R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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. 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 2013-2024 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 v3.11/ 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: R2OpenBUGS > > 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 11.93887 0.1688411 0.1774720 sigma2 5.665e-01 0.01272 0.0001800 0.0001800 sigma2.u2 9.661e-02 0.02033 0.0002875 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 0.0028449 u2[12] -6.347e-02 0.10478 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 0.0025539 u2[18] -8.481e-02 0.07750 0.0010961 0.0023982 u2[19] -1.598e-02 0.10214 0.0014445 0.0027279 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 0.11815 0.0016708 0.0024794 u2[28] -6.128e-01 0.10190 0.0014411 0.0021463 u2[29] 2.367e-01 0.09060 0.0012813 0.0026303 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 0.10675 0.0015096 0.0024175 u2[32] -8.584e-03 0.11330 0.0016023 0.0021816 u2[33] 2.871e-02 0.08998 0.0012725 0.0024803 u2[34] -1.389e-01 0.13743 0.0019436 0.0027088 u2[35] 1.268e-01 0.11843 0.0016749 0.0025395 u2[36] -1.860e-01 0.09333 0.0013199 0.0024271 u2[37] -1.917e-01 0.14603 0.0020652 0.0027079 u2[38] -1.540e-01 0.10202 0.0014427 0.0023468 u2[39] 1.280e-01 0.10793 0.0015264 0.0021961 u2[4] 1.610e-02 0.08936 0.0012637 0.0023718 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 0.0014605 0.0026293 u2[46] -3.553e-01 0.08749 0.0012373 0.0025947 u2[47] -4.387e-02 0.08717 0.0012328 0.0022481 u2[48] -4.284e-02 0.26600 0.0037618 0.0040374 u2[49] 3.955e-02 0.07841 0.0011088 0.0024538 u2[5] 2.384e-01 0.12228 0.0017293 0.0026789 u2[50] -3.024e-01 0.09247 0.0013077 0.0023566 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 0.0028080 u2[54] -5.580e-01 0.20835 0.0029465 0.0031467 u2[55] 5.034e-01 0.10685 0.0015110 0.0026525 u2[56] 9.350e-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 0.0025391 u2[63] 5.348e-01 0.13310 0.0018823 0.0029428 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 9201.00000 9.209e+03 9.216e+03 9.235e+03 sigma2 5.420e-01 0.55770 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 0.31038 3.700e-01 4.348e-01 5.531e-01 u2[10] -5.567e-01 -0.41330 -3.407e-01 -2.678e-01 -1.307e-01 u2[11] -1.924e-02 0.11380 1.780e-01 2.451e-01 3.638e-01 u2[12] -2.695e-01 -0.13462 -6.151e-02 7.704e-03 1.414e-01 u2[13] -3.458e-01 -0.21673 -1.507e-01 -8.769e-02 3.868e-02 u2[14] -2.979e-01 -0.21100 -1.685e-01 -1.242e-01 -4.027e-02 u2[15] -3.470e-01 -0.24302 -1.851e-01 -1.279e-01 -1.984e-02 u2[16] -5.808e-01 -0.46850 -4.111e-01 -3.542e-01 -2.443e-01 u2[17] -3.263e-01 -0.22503 -1.733e-01 -1.247e-01 -2.610e-02 u2[18] -2.366e-01 -0.13572 -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 0.43367 5.030e-01 5.725e-01 6.988e-01 u2[20] -1.339e-02 0.13758 2.151e-01 2.945e-01 4.481e-01 u2[21] 6.441e-02 0.18077 2.441e-01 3.079e-01 4.278e-01 u2[22] -6.080e-01 -0.49380 -4.375e-01 -3.811e-01 -2.742e-01 u2[23] -7.584e-01 -0.58543 -4.925e-01 -3.982e-01 -2.294e-01 u2[24] -2.385e-02 0.12420 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 -0.68272 -6.121e-01 -5.438e-01 -4.137e-01 u2[29] 5.583e-02 0.17510 2.379e-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 2.310e-01 3.758e-01 u2[31] -1.763e-01 -0.03758 3.338e-02 1.049e-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 8.897e-02 2.012e-01 u2[34] -4.025e-01 -0.23283 -1.397e-01 -4.531e-02 1.227e-01 u2[35] -1.083e-01 0.04956 1.275e-01 2.074e-01 3.659e-01 u2[36] -3.697e-01 -0.24985 -1.842e-01 -1.222e-01 -5.621e-03 u2[37] -4.779e-01 -0.28830 -1.926e-01 -9.295e-02 9.370e-02 u2[38] -3.549e-01 -0.22330 -1.545e-01 -8.453e-02 4.369e-02 u2[39] -8.498e-02 0.05423 1.267e-01 2.013e-01 3.427e-01 u2[4] -1.575e-01 -0.04274 1.580e-02 7.654e-02 1.910e-01 u2[40] -4.187e-01 -0.29752 -2.354e-01 -1.740e-01 -5.980e-02 u2[41] 1.806e-02 0.14620 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 -0.15363 -8.973e-02 -2.526e-02 1.039e-01 u2[44] -5.058e-01 -0.34010 -2.500e-01 -1.611e-01 5.718e-03 u2[45] -3.135e-01 -0.18070 -1.116e-01 -4.375e-02 8.561e-02 u2[46] -5.244e-01 -0.41540 -3.553e-01 -2.956e-01 -1.802e-01 u2[47] -2.159e-01 -0.10180 -4.417e-02 1.411e-02 1.240e-01 u2[48] -5.711e-01 -0.22135 -4.104e-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 0.15610 2.383e-01 3.214e-01 4.796e-01 u2[50] -4.804e-01 -0.36520 -3.015e-01 -2.379e-01 -1.210e-01 u2[51] -2.471e-01 -0.12350 -5.610e-02 1.189e-02 1.399e-01 u2[52] 1.855e-01 0.31430 3.811e-01 4.461e-01 5.732e-01 u2[53] 5.348e-01 0.65460 7.198e-01 7.863e-01 9.090e-01 u2[54] -9.821e-01 -0.69342 -5.539e-01 -4.125e-01 -1.631e-01 u2[55] 2.947e-01 0.43210 5.013e-01 5.742e-01 7.148e-01 u2[56] -2.166e-01 -0.06950 8.965e-03 8.694e-02 2.408e-01 u2[57] -1.590e-01 -0.03566 2.851e-02 9.574e-02 2.187e-01 u2[58] -9.771e-02 0.05955 1.413e-01 2.219e-01 3.754e-01 u2[59] -8.850e-01 -0.73463 -6.595e-01 -5.857e-01 -4.445e-01 u2[6] 3.662e-01 0.47970 5.406e-01 6.018e-01 7.157e-01 u2[60] 5.461e-02 0.16460 2.243e-01 2.848e-01 3.989e-01 u2[61] -2.335e-01 -0.10642 -4.228e-02 2.342e-02 1.447e-01 u2[62] -2.341e-01 -0.11782 -5.477e-02 5.279e-03 1.222e-01 u2[63] 2.712e-01 0.44430 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 -0.22562 -1.683e-01 -1.072e-01 9.315e-04 u2[7] 2.075e-01 0.31967 3.788e-01 4.374e-01 5.493e-01 u2[8] -1.871e-01 -0.08283 -2.833e-02 2.689e-02 1.362e-01 u2[9] -3.810e-01 -0.22002 -1.391e-01 -5.585e-02 9.811e-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 0.5635179 0.0125139 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("https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_model.txt", modelfile, method = "auto") trying URL 'https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_model.txt' Content type 'text/plain; charset=UTF-8' length 557 bytes ================================================== downloaded 557 bytes > file.show(modelfile) > > initfile <- paste0(tempdir(), "/tutorial1_inits.txt") > download.file("https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_inits.txt", initfile, method = "auto") trying URL 'https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_inits.txt' Content type 'text/plain; charset=UTF-8' length 780 bytes ================================================== downloaded 780 bytes > file.show(initfile) > > datafile <- paste0(tempdir(), "/tutorial1_data.txt") > download.file("https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_data.txt", datafile, method = "auto") trying URL 'https://www.bristol.ac.uk/cmm/media/r2mlwin/tutorial1_data.txt' Content type 'text/plain; charset=UTF-8' length 128091 bytes (125 KB) ================================================== downloaded 125 KB > > bugEst <- paste0(tempdir(), "/tutorial1_log.txt") > > > chains.bugs1 <- mlwin2bugs(datafile, initfile, modelfile, parameters = c("beta", "sigma2", "u2", "sigma2.u2", "df"), + n.chains = 1, n.iter = 5500, n.burnin = 500, n.thin = 1, debug = TRUE, bugsWorkingDir = tempdir(), + OpenBugs = TRUE) > ## 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 12.16633 0.1720578 0.1831897 df 1.085e+02 55.41030 0.7836199 4.8289364 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 0.0029811 u2[10] -3.367e-01 0.10397 0.0014704 0.0023876 u2[11] 1.801e-01 0.09888 0.0013984 0.0024701 u2[12] -6.269e-02 0.10962 0.0015503 0.0031496 u2[13] -1.488e-01 0.09662 0.0013664 0.0028148 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 0.0028115 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 0.0014655 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 0.0029438 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 0.0028806 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 0.0027890 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] -6.904e-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 0.0030015 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 0.0014356 0.0023768 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 0.0028745 u2[41] 2.132e-01 0.10063 0.0014231 0.0028351 u2[42] 8.996e-02 0.09971 0.0014102 0.0028349 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 0.0025583 u2[51] -5.472e-02 0.10013 0.0014160 0.0027751 u2[52] 3.807e-01 0.09872 0.0013961 0.0029147 u2[53] 7.258e-01 0.09799 0.0013857 0.0029537 u2[54] -5.615e-01 0.21869 0.0030927 0.0037486 u2[55] 5.032e-01 0.10734 0.0015181 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 0.0015941 0.0025736 u2[6] 5.415e-01 0.08970 0.0012685 0.0027371 u2[60] 2.235e-01 0.09010 0.0012741 0.0029205 u2[61] -3.968e-02 0.09846 0.0013924 0.0028056 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 0.0029365 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] 0.53800 0.55450 5.629e-01 0.57190 5.872e-01 deviance 9188.00000 9201.00000 9.209e+03 9217.00000 9.235e+03 df 9.88843 63.26500 1.129e+02 155.40000 1.956e+02 sigma2 0.54170 0.55760 5.664e-01 0.57470 5.920e-01 sigma2.u2 0.05978 0.07938 9.161e-02 0.10570 1.398e-01 u2[1] 0.19059 0.30755 3.700e-01 0.43042 5.591e-01 u2[10] -0.53740 -0.40910 -3.379e-01 -0.26530 -1.314e-01 u2[11] -0.01029 0.11275 1.805e-01 0.24540 3.744e-01 u2[12] -0.28541 -0.13522 -6.088e-02 0.01140 1.482e-01 u2[13] -0.33470 -0.21363 -1.492e-01 -0.08376 4.203e-02 u2[14] -0.29411 -0.20860 -1.657e-01 -0.12150 -3.544e-02 u2[15] -0.35251 -0.24213 -1.830e-01 -0.12220 -1.699e-02 u2[16] -0.57962 -0.46850 -4.084e-01 -0.34887 -2.407e-01 u2[17] -0.32200 -0.22590 -1.734e-01 -0.12198 -2.132e-02 u2[18] -0.23660 -0.13770 -8.553e-02 -0.03326 6.506e-02 u2[19] -0.21041 -0.08327 -9.346e-03 0.05734 1.978e-01 u2[2] 0.29720 0.43138 5.029e-01 0.57360 7.066e-01 u2[20] -0.01841 0.13267 2.125e-01 0.29410 4.460e-01 u2[21] 0.05888 0.17818 2.420e-01 0.30462 4.286e-01 u2[22] -0.60570 -0.49672 -4.367e-01 -0.37870 -2.735e-01 u2[23] -0.75550 -0.57955 -4.894e-01 -0.39958 -2.311e-01 u2[24] -0.02902 0.12750 2.101e-01 0.28982 4.478e-01 u2[25] -0.41192 -0.29363 -2.303e-01 -0.16620 -5.173e-02 u2[26] -0.20620 -0.08768 -2.530e-02 0.03589 1.492e-01 u2[27] -0.21090 -0.06034 2.173e-02 0.10062 2.497e-01 u2[28] -0.81060 -0.68232 -6.115e-01 -0.54130 -4.133e-01 u2[29] 0.07011 0.17880 2.397e-01 0.29970 4.174e-01 u2[3] 0.29749 0.43470 5.066e-01 0.57970 7.045e-01 u2[30] -0.06500 0.08304 1.597e-01 0.23723 3.814e-01 u2[31] -0.17860 -0.03764 3.417e-02 0.10483 2.415e-01 u2[32] -0.22884 -0.08571 -5.606e-03 0.07103 2.183e-01 u2[33] -0.14640 -0.03119 3.029e-02 0.09118 2.125e-01 u2[34] -0.41742 -0.23220 -1.395e-01 -0.04906 1.354e-01 u2[35] -0.10391 0.04822 1.321e-01 0.20830 3.535e-01 u2[36] -0.35970 -0.24280 -1.787e-01 -0.11770 7.821e-03 u2[37] -0.46780 -0.28670 -1.874e-01 -0.08754 1.096e-01 u2[38] -0.34840 -0.22120 -1.537e-01 -0.08414 4.530e-02 u2[39] -0.08333 0.05501 1.281e-01 0.20223 3.439e-01 u2[4] -0.16270 -0.04247 1.644e-02 0.07553 1.854e-01 u2[40] -0.41160 -0.29635 -2.345e-01 -0.16970 -4.998e-02 u2[41] 0.02006 0.14567 2.112e-01 0.28213 4.089e-01 u2[42] -0.10841 0.02282 8.984e-02 0.15732 2.813e-01 u2[43] -0.28251 -0.15660 -8.837e-02 -0.02120 1.056e-01 u2[44] -0.51181 -0.34053 -2.483e-01 -0.15677 1.338e-02 u2[45] -0.31861 -0.17805 -1.091e-01 -0.04203 9.229e-02 u2[46] -0.52540 -0.41123 -3.511e-01 -0.29240 -1.793e-01 u2[47] -0.21361 -0.10253 -4.378e-02 0.01884 1.252e-01 u2[48] -0.55053 -0.22273 -3.929e-02 0.13722 4.918e-01 u2[49] -0.11730 -0.01287 4.106e-02 0.09495 1.966e-01 u2[5] -0.00691 0.15910 2.393e-01 0.32062 4.709e-01 u2[50] -0.48840 -0.36675 -3.038e-01 -0.24057 -1.182e-01 u2[51] -0.24613 -0.12213 -5.609e-02 0.01499 1.338e-01 u2[52] 0.18819 0.31400 3.804e-01 0.44682 5.783e-01 u2[53] 0.53180 0.66068 7.252e-01 0.78953 9.199e-01 u2[54] -1.00200 -0.70240 -5.583e-01 -0.41313 -1.298e-01 u2[55] 0.29240 0.43078 5.034e-01 0.57540 7.165e-01 u2[56] -0.22400 -0.07035 1.123e-02 0.09030 2.505e-01 u2[57] -0.16431 -0.03608 3.016e-02 0.09924 2.318e-01 u2[58] -0.08910 0.06045 1.401e-01 0.21670 3.720e-01 u2[59] -0.88541 -0.74080 -6.620e-01 -0.58780 -4.435e-01 u2[6] 0.36719 0.48210 5.413e-01 0.60200 7.169e-01 u2[60] 0.04991 0.16340 2.233e-01 0.28270 4.004e-01 u2[61] -0.22963 -0.10650 -4.016e-02 0.02906 1.495e-01 u2[62] -0.23901 -0.11563 -5.425e-02 0.01042 1.287e-01 u2[63] 0.27627 0.45130 5.415e-01 0.62940 8.043e-01 u2[64] -0.11140 0.02141 8.997e-02 0.15590 2.836e-01 u2[65] -0.34590 -0.22792 -1.663e-01 -0.10570 8.141e-03 u2[7] 0.21540 0.32090 3.774e-01 0.43565 5.534e-01 u2[8] -0.18611 -0.08318 -2.636e-02 0.02743 1.335e-01 u2[9] -0.38160 -0.21795 -1.353e-01 -0.05498 1.081e-01 > sixway(chains.bugs1[, "df", drop = FALSE]) > > chains.bugs2 <- mlwin2bugs(datafile, initfile, modelfile, parameters = c("beta", "sigma2", "u2", "sigma2.u2", "df"), + n.chains = 1, n.iter = 12000, n.burnin = 2000, n.thin = 1, debug = TRUE, bugsWorkingDir = tempdir(), + OpenBugs = TRUE) > ## 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 11.89012 0.1189012 0.1224555 df 1.098e+02 53.56165 0.5356165 2.8728023 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 0.0020349 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 0.0021350 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 0.0021662 u2[25] -2.278e-01 0.09230 0.0009230 0.0019627 u2[26] -1.938e-02 0.09132 0.0009132 0.0018158 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 0.0021399 u2[3] 5.088e-01 0.10646 0.0010646 0.0019381 u2[30] 1.632e-01 0.11473 0.0011473 0.0021205 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 0.0019876 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 0.0018478 u2[39] 1.320e-01 0.10944 0.0010944 0.0021785 u2[4] 2.231e-02 0.08882 0.0008882 0.0018995 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 0.0020277 u2[48] -4.007e-02 0.26724 0.0026724 0.0026724 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] -4.789e-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 0.0021439 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 0.0019971 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 0.0020350 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 -2.981e-02 -1.846e-03 0.02651 7.916e-02 beta[2] 5.385e-01 5.549e-01 5.631e-01 0.57170 5.871e-01 deviance 9.188e+03 9.201e+03 9.208e+03 9217.00000 9.234e+03 df 1.436e+01 6.622e+01 1.111e+02 156.12500 1.955e+02 sigma2 5.423e-01 5.575e-01 5.663e-01 0.57460 5.922e-01 sigma2.u2 6.114e-02 8.000e-02 9.173e-02 0.10590 1.395e-01 u2[1] 1.980e-01 3.142e-01 3.761e-01 0.43883 5.626e-01 u2[10] -5.424e-01 -4.058e-01 -3.315e-01 -0.26190 -1.271e-01 u2[11] -1.060e-02 1.162e-01 1.840e-01 0.24973 3.767e-01 u2[12] -2.799e-01 -1.317e-01 -5.910e-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 -1.619e-01 -0.11760 -3.046e-02 u2[15] -3.434e-01 -2.368e-01 -1.787e-01 -0.11980 -1.140e-02 u2[16] -5.765e-01 -4.642e-01 -4.045e-01 -0.34547 -2.286e-01 u2[17] -3.176e-01 -2.190e-01 -1.671e-01 -0.11548 -1.871e-02 u2[18] -2.329e-01 -1.325e-01 -7.937e-02 -0.02808 7.097e-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 0.29520 4.474e-01 u2[21] 6.611e-02 1.827e-01 2.465e-01 0.31090 4.322e-01 u2[22] -6.006e-01 -4.903e-01 -4.318e-01 -0.37370 -2.654e-01 u2[23] -7.543e-01 -5.778e-01 -4.889e-01 -0.39998 -2.306e-01 u2[24] -2.585e-02 1.309e-01 2.142e-01 0.29490 4.525e-01 u2[25] -4.125e-01 -2.911e-01 -2.279e-01 -0.16470 -4.790e-02 u2[26] -1.999e-01 -8.033e-02 -1.878e-02 0.04290 1.566e-01 u2[27] -2.022e-01 -5.250e-02 2.857e-02 0.10760 2.573e-01 u2[28] -8.064e-01 -6.760e-01 -6.070e-01 -0.53780 -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 0.23972 3.893e-01 u2[31] -1.740e-01 -3.458e-02 3.806e-02 0.10890 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 1.315e-01 0.21082 3.612e-01 u2[36] -3.594e-01 -2.415e-01 -1.753e-01 -0.11348 8.897e-03 u2[37] -4.729e-01 -2.834e-01 -1.841e-01 -0.08436 1.062e-01 u2[38] -3.531e-01 -2.195e-01 -1.512e-01 -0.08140 5.106e-02 u2[39] -7.876e-02 5.921e-02 1.308e-01 0.20470 3.488e-01 u2[4] -1.520e-01 -3.810e-02 2.309e-02 0.08172 1.960e-01 u2[40] -4.164e-01 -2.944e-01 -2.313e-01 -0.16710 -4.467e-02 u2[41] 2.033e-02 1.475e-01 2.153e-01 0.28490 4.142e-01 u2[42] -1.026e-01 2.630e-02 9.435e-02 0.16290 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 -2.445e-01 -0.15740 2.197e-02 u2[45] -3.083e-01 -1.738e-01 -1.053e-01 -0.03805 9.502e-02 u2[46] -5.191e-01 -4.072e-01 -3.480e-01 -0.28897 -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 0.13880 4.910e-01 u2[49] -1.120e-01 -7.888e-03 4.534e-02 0.10043 1.992e-01 u2[5] -2.354e-03 1.606e-01 2.430e-01 0.32620 4.804e-01 u2[50] -4.852e-01 -3.635e-01 -3.002e-01 -0.23490 -1.147e-01 u2[51] -2.478e-01 -1.142e-01 -4.838e-02 0.02136 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 0.79212 9.199e-01 u2[54] -9.866e-01 -6.982e-01 -5.527e-01 -0.40920 -1.434e-01 u2[55] 2.992e-01 4.345e-01 5.072e-01 0.58160 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 0.10220 2.301e-01 u2[58] -8.975e-02 6.179e-02 1.421e-01 0.22050 3.720e-01 u2[59] -8.856e-01 -7.360e-01 -6.581e-01 -0.58248 -4.391e-01 u2[6] 3.662e-01 4.850e-01 5.449e-01 0.60770 7.254e-01 u2[60] 5.345e-02 1.698e-01 2.289e-01 0.28852 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] 2.865e-01 4.528e-01 5.410e-01 0.63062 8.033e-01 u2[64] -1.040e-01 2.717e-02 9.275e-02 0.16023 2.871e-01 u2[65] -3.397e-01 -2.224e-01 -1.626e-01 -0.10190 1.612e-02 u2[7] 2.148e-01 3.245e-01 3.822e-01 0.44040 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 9.34 1.14 369.21