0020728 u2[13] -1.454e-01 0.09727 0.0009727 0.0018973 u2[14] -1.618e-01 0.06645 0.0006645 0.0020350 u2[15] -1.783e-01 0.08601 0.0008601 0.0020290 u2[16] -4.048e-01 0.08790 0.0008790 0.0019331 u2[17] -1.675e-01 0.07644 0.0007644 0.0020227 u2[18] -8.028e-02 0.07737 0.0007737 0.0020054 u2[19] -6.805e-03 0.10284 0.0010284 0.0020280 u2[2] 5.068e-01 0.10570 0.0010570 0.0021351 u2[20] 2.157e-01 0.11784 0.0011784 0.0020056 u2[21] 2.469e-01 0.09352 0.0009352 0.0020824 u2[22] -4.321e-01 0.08612 0.0008612 0.0018871 u2[23] -4.896e-01 0.13479 0.0013479 0.0019346 u2[24] 2.125e-01 0.12102 0.0012102 0.0021663 u2[25] -2.278e-01 0.09230 0.0009230 0.0019627 u2[26] -1.938e-02 0.09132 0.0009132 0.0018157 u2[27] 2.762e-02 0.11729 0.0011729 0.0020742 u2[28] -6.072e-01 0.10204 0.0010204 0.0019394 u2[29] 2.434e-01 0.09020 0.0009020 0.0021398 u2[3] 5.088e-01 0.10646 0.0010646 0.0019381 u2[30] 1.632e-01 0.11473 0.0011473 0.0021204 u2[31] 3.728e-02 0.10656 0.0010656 0.0019407 u2[32] -2.241e-03 0.11492 0.0011492 0.0019030 u2[33] 3.474e-02 0.09092 0.0009092 0.0019111 u2[34] -1.335e-01 0.13688 0.0013688 0.0019457 u2[35] 1.308e-01 0.11894 0.0011894 0.0019877 u2[36] -1.769e-01 0.09423 0.0009423 0.0018497 u2[37] -1.842e-01 0.14660 0.0014660 0.0019488 u2[38] -1.510e-01 0.10308 0.0010308 0.0018479 u2[39] 1.320e-01 0.10944 0.0010944 0.0021785 u2[4] 2.231e-02 0.08882 0.0008882 0.0018996 u2[40] -2.305e-01 0.09464 0.0009464 0.0019596 u2[41] 2.164e-01 0.10085 0.0010085 0.0019606 u2[42] 9.442e-02 0.10057 0.0010057 0.0019009 u2[43] -8.540e-02 0.09937 0.0009937 0.0020010 u2[44] -2.449e-01 0.13312 0.0013312 0.0020720 u2[45] -1.062e-01 0.10386 0.0010386 0.0019288 u2[46] -3.481e-01 0.08783 0.0008783 0.0019088 u2[47] -3.854e-02 0.08822 0.0008822 0.0020278 u2[48] -4.007e-02 0.26723 0.0026723 0.0026723 u2[49] 4.569e-02 0.07998 0.0007998 0.0020175 u2[5] 2.420e-01 0.12273 0.0012273 0.0020528 u2[50] -2.996e-01 0.09428 0.0009428 0.0020431 u2[51] -4.788e-02 0.10156 0.0010156 0.0019658 u2[52] 3.856e-01 0.09935 0.0009935 0.0020452 u2[53] 7.290e-01 0.09669 0.0009669 0.0020222 u2[54] -5.563e-01 0.21448 0.0021448 0.0025663 u2[55] 5.084e-01 0.10827 0.0010827 0.0019931 u2[56] 1.374e-02 0.11937 0.0011937 0.0020370 u2[57] 3.611e-02 0.09871 0.0009871 0.0021438 u2[58] 1.412e-01 0.11782 0.0011782 0.0017951 u2[59] -6.596e-01 0.11285 0.0011285 0.0019918 u2[6] 5.458e-01 0.09095 0.0009095 0.0020119 u2[60] 2.288e-01 0.09004 0.0009004 0.0021044 u2[61] -3.654e-02 0.09712 0.0009712 0.0019972 u2[62] -4.823e-02 0.09298 0.0009298 0.0018880 u2[63] 5.421e-01 0.13282 0.0013282 0.0022368 u2[64] 9.316e-02 0.09962 0.0009962 0.0019034 u2[65] -1.620e-01 0.09034 0.0009034 0.0019899 u2[7] 3.825e-01 0.08602 0.0008602 0.0020351 u2[8] -2.144e-02 0.08255 0.0008255 0.0019351 u2[9] -1.336e-01 0.12350 0.0012350 0.0020263 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta[1] -8.105e-02 -2.980e-02 -1.846e-03 0.02651 7.916e-02 beta[2] 5.385e-01 5.549e-01 5.631e-01 0.57172 5.871e-01 deviance 9.188e+03 9.201e+03 9.208e+03 9216.58374 9.234e+03 df 1.436e+01 6.622e+01 1.111e+02 156.13959 1.955e+02 sigma2 5.423e-01 5.575e-01 5.663e-01 0.57463 5.922e-01 sigma2.u2 6.113e-02 8.000e-02 9.173e-02 0.10594 1.395e-01 u2[1] 1.980e-01 3.142e-01 3.761e-01 0.43885 5.626e-01 u2[10] -5.424e-01 -4.058e-01 -3.315e-01 -0.26192 -1.271e-01 u2[11] -1.060e-02 1.162e-01 1.839e-01 0.24971 3.767e-01 u2[12] -2.799e-01 -1.317e-01 -5.909e-02 0.01649 1.542e-01 u2[13] -3.327e-01 -2.111e-01 -1.466e-01 -0.08035 4.630e-02 u2[14] -2.913e-01 -2.066e-01 -1.620e-01 -0.11761 -3.046e-02 u2[15] -3.435e-01 -2.368e-01 -1.787e-01 -0.11981 -1.140e-02 u2[16] -5.765e-01 -4.642e-01 -4.045e-01 -0.34546 -2.286e-01 u2[17] -3.176e-01 -2.190e-01 -1.671e-01 -0.11546 -1.871e-02 u2[18] -2.329e-01 -1.326e-01 -7.937e-02 -0.02808 7.098e-02 u2[19] -2.076e-01 -7.659e-02 -5.363e-03 0.06415 1.937e-01 u2[2] 2.984e-01 4.359e-01 5.071e-01 0.57870 7.093e-01 u2[20] -1.563e-02 1.370e-01 2.152e-01 0.29522 4.474e-01 u2[21] 6.610e-02 1.827e-01 2.465e-01 0.31092 4.322e-01 u2[22] -6.006e-01 -4.903e-01 -4.318e-01 -0.37373 -2.654e-01 u2[23] -7.543e-01 -5.778e-01 -4.889e-01 -0.39995 -2.306e-01 u2[24] -2.585e-02 1.309e-01 2.142e-01 0.29492 4.525e-01 u2[25] -4.125e-01 -2.911e-01 -2.279e-01 -0.16468 -4.790e-02 u2[26] -1.999e-01 -8.033e-02 -1.878e-02 0.04290 1.566e-01 u2[27] -2.023e-01 -5.251e-02 2.857e-02 0.10756 2.573e-01 u2[28] -8.064e-01 -6.761e-01 -6.071e-01 -0.53778 -4.096e-01 u2[29] 6.949e-02 1.818e-01 2.431e-01 0.30373 4.225e-01 u2[3] 3.009e-01 4.369e-01 5.091e-01 0.58070 7.171e-01 u2[30] -6.028e-02 8.664e-02 1.634e-01 0.23976 3.893e-01 u2[31] -1.740e-01 -3.458e-02 3.806e-02 0.10891 2.426e-01 u2[32] -2.279e-01 -7.808e-02 -2.184e-03 0.07466 2.247e-01 u2[33] -1.434e-01 -2.651e-02 3.491e-02 0.09543 2.128e-01 u2[34] -4.032e-01 -2.246e-01 -1.324e-01 -0.04224 1.352e-01 u2[35] -1.032e-01 5.011e-02 1.314e-01 0.21083 3.613e-01 u2[36] -3.594e-01 -2.415e-01 -1.753e-01 -0.11344 8.897e-03 u2[37] -4.729e-01 -2.834e-01 -1.841e-01 -0.08435 1.062e-01 u2[38] -3.531e-01 -2.195e-01 -1.512e-01 -0.08141 5.106e-02 u2[39] -7.875e-02 5.921e-02 1.308e-01 0.20472 3.488e-01 u2[4] -1.520e-01 -3.811e-02 2.309e-02 0.08173 1.960e-01 u2[40] -4.164e-01 -2.944e-01 -2.313e-01 -0.16708 -4.467e-02 u2[41] 2.033e-02 1.475e-01 2.154e-01 0.28493 4.142e-01 u2[42] -1.026e-01 2.630e-02 9.435e-02 0.16288 2.890e-01 u2[43] -2.804e-01 -1.512e-01 -8.610e-02 -0.01878 1.095e-01 u2[44] -5.000e-01 -3.355e-01 -2.446e-01 -0.15743 2.198e-02 u2[45] -3.083e-01 -1.738e-01 -1.053e-01 -0.03805 9.502e-02 u2[46] -5.191e-01 -4.073e-01 -3.480e-01 -0.28895 -1.781e-01 u2[47] -2.090e-01 -9.855e-02 -4.002e-02 0.02146 1.374e-01 u2[48] -5.581e-01 -2.178e-01 -3.912e-02 0.13876 4.910e-01 u2[49] -1.120e-01 -7.888e-03 4.534e-02 0.10038 1.992e-01 u2[5] -2.354e-03 1.606e-01 2.430e-01 0.32617 4.804e-01 u2[50] -4.852e-01 -3.635e-01 -3.002e-01 -0.23494 -1.147e-01 u2[51] -2.478e-01 -1.142e-01 -4.837e-02 0.02137 1.472e-01 u2[52] 1.908e-01 3.192e-01 3.868e-01 0.45090 5.812e-01 u2[53] 5.367e-01 6.652e-01 7.291e-01 0.79215 9.200e-01 u2[54] -9.866e-01 -6.982e-01 -5.527e-01 -0.40918 -1.434e-01 u2[55] 2.992e-01 4.345e-01 5.072e-01 0.58161 7.209e-01 u2[56] -2.220e-01 -6.636e-02 1.459e-02 0.09406 2.452e-01 u2[57] -1.582e-01 -3.041e-02 3.624e-02 0.10223 2.301e-01 u2[58] -8.975e-02 6.178e-02 1.421e-01 0.22048 3.720e-01 u2[59] -8.856e-01 -7.360e-01 -6.581e-01 -0.58247 -4.391e-01 u2[6] 3.662e-01 4.849e-01 5.449e-01 0.60766 7.254e-01 u2[60] 5.345e-02 1.698e-01 2.290e-01 0.28853 4.064e-01 u2[61] -2.281e-01 -1.022e-01 -3.751e-02 0.02980 1.505e-01 u2[62] -2.323e-01 -1.107e-01 -4.903e-02 0.01501 1.353e-01 u2[63] 2.864e-01 4.528e-01 5.410e-01 0.63062 8.034e-01 u2[64] -1.040e-01 2.717e-02 9.275e-02 0.16024 2.871e-01 u2[65] -3.397e-01 -2.224e-01 -1.625e-01 -0.10194 1.612e-02 u2[7] 2.147e-01 3.245e-01 3.822e-01 0.44044 5.522e-01 u2[8] -1.812e-01 -7.788e-02 -2.108e-02 0.03392 1.412e-01 u2[9] -3.771e-01 -2.161e-01 -1.341e-01 -0.05081 1.091e-01 > sixway(chains.bugs2[, "df", drop = FALSE]) > > # Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . . 96 > > > > > > ############################################################################ > > proc.time() user system elapsed 15.95 24.10 652.93