Clinician's
corner

Back to main page

Programmer's
corner
So, you use WinBUGS a lot? Want more?
Patrick Blisle
Division of Clinical Epidemiology
McGill University Health Center
Montreal, Quebec CANADA
patrick.belisle@rimuhc.ca

Last modification: 28 mar 2012















Version 1.8 (July 2015)
WinBUGSlogs2HtmlSummary
Summarizing WinBUGS output files into an html table
[ WinBUGSLogs2htmlSummary is a Perl program to write easy-to-read summaries of Node Statistics found in a series of WinBUGS text log files. ]


When running WinBUGS programs, one often runs the same program several times but with small variations, such as trying out different initial values, different prior distributions, or different data sets. Following these runs, one would usually want to compare the results across runs, contained in several distinct WinBUGS log files.

It is not convenient to compare results saved in different WinBUGS log files, so some file manipulations, either on screen or on hard printed copies, are required. The program described on this page, called WinBUGSLogs2HtmlSummary, makes such comparisons easy with only a few mouse clicks. WinBUGSLogs2HtmlSummary is both easy to install and easy to use.

Once it is installed and a shortcut to it has been placed on Desktop, you simply drag-n-drop the WinBUGS text log files to compare onto WinBUGSLogs2HtmlSummary's Desktop Icon and a pop-up form allows you to select nodes to include in summary, change output file name, enter a relevant title, select output maximum width, etc.


Menu



Top
Example

To illustrate the use of WinBUGSLogs2HtmlSummary, we will use the rats example found in WinBUGS documentation, version 1.4.1. The models, data, and inits files are given at the bottom of this page. Suppose for the moment that you have run the two different models presented therein and saved the results to two WinBUGS text log files, named respectively dGammaPrecision-WinBUGSlog.txt and UniformSD-WinBUGSlog.txt, from which the Node statistics are reproduced below, in order.
Node statistics
	node       mean        sd        MC error  2.5%        median      97.5%      start  sample
	alpha[1]   239.9       2.671     0.02855   234.7       239.8       245.2      1001   10000
	alpha[2]   247.8       2.642     0.02543   242.6       247.8       253.0      1001   10000
	alpha[3]   252.4       2.677     0.02487   247.2       252.4       257.6      1001   10000
	alpha[4]   232.6       2.671     0.02729   227.3       232.6       237.8      1001   10000
	alpha[5]   231.6       2.677     0.02386   226.3       231.6       236.9      1001   10000
	alpha[6]   249.7       2.673     0.02963   244.4       249.8       255.0      1001   10000
	alpha[7]   228.7       2.647     0.02826   223.6       228.7       233.9      1001   10000
	alpha[8]   248.3       2.702     0.02836   243.1       248.4       253.6      1001   10000
	alpha[9]   283.3       2.704     0.02751   277.9       283.3       288.6      1001   10000
	alpha[10]  219.3       2.692     0.02606   214.1       219.3       224.6      1001   10000
	alpha[11]  258.2       2.677     0.02809   253.1       258.2       263.5      1001   10000
	alpha[12]  228.1       2.692     0.02707   223.0       228.2       233.4      1001   10000
	alpha[13]  242.4       2.693     0.02687   237.2       242.4       247.8      1001   10000
	alpha[14]  268.3       2.678     0.02649   263.0       268.3       273.5      1001   10000
	alpha[15]  242.8       2.651     0.02715   237.6       242.7       248.1      1001   10000
	alpha[16]  245.3       2.682     0.0246    240.1       245.3       250.6      1001   10000
	alpha[17]  232.2       2.679     0.02503   226.9       232.2       237.5      1001   10000
	alpha[18]  240.5       2.672     0.02549   235.2       240.5       245.7      1001   10000
	alpha[19]  253.8       2.686     0.02272   248.5       253.8       259.1      1001   10000
	alpha[20]  241.6       2.698     0.02646   236.3       241.6       246.9      1001   10000
	alpha[21]  248.6       2.702     0.02906   243.2       248.6       253.8      1001   10000
	alpha[22]  225.3       2.684     0.02668   220.0       225.3       230.5      1001   10000
	alpha[23]  228.5       2.678     0.02701   223.2       228.5       233.8      1001   10000
	alpha[24]  245.1       2.683     0.03076   239.9       245.1       250.4      1001   10000
	alpha[25]  234.5       2.715     0.03038   229.2       234.5       239.8      1001   10000
	alpha[26]  254.0       2.685     0.02873   248.8       254.0       259.3      1001   10000
	alpha[27]  254.4       2.643     0.02636   249.2       254.4       259.5      1001   10000
	alpha[28]  243.0       2.692     0.02858   237.7       243.0       248.3      1001   10000
	alpha[29]  217.9       2.704     0.02519   212.6       217.9       223.2      1001   10000
	alpha[30]  241.4       2.679     0.02763   236.2       241.4       246.8      1001   10000
	alpha.c    242.6       2.776     0.02659   237.1       242.6       248.2      1001   10000
	alpha0     106.6       3.655     0.04103    99.44      106.5       113.8      1001   10000
	beta[1]      6.064     0.2487    0.002582    5.578       6.066       6.555    1001   10000
	beta[2]      7.047     0.2532    0.003249    6.542       7.044       7.545    1001   10000
	beta[3]      6.483     0.2427    0.002361    6.01        6.482       6.956    1001   10000
	beta[4]      5.344     0.2545    0.003057    4.85        5.342       5.838    1001   10000
	beta[5]      6.565     0.2462    0.002938    6.085       6.564       7.048    1001   10000
	beta[6]      6.172     0.2407    0.002519    5.71        6.172       6.651    1001   10000
	beta[7]      5.978     0.2433    0.002409    5.507       5.979       6.449    1001   10000
	beta[8]      6.415     0.2471    0.002459    5.937       6.415       6.898    1001   10000
	beta[9]      7.047     0.2536    0.003015    6.543       7.053       7.534    1001   10000
	beta[10]     5.846     0.2453    0.002693    5.363       5.845       6.327    1001   10000
	beta[11]     6.798     0.2493    0.002834    6.304       6.799       7.278    1001   10000
	beta[12]     6.121     0.2395    0.002743    5.647       6.12        6.595    1001   10000
	beta[13]     6.166     0.2405    0.002348    5.686       6.169       6.635    1001   10000
	beta[14]     6.685     0.2452    0.002731    6.202       6.685       7.163    1001   10000
	beta[15]     5.416     0.2491    0.002756    4.922       5.415       5.894    1001   10000
	beta[16]     5.921     0.2434    0.002616    5.451       5.92        6.402    1001   10000
	beta[17]     6.273     0.2414    0.002537    5.801       6.273       6.755    1001   10000
	beta[18]     5.845     0.243     0.00264     5.369       5.844       6.324    1001   10000
	beta[19]     6.402     0.244     0.002438    5.929       6.402       6.882    1001   10000
	beta[20]     6.055     0.238     0.002447    5.593       6.058       6.527    1001   10000
	beta[21]     6.404     0.2415    0.002601    5.928       6.404       6.871    1001   10000
	beta[22]     5.862     0.2421    0.002504    5.379       5.863       6.335    1001   10000
	beta[23]     5.749     0.242     0.002603    5.267       5.75        6.221    1001   10000
	beta[24]     5.889     0.241     0.002509    5.413       5.889       6.364    1001   10000
	beta[25]     6.907     0.2538    0.002887    6.401       6.91        7.404    1001   10000
	beta[26]     6.546     0.2387    0.002667    6.065       6.548       7.008    1001   10000
	beta[27]     5.9       0.2421    0.002648    5.427       5.9         6.369    1001   10000
	beta[28]     5.848     0.2445    0.002618    5.359       5.849       6.325    1001   10000
	beta[29]     5.663     0.2442    0.0026      5.189       5.662       6.141    1001   10000
	beta[30]     6.127     0.2402    0.002408    5.66        6.126       6.601    1001   10000
	beta.c       6.185     0.1061    0.001313    5.975       6.185       6.395    1001   10000
	mu[1,1]    155.0       4.361     0.04912   146.5       154.9       163.6      1001   10000
	mu[1,2]    197.4       3.169     0.03588   191.2       197.4       203.7      1001   10000
	mu[1,3]    239.9       2.671     0.02855   234.7       239.8       245.2      1001   10000
	mu[1,4]    282.3       3.207     0.03157   276.1       282.3       288.6      1001   10000
	mu[1,5]    324.8       4.415     0.04279   316.3       324.8       333.4      1001   10000
	mu[2,1]    149.1       4.411     0.0494    140.4       149.1       157.8      1001   10000
	mu[2,2]    198.5       3.175     0.03204   192.2       198.4       204.7      1001   10000
	mu[2,3]    247.8       2.642     0.02543   242.6       247.8       253.0      1001   10000
	mu[2,4]    297.1       3.189     0.03607   290.8       297.2       303.2      1001   10000
	mu[2,5]    346.5       4.432     0.05468   337.6       346.5       355.1      1001   10000
	mu[3,1]    161.7       4.34      0.04219   153.1       161.6       170.3      1001   10000
	mu[3,2]    207.1       3.181     0.03043   200.8       207.1       213.3      1001   10000
	mu[3,3]    252.4       2.677     0.02487   247.2       252.4       257.6      1001   10000
	mu[3,4]    297.8       3.161     0.02928   291.7       297.8       304.1      1001   10000
	mu[3,5]    343.2       4.311     0.04053   334.7       343.2       351.7      1001   10000
	mu[4,1]    157.8       4.46      0.05048   149.0       157.8       166.4      1001   10000
	mu[4,2]    195.2       3.215     0.03448   188.7       195.2       201.5      1001   10000
	mu[4,3]    232.6       2.671     0.02729   227.3       232.6       237.8      1001   10000
	mu[4,4]    270.0       3.206     0.03489   263.7       270.0       276.4      1001   10000
	mu[4,5]    307.4       4.446     0.05104   298.7       307.4       316.3      1001   10000
	mu[5,1]    139.7       4.366     0.04579   131.2       139.7       148.3      1001   10000
	mu[5,2]    185.6       3.185     0.03017   179.4       185.6       192.0      1001   10000
	mu[5,3]    231.6       2.677     0.02386   226.3       231.6       236.9      1001   10000
	mu[5,4]    277.5       3.183     0.03278   271.2       277.6       283.7      1001   10000
	mu[5,5]    323.5       4.364     0.04925   314.9       323.5       331.9      1001   10000
	mu[6,1]    163.3       4.314     0.0439    154.8       163.3       171.6      1001   10000
	mu[6,2]    206.5       3.168     0.03304   200.3       206.5       212.7      1001   10000
	mu[6,3]    249.7       2.673     0.02963   244.4       249.8       255.0      1001   10000
	mu[6,4]    293.0       3.151     0.03586   286.7       292.9       299.1      1001   10000
	mu[6,5]    336.2       4.289     0.04813   327.6       336.1       344.8      1001   10000
	mu[7,1]    145.0       4.307     0.04728   136.6       145.0       153.5      1001   10000
	mu[7,2]    186.9       3.143     0.03511   180.8       186.9       193.0      1001   10000
	mu[7,3]    228.7       2.647     0.02826   223.6       228.7       233.9      1001   10000
	mu[7,4]    270.6       3.151     0.03055   264.4       270.6       276.7      1001   10000
	mu[7,5]    312.4       4.32      0.04046   304.0       312.4       320.9      1001   10000
	mu[8,1]    158.5       4.38      0.04657   149.9       158.5       167.3      1001   10000
	mu[8,2]    203.4       3.202     0.0345    197.2       203.4       209.8      1001   10000
	mu[8,3]    248.3       2.702     0.02836   243.1       248.4       253.6      1001   10000
	mu[8,4]    293.3       3.214     0.0318    286.9       293.2       299.6      1001   10000
	mu[8,5]    338.2       4.398     0.04256   329.5       338.1       346.9      1001   10000
	mu[9,1]    184.7       4.379     0.0473    176.1       184.6       193.3      1001   10000
	mu[9,2]    234.0       3.177     0.03243   227.8       234.0       240.1      1001   10000
	mu[9,3]    283.3       2.704     0.02751   277.9       283.3       288.6      1001   10000
	mu[9,4]    332.7       3.292     0.03677   326.0       332.7       339.0      1001   10000
	mu[9,5]    382.0       4.545     0.05328   372.6       382.1       390.8      1001   10000
	mu[10,1]   137.4       4.385     0.0486    128.8       137.4       146.1      1001   10000
	mu[10,2]   178.4       3.208     0.03413   172.1       178.4       184.6      1001   10000
	mu[10,3]   219.3       2.692     0.02606   214.1       219.3       224.6      1001   10000
	mu[10,4]   260.2       3.179     0.03006   254.0       260.2       266.6      1001   10000
	mu[10,5]   301.1       4.342     0.04289   292.7       301.1       309.8      1001   10000
	mu[11,1]   163.1       4.411     0.05046   154.6       163.0       171.8      1001   10000
	mu[11,2]   210.7       3.204     0.03569   204.5       210.6       217.1      1001   10000
	mu[11,3]   258.2       2.677     0.02809   253.1       258.2       263.5      1001   10000
	mu[11,4]   305.8       3.187     0.03303   299.6       305.8       312.0      1001   10000
	mu[11,5]   353.4       4.386     0.04669   344.8       353.4       362.0      1001   10000
	mu[12,1]   142.4       4.303     0.04779   133.9       142.5       151.0      1001   10000
	mu[12,2]   185.3       3.173     0.03376   179.2       185.3       191.6      1001   10000
	mu[12,3]   228.1       2.692     0.02707   223.0       228.2       233.4      1001   10000
	mu[12,4]   271.0       3.169     0.03261   264.8       271.0       277.3      1001   10000
	mu[12,5]   313.8       4.296     0.04617   305.4       313.8       322.2      1001   10000
	mu[13,1]   156.1       4.342     0.04222   147.6       156.1       164.6      1001   10000
	mu[13,2]   199.3       3.197     0.03134   193.1       199.3       205.5      1001   10000
	mu[13,3]   242.4       2.693     0.02687   237.2       242.4       247.8      1001   10000
	mu[13,4]   285.6       3.155     0.03166   279.4       285.6       291.8      1001   10000
	mu[13,5]   328.8       4.281     0.04269   320.4       328.8       337.0      1001   10000
	mu[14,1]   174.7       4.342     0.04496   166.2       174.7       183.2      1001   10000
	mu[14,2]   221.5       3.173     0.03156   215.3       221.5       227.7      1001   10000
	mu[14,3]   268.3       2.678     0.02649   263.0       268.3       273.5      1001   10000
	mu[14,4]   315.1       3.189     0.03375   308.7       315.1       321.3      1001   10000
	mu[14,5]   361.9       4.366     0.04803   353.2       361.9       370.3      1001   10000
	mu[15,1]   166.9       4.397     0.04997   158.5       166.9       175.5      1001   10000
	mu[15,2]   204.9       3.185     0.03528   198.7       204.9       211.1      1001   10000
	mu[15,3]   242.8       2.651     0.02715   237.6       242.7       248.1      1001   10000
	mu[15,4]   280.7       3.162     0.0312    274.6       280.6       286.9      1001   10000
	mu[15,5]   318.6       4.364     0.04421   310.0       318.5       327.2      1001   10000
	mu[16,1]   162.4       4.323     0.04542   153.9       162.5       170.8      1001   10000
	mu[16,2]   203.9       3.169     0.0316    197.7       203.9       210.1      1001   10000
	mu[16,3]   245.3       2.682     0.0246    240.1       245.3       250.6      1001   10000
	mu[16,4]   286.8       3.187     0.0297    280.5       286.7       293.1      1001   10000
	mu[16,5]   328.2       4.35      0.04277   319.7       328.2       336.8      1001   10000
	mu[17,1]   144.4       4.312     0.04152   136.0       144.4       152.8      1001   10000
	mu[17,2]   188.3       3.167     0.02932   182.1       188.3       194.6      1001   10000
	mu[17,3]   232.2       2.679     0.02503   226.9       232.2       237.5      1001   10000
	mu[17,4]   276.1       3.168     0.032     269.9       276.1       282.3      1001   10000
	mu[17,5]   320.0       4.313     0.0453    311.5       320.0       328.5      1001   10000
	mu[18,1]   158.6       4.301     0.04388   150.2       158.7       167.1      1001   10000
	mu[18,2]   199.5       3.151     0.03076   193.3       199.5       205.7      1001   10000
	mu[18,3]   240.5       2.672     0.02549   235.2       240.5       245.7      1001   10000
	mu[18,4]   281.4       3.185     0.03219   275.1       281.4       287.6      1001   10000
	mu[18,5]   322.3       4.351     0.04589   313.8       322.3       330.8      1001   10000
	mu[19,1]   164.2       4.336     0.04267   155.6       164.2       172.7      1001   10000
	mu[19,2]   209.0       3.177     0.02962   202.8       209.0       215.2      1001   10000
	mu[19,3]   253.8       2.686     0.02272   248.5       253.8       259.1      1001   10000
	mu[19,4]   298.6       3.189     0.02716   292.2       298.7       304.8      1001   10000
	mu[19,5]   343.4       4.354     0.03926   334.9       343.5       352.0      1001   10000
	mu[20,1]   156.9       4.28      0.04219   148.2       156.9       165.2      1001   10000
	mu[20,2]   199.2       3.166     0.03077   192.9       199.3       205.5      1001   10000
	mu[20,3]   241.6       2.698     0.02646   236.3       241.6       246.9      1001   10000
	mu[20,4]   284.0       3.175     0.03226   277.8       284.0       290.2      1001   10000
	mu[20,5]   326.4       4.294     0.04436   318.1       326.4       334.8      1001   10000
	mu[21,1]   158.9       4.333     0.04633   150.5       158.9       167.5      1001   10000
	mu[21,2]   203.8       3.191     0.03412   197.5       203.7       210.0      1001   10000
	mu[21,3]   248.6       2.702     0.02906   243.2       248.6       253.8      1001   10000
	mu[21,4]   293.4       3.185     0.03447   287.1       293.4       299.6      1001   10000
	mu[21,5]   338.3       4.324     0.04685   329.8       338.2       346.7      1001   10000
	mu[22,1]   143.2       4.34      0.04236   134.8       143.2       151.7      1001   10000
	mu[22,2]   184.2       3.185     0.03075   178.0       184.2       190.4      1001   10000
	mu[22,3]   225.3       2.684     0.02668   220.0       225.3       230.5      1001   10000
	mu[22,4]   266.3       3.162     0.03305   260.0       266.3       272.5      1001   10000
	mu[22,5]   307.3       4.306     0.04568   298.9       307.3       315.9      1001   10000
	mu[23,1]   148.0       4.283     0.0455    139.6       147.9       156.5      1001   10000
	mu[23,2]   188.2       3.145     0.03268   182.0       188.2       194.4      1001   10000
	mu[23,3]   228.5       2.678     0.02701   223.2       228.5       233.8      1001   10000
	mu[23,4]   268.7       3.193     0.03248   262.5       268.7       275.0      1001   10000
	mu[23,5]   309.0       4.353     0.04522   300.5       309.0       317.4      1001   10000
	mu[24,1]   162.7       4.334     0.04491   154.1       162.7       171.2      1001   10000
	mu[24,2]   203.9       3.186     0.03425   197.7       203.9       210.1      1001   10000
	mu[24,3]   245.1       2.683     0.03076   239.9       245.1       250.4      1001   10000
	mu[24,4]   286.4       3.153     0.03655   280.1       286.4       292.6      1001   10000
	mu[24,5]   327.6       4.287     0.04841   319.1       327.6       336.1      1001   10000
	mu[25,1]   137.8       4.471     0.04483   129.1       137.7       146.6      1001   10000
	mu[25,2]   186.1       3.244     0.03253   179.8       186.1       192.5      1001   10000
	mu[25,3]   234.5       2.715     0.03038   229.2       234.5       239.8      1001   10000
	mu[25,4]   282.8       3.245     0.04005   276.5       282.8       289.3      1001   10000
	mu[25,5]   331.2       4.472     0.0557    322.4       331.2       340.0      1001   10000
	mu[26,1]   162.4       4.266     0.0481    154.0       162.3       170.9      1001   10000
	mu[26,2]   208.2       3.149     0.03494   202.0       208.2       214.4      1001   10000
	mu[26,3]   254.0       2.685     0.02873   248.8       254.0       259.3      1001   10000
	mu[26,4]   299.8       3.176     0.03358   293.5       299.8       306.0      1001   10000
	mu[26,5]   345.6       4.306     0.04611   337.0       345.7       354.0      1001   10000
	mu[27,1]   171.8       4.257     0.04695   163.4       171.8       180.0      1001   10000
	mu[27,2]   213.1       3.111     0.03325   206.9       213.1       219.2      1001   10000
	mu[27,3]   254.4       2.643     0.02636   249.2       254.4       259.5      1001   10000
	mu[27,4]   295.7       3.168     0.03117   289.5       295.7       301.8      1001   10000
	mu[27,5]   337.0       4.339     0.04399   328.4       337.0       345.2      1001   10000
	mu[28,1]   161.1       4.349     0.0479    152.6       161.1       169.8      1001   10000
	mu[28,2]   202.0       3.186     0.03492   195.7       202.0       208.4      1001   10000
	mu[28,3]   243.0       2.692     0.02858   237.7       243.0       248.3      1001   10000
	mu[28,4]   283.9       3.195     0.03295   277.6       283.9       290.2      1001   10000
	mu[28,5]   324.9       4.362     0.04502   316.3       324.8       333.4      1001   10000
	mu[29,1]   138.6       4.347     0.04565   130.1       138.6       147.3      1001   10000
	mu[29,2]   178.3       3.191     0.03206   172.0       178.2       184.6      1001   10000
	mu[29,3]   217.9       2.704     0.02519   212.6       217.9       223.2      1001   10000
	mu[29,4]   257.5       3.207     0.03007   251.3       257.5       263.9      1001   10000
	mu[29,5]   297.2       4.371     0.04285   288.6       297.1       305.7      1001   10000
	mu[30,1]   155.7       4.35      0.04148   147.0       155.7       164.1      1001   10000
	mu[30,2]   198.6       3.197     0.03095   192.3       198.6       204.8      1001   10000
	mu[30,3]   241.4       2.679     0.02763   236.2       241.4       246.8      1001   10000
	mu[30,4]   284.3       3.127     0.03373   278.2       284.3       290.5      1001   10000
	mu[30,5]   327.2       4.247     0.04561   318.9       327.2       335.5      1001   10000
	sigma        6.086     0.4606    0.007422    5.255       6.061       7.049    1001   10000
	tau.alpha    0.004932  0.001367  1.492E-5    0.002688    0.004801    0.008    1001   10000
	tau.beta     4.155     1.55      0.02774     1.95        3.888       7.911    1001   10000
	tau.c        0.02746   0.004124  6.667E-5    0.02013     0.02723     0.03625  1001   10000
Node statistics
	node         mean        sd        MC error  2.5%        median      97.5%       start  sample
	alpha[1]     239.9       2.709     0.02984   234.5       239.9       245.2       1001   10000
	alpha[2]     247.8       2.702     0.02425   242.6       247.8       253.1       1001   10000
	alpha[3]     252.4       2.659     0.0213    247.3       252.5       257.7       1001   10000
	alpha[4]     232.5       2.679     0.02458   227.3       232.6       237.7       1001   10000
	alpha[5]     231.7       2.663     0.02514   226.4       231.7       236.9       1001   10000
	alpha[6]     249.8       2.65      0.02415   244.6       249.8       255.0       1001   10000
	alpha[7]     228.7       2.646     0.02366   223.5       228.7       233.9       1001   10000
	alpha[8]     248.4       2.704     0.02569   243.1       248.4       253.6       1001   10000
	alpha[9]     283.4       2.706     0.02924   278.1       283.4       288.7       1001   10000
	alpha[10]    219.2       2.681     0.02928   214.0       219.2       224.5       1001   10000
	alpha[11]    258.3       2.673     0.02572   252.9       258.3       263.4       1001   10000
	alpha[12]    228.1       2.686     0.02537   222.8       228.1       233.5       1001   10000
	alpha[13]    242.4       2.667     0.0287    237.1       242.4       247.5       1001   10000
	alpha[14]    268.3       2.707     0.02451   262.9       268.3       273.6       1001   10000
	alpha[15]    242.8       2.681     0.02934   237.5       242.8       248.1       1001   10000
	alpha[16]    245.4       2.682     0.02593   240.0       245.4       250.5       1001   10000
	alpha[17]    232.2       2.668     0.03022   227.0       232.2       237.4       1001   10000
	alpha[18]    240.5       2.683     0.02907   235.2       240.5       245.8       1001   10000
	alpha[19]    253.8       2.667     0.02977   248.6       253.7       259.1       1001   10000
	alpha[20]    241.6       2.702     0.02906   236.3       241.6       246.9       1001   10000
	alpha[21]    248.6       2.663     0.02487   243.2       248.6       253.7       1001   10000
	alpha[22]    225.3       2.689     0.02645   220.0       225.2       230.6       1001   10000
	alpha[23]    228.5       2.654     0.02464   223.3       228.5       233.7       1001   10000
	alpha[24]    245.1       2.668     0.02811   239.9       245.1       250.4       1001   10000
	alpha[25]    234.5       2.67      0.02429   229.2       234.5       239.7       1001   10000
	alpha[26]    254.0       2.726     0.0283    248.6       254.0       259.2       1001   10000
	alpha[27]    254.4       2.684     0.02426   249.1       254.4       259.6       1001   10000
	alpha[28]    243.0       2.659     0.02653   237.8       243.0       248.2       1001   10000
	alpha[29]    217.9       2.699     0.02643   212.6       217.9       223.2       1001   10000
	alpha[30]    241.4       2.683     0.02469   236.1       241.4       246.6       1001   10000
	alpha.c      242.6       2.78      0.02816   237.3       242.6       248.2       1001   10000
	alpha0       106.6       3.65      0.04151    99.43      106.5       113.9       1001   10000
	beta[1]        6.06      0.244     0.002281    5.587       6.056       6.54      1001   10000
	beta[2]        7.068     0.2513    0.002792    6.564       7.067       7.552     1001   10000
	beta[3]        6.485     0.242     0.002477    6.013       6.484       6.97      1001   10000
	beta[4]        5.333     0.2554    0.002823    4.832       5.333       5.837     1001   10000
	beta[5]        6.576     0.244     0.002394    6.102       6.574       7.059     1001   10000
	beta[6]        6.174     0.2439    0.002296    5.695       6.174       6.658     1001   10000
	beta[7]        5.98      0.2432    0.002558    5.503       5.98        6.456     1001   10000
	beta[8]        6.42      0.245     0.002323    5.942       6.42        6.896     1001   10000
	beta[9]        7.064     0.255     0.003149    6.562       7.064       7.567     1001   10000
	beta[10]       5.846     0.2425    0.002341    5.363       5.847       6.319     1001   10000
	beta[11]       6.806     0.2464    0.002708    6.32        6.808       7.282     1001   10000
	beta[12]       6.118     0.2409    0.002511    5.65        6.115       6.601     1001   10000
	beta[13]       6.159     0.2423    0.002466    5.673       6.158       6.628     1001   10000
	beta[14]       6.696     0.2462    0.002467    6.211       6.694       7.186     1001   10000
	beta[15]       5.407     0.2558    0.002956    4.917       5.406       5.914     1001   10000
	beta[16]       5.919     0.2442    0.002639    5.436       5.919       6.397     1001   10000
	beta[17]       6.274     0.2456    0.002604    5.785       6.278       6.755     1001   10000
	beta[18]       5.841     0.2439    0.002864    5.364       5.843       6.316     1001   10000
	beta[19]       6.407     0.2402    0.002522    5.94        6.404       6.887     1001   10000
	beta[20]       6.051     0.2429    0.002321    5.582       6.052       6.52      1001   10000
	beta[21]       6.41      0.2393    0.002444    5.945       6.412       6.881     1001   10000
	beta[22]       5.85      0.2474    0.002406    5.366       5.852       6.332     1001   10000
	beta[23]       5.736     0.2489    0.002605    5.245       5.738       6.223     1001   10000
	beta[24]       5.885     0.2411    0.002232    5.403       5.886       6.351     1001   10000
	beta[25]       6.918     0.2543    0.003034    6.413       6.918       7.414     1001   10000
	beta[26]       6.554     0.2443    0.002606    6.078       6.552       7.033     1001   10000
	beta[27]       5.893     0.2436    0.002648    5.418       5.889       6.378     1001   10000
	beta[28]       5.842     0.2425    0.00244     5.364       5.844       6.32      1001   10000
	beta[29]       5.664     0.2487    0.002759    5.178       5.664       6.147     1001   10000
	beta[30]       6.132     0.243     0.00257     5.65        6.131       6.608     1001   10000
	beta.c         6.185     0.1102    0.001294    5.967       6.185       6.404     1001   10000
	mu[1,1]      155.1       4.341     0.04037   146.5       155.1       163.5       1001   10000
	mu[1,2]      197.5       3.19      0.0317    191.2       197.5       203.8       1001   10000
	mu[1,3]      239.9       2.709     0.02984   234.5       239.9       245.2       1001   10000
	mu[1,4]      282.3       3.214     0.03585   276.0       282.3       288.6       1001   10000
	mu[1,5]      324.7       4.377     0.0468    316.3       324.7       333.3       1001   10000
	mu[2,1]      148.9       4.462     0.0433    140.1       148.8       157.7       1001   10000
	mu[2,2]      198.3       3.242     0.02915   192.1       198.3       204.7       1001   10000
	mu[2,3]      247.8       2.702     0.02425   242.6       247.8       253.1       1001   10000
	mu[2,4]      297.3       3.207     0.03302   290.9       297.3       303.5       1001   10000
	mu[2,5]      346.8       4.411     0.04854   338.0       346.8       355.4       1001   10000
	mu[3,1]      161.6       4.259     0.04094   153.3       161.6       169.9       1001   10000
	mu[3,2]      207.0       3.12      0.02765   200.9       207.0       213.2       1001   10000
	mu[3,3]      252.4       2.659     0.0213    247.3       252.5       257.7       1001   10000
	mu[3,4]      297.8       3.186     0.02729   291.6       297.8       304.0       1001   10000
	mu[3,5]      343.2       4.355     0.04046   334.7       343.3       351.8       1001   10000
	mu[4,1]      157.9       4.461     0.0459    149.2       157.9       166.7       1001   10000
	mu[4,2]      195.2       3.216     0.03106   188.8       195.3       201.5       1001   10000
	mu[4,3]      232.5       2.679     0.02458   227.3       232.6       237.7       1001   10000
	mu[4,4]      269.9       3.226     0.03201   263.6       269.9       276.2       1001   10000
	mu[4,5]      307.2       4.476     0.04718   298.6       307.2       316.1       1001   10000
	mu[5,1]      139.6       4.332     0.04097   131.2       139.6       148.4       1001   10000
	mu[5,2]      185.6       3.164     0.02957   179.5       185.6       192.0       1001   10000
	mu[5,3]      231.7       2.663     0.02514   226.4       231.7       236.9       1001   10000
	mu[5,4]      277.7       3.163     0.03085   271.5       277.7       283.9       1001   10000
	mu[5,5]      323.7       4.33      0.04281   315.3       323.7       332.4       1001   10000
	mu[6,1]      163.4       4.331     0.03925   154.9       163.4       171.7       1001   10000
	mu[6,2]      206.6       3.158     0.02835   200.4       206.6       212.7       1001   10000
	mu[6,3]      249.8       2.65      0.02415   244.6       249.8       255.0       1001   10000
	mu[6,4]      293.0       3.147     0.02965   286.8       293.0       299.2       1001   10000
	mu[6,5]      336.2       4.314     0.04112   327.8       336.2       344.7       1001   10000
	mu[7,1]      145.0       4.319     0.04387   136.6       145.0       153.6       1001   10000
	mu[7,2]      186.8       3.151     0.03035   180.8       186.8       193.0       1001   10000
	mu[7,3]      228.7       2.646     0.02366   223.5       228.7       233.9       1001   10000
	mu[7,4]      270.5       3.142     0.02897   264.4       270.5       276.7       1001   10000
	mu[7,5]      312.4       4.306     0.04196   304.0       312.4       320.8       1001   10000
	mu[8,1]      158.5       4.365     0.04066   149.9       158.5       167.0       1001   10000
	mu[8,2]      203.5       3.2       0.02987   197.2       203.5       209.8       1001   10000
	mu[8,3]      248.4       2.704     0.02569   243.1       248.4       253.6       1001   10000
	mu[8,4]      293.3       3.205     0.03093   287.0       293.4       299.5       1001   10000
	mu[8,5]      338.3       4.372     0.04221   329.7       338.3       346.7       1001   10000
	mu[9,1]      184.5       4.437     0.05666   175.8       184.4       193.1       1001   10000
	mu[9,2]      233.9       3.212     0.03933   227.7       233.9       240.2       1001   10000
	mu[9,3]      283.4       2.706     0.02924   278.1       283.4       288.7       1001   10000
	mu[9,4]      332.8       3.27      0.03369   326.3       332.8       339.2       1001   10000
	mu[9,5]      382.3       4.521     0.04885   373.2       382.3       391.2       1001   10000
	mu[10,1]     137.4       4.297     0.04316   128.9       137.4       145.9       1001   10000
	mu[10,2]     178.3       3.153     0.03304   172.2       178.3       184.5       1001   10000
	mu[10,3]     219.2       2.681     0.02928   214.0       219.2       224.5       1001   10000
	mu[10,4]     260.1       3.193     0.03406   253.9       260.1       266.4       1001   10000
	mu[10,5]     301.0       4.355     0.04472   292.6       301.0       309.7       1001   10000
	mu[11,1]     163.0       4.348     0.04726   154.5       163.0       171.5       1001   10000
	mu[11,2]     210.6       3.17      0.03299   204.4       210.6       216.8       1001   10000
	mu[11,3]     258.3       2.673     0.02572   252.9       258.3       263.4       1001   10000
	mu[11,4]     305.9       3.192     0.03088   299.4       305.9       312.0       1001   10000
	mu[11,5]     353.5       4.38      0.04432   344.7       353.6       362.0       1001   10000
	mu[12,1]     142.4       4.323     0.04469   133.8       142.5       150.9       1001   10000
	mu[12,2]     185.2       3.179     0.03181   179.0       185.3       191.5       1001   10000
	mu[12,3]     228.1       2.686     0.02537   222.8       228.1       233.5       1001   10000
	mu[12,4]     270.9       3.163     0.0299    264.8       270.9       277.2       1001   10000
	mu[12,5]     313.7       4.3       0.04198   305.4       313.7       322.1       1001   10000
	mu[13,1]     156.2       4.322     0.04484   147.6       156.2       164.5       1001   10000
	mu[13,2]     199.3       3.166     0.03345   193.0       199.3       205.4       1001   10000
	mu[13,3]     242.4       2.667     0.0287    237.1       242.4       247.5       1001   10000
	mu[13,4]     285.5       3.155     0.03354   279.2       285.5       291.6       1001   10000
	mu[13,5]     328.6       4.307     0.04496   320.1       328.6       336.9       1001   10000
	mu[14,1]     174.6       4.371     0.04435   166.1       174.5       183.2       1001   10000
	mu[14,2]     221.4       3.201     0.03139   215.2       221.4       227.8       1001   10000
	mu[14,3]     268.3       2.707     0.02451   262.9       268.3       273.6       1001   10000
	mu[14,4]     315.2       3.217     0.0285    308.9       315.2       321.4       1001   10000
	mu[14,5]     362.0       4.395     0.04026   353.6       362.0       370.7       1001   10000
	mu[15,1]     167.1       4.431     0.04947   158.2       167.1       175.7       1001   10000
	mu[15,2]     204.9       3.194     0.03501   198.6       205.0       211.2       1001   10000
	mu[15,3]     242.8       2.681     0.02934   237.5       242.8       248.1       1001   10000
	mu[15,4]     280.6       3.253     0.03677   274.4       280.6       287.0       1001   10000
	mu[15,5]     318.5       4.515     0.05196   309.7       318.5       327.5       1001   10000
	mu[16,1]     162.5       4.337     0.0402    154.0       162.5       171.0       1001   10000
	mu[16,2]     203.9       3.175     0.02834   197.8       203.9       210.1       1001   10000
	mu[16,3]     245.4       2.682     0.02593   240.0       245.4       250.5       1001   10000
	mu[16,4]     286.8       3.185     0.03499   280.5       286.8       292.9       1001   10000
	mu[16,5]     328.2       4.352     0.04959   319.7       328.3       336.6       1001   10000
	mu[17,1]     144.3       4.385     0.04819   135.7       144.3       152.9       1001   10000
	mu[17,2]     188.3       3.196     0.03586   182.0       188.3       194.5       1001   10000
	mu[17,3]     232.2       2.668     0.03022   227.0       232.2       237.4       1001   10000
	mu[17,4]     276.1       3.152     0.03473   269.9       276.1       282.3       1001   10000
	mu[17,5]     320.0       4.32      0.0465    311.5       320.0       328.4       1001   10000
	mu[18,1]     158.7       4.3       0.04975   150.1       158.8       167.1       1001   10000
	mu[18,2]     199.6       3.151     0.03547   193.4       199.7       205.7       1001   10000
	mu[18,3]     240.5       2.683     0.02907   235.2       240.5       245.8       1001   10000
	mu[18,4]     281.4       3.208     0.03515   275.2       281.3       287.7       1001   10000
	mu[18,5]     322.2       4.384     0.0493    313.6       322.2       330.9       1001   10000
	mu[19,1]     164.1       4.281     0.04736   155.6       164.1       172.3       1001   10000
	mu[19,2]     208.9       3.145     0.0354    202.7       208.9       215.1       1001   10000
	mu[19,3]     253.8       2.667     0.02977   248.6       253.7       259.1       1001   10000
	mu[19,4]     298.6       3.16      0.03381   292.4       298.6       304.8       1001   10000
	mu[19,5]     343.5       4.303     0.04498   335.1       343.5       351.9       1001   10000
	mu[20,1]     156.9       4.369     0.0468    148.4       156.9       165.3       1001   10000
	mu[20,2]     199.2       3.209     0.0354    193.0       199.3       205.5       1001   10000
	mu[20,3]     241.6       2.702     0.02906   236.3       241.6       246.9       1001   10000
	mu[20,4]     284.0       3.175     0.03104   277.7       283.9       290.2       1001   10000
	mu[20,5]     326.3       4.318     0.04014   317.8       326.3       334.7       1001   10000
	mu[21,1]     158.8       4.298     0.0374    150.6       158.8       167.4       1001   10000
	mu[21,2]     203.7       3.158     0.02675   197.5       203.7       209.9       1001   10000
	mu[21,3]     248.6       2.663     0.02487   243.2       248.6       253.7       1001   10000
	mu[21,4]     293.5       3.134     0.03327   287.3       293.5       299.6       1001   10000
	mu[21,5]     338.3       4.263     0.0467    329.9       338.3       346.6       1001   10000
	mu[22,1]     143.4       4.37      0.04641   134.7       143.4       151.9       1001   10000
	mu[22,2]     184.3       3.188     0.03381   178.1       184.3       190.6       1001   10000
	mu[22,3]     225.3       2.689     0.02645   220.0       225.2       230.6       1001   10000
	mu[22,4]     266.2       3.208     0.0287    259.9       266.2       272.6       1001   10000
	mu[22,5]     307.2       4.4       0.03892   298.4       307.2       315.9       1001   10000
	mu[23,1]     148.2       4.344     0.04298   139.6       148.1       156.8       1001   10000
	mu[23,2]     188.3       3.15      0.02991   182.0       188.3       194.5       1001   10000
	mu[23,3]     228.5       2.654     0.02464   223.3       228.5       233.7       1001   10000
	mu[23,4]     268.6       3.199     0.03137   262.4       268.6       274.9       1001   10000
	mu[23,5]     308.8       4.416     0.04502   300.1       308.8       317.5       1001   10000
	mu[24,1]     162.7       4.261     0.03874   154.3       162.7       171.1       1001   10000
	mu[24,2]     203.9       3.129     0.03002   197.7       203.9       210.0       1001   10000
	mu[24,3]     245.1       2.668     0.02811   239.9       245.1       250.4       1001   10000
	mu[24,4]     286.3       3.185     0.03416   280.0       286.3       292.6       1001   10000
	mu[24,5]     327.5       4.344     0.04508   318.9       327.5       335.9       1001   10000
	mu[25,1]     137.6       4.47      0.04839   128.8       137.6       146.6       1001   10000
	mu[25,2]     186.0       3.223     0.03186   179.7       186.0       192.5       1001   10000
	mu[25,3]     234.5       2.67      0.02429   229.2       234.5       239.7       1001   10000
	mu[25,4]     282.9       3.195     0.03267   276.6       282.9       289.1       1001   10000
	mu[25,5]     331.3       4.43      0.04946   322.7       331.3       340.0       1001   10000
	mu[26,1]     162.2       4.358     0.04652   153.7       162.2       170.7       1001   10000
	mu[26,2]     208.1       3.207     0.03391   201.8       208.1       214.4       1001   10000
	mu[26,3]     254.0       2.726     0.0283    248.6       254.0       259.2       1001   10000
	mu[26,4]     299.9       3.228     0.03344   293.4       299.9       306.1       1001   10000
	mu[26,5]     345.7       4.389     0.04584   336.9       345.8       354.2       1001   10000
	mu[27,1]     171.9       4.368     0.04558   163.2       172.0       180.5       1001   10000
	mu[27,2]     213.2       3.199     0.03145   206.9       213.2       219.4       1001   10000
	mu[27,3]     254.4       2.684     0.02426   249.1       254.4       259.6       1001   10000
	mu[27,4]     295.7       3.16      0.02958   289.5       295.7       301.9       1001   10000
	mu[27,5]     336.9       4.31      0.04299   328.5       336.9       345.4       1001   10000
	mu[28,1]     161.2       4.292     0.04433   152.7       161.3       169.5       1001   10000
	mu[28,2]     202.1       3.141     0.03229   195.9       202.2       208.3       1001   10000
	mu[28,3]     243.0       2.659     0.02653   237.8       243.0       248.2       1001   10000
	mu[28,4]     283.9       3.168     0.0308    277.7       283.9       290.2       1001   10000
	mu[28,5]     324.8       4.332     0.04216   316.5       324.8       333.2       1001   10000
	mu[29,1]     138.6       4.397     0.04733   129.9       138.5       147.1       1001   10000
	mu[29,2]     178.2       3.206     0.03311   171.9       178.2       184.5       1001   10000
	mu[29,3]     217.9       2.699     0.02643   212.6       217.9       223.2       1001   10000
	mu[29,4]     257.5       3.217     0.03236   251.3       257.5       263.8       1001   10000
	mu[29,5]     297.2       4.413     0.04628   288.5       297.1       305.8       1001   10000
	mu[30,1]     155.6       4.376     0.04098   146.9       155.6       164.2       1001   10000
	mu[30,2]     198.5       3.206     0.02865   192.2       198.5       204.8       1001   10000
	mu[30,3]     241.4       2.683     0.02469   236.1       241.4       246.6       1001   10000
	mu[30,4]     284.3       3.147     0.03233   278.1       284.4       290.4       1001   10000
	mu[30,5]     327.3       4.288     0.04613   318.8       327.3       335.6       1001   10000
	sigma          6.074     0.4673    0.007724    5.247       6.044       7.068     1001   10000
	sigma.alpha   14.88      2.135     0.02675    11.28       14.64       19.72      1001   10000
	sigma.beta     0.5317    0.09454   0.001509    0.3679      0.5237      0.7416    1001   10000
	tau.alpha      0.004791  0.001334  1.517E-5    0.002573    0.004666    0.007854  1001   10000
	tau.beta       3.884     1.423     0.02366     1.82        3.646       7.389     1001   10000
	tau.c          0.02758   0.004191  6.869E-5    0.02002     0.02737     0.03633   1001   10000
Comparing the above two sets of results node by node is inconvenient, because one must constantly switch which file one is viewing, and each time search for the same node within two different files. Summarizing all nodes from both runs together within an Html table provides one solution to this problem.

Here are the steps in running WinBUGSLogs2HtmlSummary: First, from Windows Explorer, drag-and-drop the two (can be more than two, but in this example we have only two) WinBUGS text log files onto WinBUGSLogs2HtmlSummary's Desktop Icon. A form then pops up (right).

Enter a relevant title in the title box.

Since we want to compare Node Statistics for almost all nodes listed, we will first select all of them by clicking the tick box next to Select all

For illustrative purposes, we will unselect the nodes alpha, beta and mu (you might of course be interested in these comparisons also) by unticking the appropriate boxes.



For more concise outputs, one may prefer to exclude MC Error, Start and Sample Node Statistics from the summary by by unticking the box below the filename window.
When the Start and Sample Node Statistics are the same for each node in every log file, their values are automatically relegated to the narrower bottom section of the output summary, along with WinBUGS log file names and paths.

The upper corner radio button allows you to chose Legal or Letter format, which can be useful even if you do not intend to print the summary. Indeed, this option allows you to select the maximal width of the output summary: Legal will fit on a 8 in x 14 in sheet (landscape) while Letter will fit (landscape) on a 8 in x 11 in sheet.

Finally, the upper-left corner tick box allows you to add links to models; in doing so, the WinBUGS log file names, when clicked, will open the file with model used in the corresponding script. This, however, comes with a warning: indeed, the link is to the model file given on the check() WinBUGS script command, but this file may have changed since the log file was produced (if you consult the summary days or weeks later), thus it may not correspond to the actual model used to produce the results presented in the summary.


Click Ok button to submit.




In the example above, the summary will be saved to file WinBUGS-summary.html, which will resemble (click image to see actual summary file):






Top
Download WinBUGSLogs2HtmlSummary

WinBUGSLogs2HtmlSummary is a free program. Save and unzip version 1.8 under the name WinBUGSLogs2HtmlSummary.pl and read the section below.

WinBUGSLogs2HtmlSummary also needs Tkx Perl module be installed. Read Perl package manager instructions on how to install Tkx in a few clicks.

Top
How to use WinBUGSLogs2HtmlSummary

WinBUGSLogs2HtmlSummary is a program written in Perl. Please refer to my generic page on running Perl programs for instructions.


Top
Details of example above

The example used in this document is the Rats example, originally presented in WinBUGS examples manual, version 1.4.1. We reproduce below the two models used, the data set and the two sets of initial values used to obtain the results presented above.

Model 1 (uniform distribution on SD)

# UniformSD

model
{
  for( i in 1 : N )
  {
    for( j in 1 : T ) 
    {
      Y[i , j] ~ dnorm(mu[i , j],tau.c)
      
      mu[i , j] <- alpha[i] + beta[i] * (x[j] - xbar)
    }
			
    alpha[i] ~ dnorm(alpha.c, tau.alpha)
    beta[i]  ~ dnorm(beta.c,  tau.beta)
  }
		
  tau.c ~ dgamma(0.001,0.001)
  sigma <- 1 / sqrt(tau.c)
  alpha.c ~ dnorm(0.0,1.0E-6)	

  beta.c ~ dnorm(0.0,1.0E-6)	
  alpha0 <- alpha.c - xbar * beta.c 
		
  # Choice of prior of random effects variances  
  # Prior: uniform on SD

  sigma.alpha ~ dunif(0,100)
  sigma.beta  ~ dunif(0,100)

  tau.alpha <- 1/(sigma.alpha*sigma.alpha)
  tau.beta  <- 1/(sigma.beta*sigma.beta)
}

Model 2 (dgamma on precision [not recommanded])

# dGammaPrecision

model
{
  for( i in 1 : N )
  {
    for( j in 1 : T ) 
    {
      Y[i , j] ~ dnorm(mu[i , j],tau.c)

      mu[i , j] <- alpha[i] + beta[i] * (x[j] - xbar)
    }

    alpha[i] ~ dnorm(alpha.c, tau.alpha)
    beta[i]  ~ dnorm(beta.c,  tau.beta)
  }

  tau.c ~ dgamma(0.001,0.001)
  sigma <- 1 / sqrt(tau.c)
  alpha.c ~ dnorm(0.0,1.0E-6)
	

  beta.c ~ dnorm(0.0,1.0E-6)

  alpha0 <- alpha.c - xbar * beta.c

  # Prior: dgamma on precision (not recommended)

  tau.alpha ~ dgamma(0.001,0.001)
  tau.beta  ~ dgamma(0.001,0.001)
}

The data to analyze are those presented in the following WinBUGS list

list(x = c(8.0, 15.0, 22.0, 29.0, 36.0), xbar = 22, N = 30, T = 5,	
		Y = structure(
			.Data =   c(151, 199, 246, 283, 320,
				    145, 199, 249, 293, 354,
				    147, 214, 263, 312, 328,
				    155, 200, 237, 272, 297,
				    135, 188, 230, 280, 323,
				    159, 210, 252, 298, 331,
				    141, 189, 231, 275, 305,
				    159, 201, 248, 297, 338,
				    177, 236, 285, 350, 376,
				    134, 182, 220, 260, 296,
				    160, 208, 261, 313, 352,
				    143, 188, 220, 273, 314,
				    154, 200, 244, 289, 325,
				    171, 221, 270, 326, 358,
				    163, 216, 242, 281, 312,
				    160, 207, 248, 288, 324,
				    142, 187, 234, 280, 316,
				    156, 203, 243, 283, 317,
				    157, 212, 259, 307, 336,
				    152, 203, 246, 286, 321,
				    154, 205, 253, 298, 334,
				    139, 190, 225, 267, 302,
				    146, 191, 229, 272, 302,
				    157, 211, 250, 285, 323,
				    132, 185, 237, 286, 331,
                                    160, 207, 257, 303, 345,		
				    169, 216, 261, 295, 333,
				    157, 205, 248, 289, 316,
				    137, 180, 219, 258, 291,
				    153, 200, 244, 286, 324),
			.Dim = c(30,5)))


while the set of initial values used for Model 1 was

list(alpha = c(250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 
	       250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250),
     beta  = c(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
	       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6),			
     alpha.c = 150, beta.c = 10, 
     tau.c = 1, sigma.alpha = 1, sigma.beta = 1)


and the set of initial values used for Model 2 was

list(alpha = c(250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 
	       250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250),
     beta  = c(6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
               6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6),			
     alpha.c = 150, beta.c = 10, 
     tau.c = 1, tau.alpha = 1, tau.beta = 1)