------------------------------------------------------------------------------- name: log: Q:\C-modelling\runmlwin\website\logfiles\2020-03-27\16\23_Using_Or > thogonal_fixed_effect_vectors.smcl log type: smcl opened on: 27 Mar 2020, 18:12:45 . **************************************************************************** . * MLwiN MCMC Manual . * . * 23 Using Orthogonal fixed effect vectors . . . . . . . . . . . . . . .357 . * . * Browne, W. J. (2009). MCMC Estimation in MLwiN, v2.26. Centre for . * Multilevel Modelling, University of Bristol. . **************************************************************************** . * Stata do-file to replicate all analyses using runmlwin . * . * George Leckie and Chris Charlton, . * Centre for Multilevel Modelling, 2012 . * http://www.bristol.ac.uk/cmm/software/runmlwin/ . **************************************************************************** . . * 23.1 A simple example . . . . . . . . . . . . . . . . . . . . . . . . .358 . . * 23.2 Constructing orthogonal vectors . . . . . . . . . . . . . . . . . 359 . . * 23.3 A Binomial response example . . . . . . . . . . . . . . . . . . . 360 . . use "http://www.bristol.ac.uk/cmm/media/runmlwin/bang1.dta", clear . . gen onekid = (lc==1) . . gen twokids = (lc==2) . . gen threepluskids = (lc==3) . . quietly runmlwin use cons age onekid twokids threepluskids urban, /// > level2(district: cons urban) /// > level1(woman:) /// > discrete(distribution(binomial) link(logit) denom(denomb)) nopause . . matrix b = e(b) . . matrix V = e(V) . . runmlwin use cons age onekid twokids threepluskids urban, /// > level2(district: cons urban) /// > level1(woman:) /// > discrete(distribution(binomial) link(logit) denom(denomb)) /// > mcmc(on) initsb(b) initsv(V) nopause MLwiN 3.05 multilevel model Number of obs = 1934 Binomial logit response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ district | 60 2 32.2 118 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 8.29 Deviance (dbar) = 2327.27 Deviance (thetabar) = 2269.60 Effective no. of pars (pd) = 57.67 Bayesian DIC = 2384.94 ------------------------------------------------------------------------------ use | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | -1.723641 .1645528 55 0.000 -2.081246 -1.398094 age | -.0259363 .0077362 262 0.001 -.0413501 -.0110153 onekid | 1.12629 .1525135 233 0.000 .8373096 1.439499 twokids | 1.346535 .1685376 213 0.000 1.013076 1.672413 threeplusk~s | 1.343007 .170597 117 0.000 1.009437 1.659421 urban | .8393166 .1987613 77 0.000 .4609401 1.254963 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: district | var(cons) | .4368685 .1454697 165 .2212415 .7852281 cov(cons,urban) | -.4615861 .1921197 129 -.9212328 -.1584836 var(urban) | .7889688 .3325157 116 .2958012 1.587222 ------------------------------------------------------------------------------ . . mcmcsum, trajectories . . runmlwin use cons age onekid twokids threepluskids urban, /// > level2(district: cons urban) /// > level1(woman:) /// > discrete(distribution(binomial) link(logit) denom(denomb)) /// > mcmc(orth) initsb(b) initsv(V) nopause MLwiN 3.05 multilevel model Number of obs = 1934 Binomial logit response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ district | 60 2 32.2 118 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 7.99 Deviance (dbar) = 2329.93 Deviance (thetabar) = 2274.25 Effective no. of pars (pd) = 55.69 Bayesian DIC = 2385.62 ------------------------------------------------------------------------------ use | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | -1.725307 .1596834 242 0.000 -2.046332 -1.416695 age | -.0268184 .0079077 1069 0.000 -.0420135 -.0112826 onekid | 1.137847 .1608216 1152 0.000 .8388709 1.45232 twokids | 1.362508 .1793792 986 0.000 1.014565 1.705076 threeplusk~s | 1.362581 .1845054 1015 0.000 1.006143 1.724028 urban | .8198761 .1775268 151 0.000 .4960341 1.182494 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: district | var(cons) | .4147928 .1309379 173 .211906 .7276892 cov(cons,urban) | -.4101084 .1750757 81 -.8206579 -.148975 var(urban) | .6757473 .319791 57 .2427979 1.467602 ------------------------------------------------------------------------------ . . . . * 23.4 A Poisson example . . . . . . . . . . . . . . . . . . . . . . . . 364 . . use "http://www.bristol.ac.uk/cmm/media/runmlwin/mmmec1.dta", clear . . generate logexp = ln(exp) . . tabulate nation nation | Freq. Percent Cum. ------------+----------------------------------- 1 | 11 3.11 3.11 2 | 30 8.47 11.58 3 | 14 3.95 15.54 4 | 94 26.55 42.09 5 | 70 19.77 61.86 6 | 95 26.84 88.70 7 | 26 7.34 96.05 8 | 3 0.85 96.89 9 | 11 3.11 100.00 ------------+----------------------------------- Total | 354 100.00 . . generate belgium = (nation==1) . . generate wgermany = (nation==2) . . generate denmark = (nation==3) . . generate france = (nation==4) . . generate uk = (nation==5) . . generate italy = (nation==6) . . generate ireland = (nation==7) . . generate luxembourg = (nation==8) . . generate netherlands = (nation==9) . . foreach var of varlist belgium-netherlands { 2. . generate `var'Xuvbi = `var'*uvbi 3. . } . . quietly runmlwin obs belgium-netherlands belgiumXuvbi-netherlandsXuvbi, /// > level2(region: cons) /// > level1(county:) /// > discrete(distribution(poisson) offset(logexp)) nopause . . matrix b = e(b) . . matrix V = e(V) . . runmlwin obs belgium-netherlands belgiumXuvbi-netherlandsXuvbi, /// > level2(region: cons) /// > level1(county:) /// > discrete(distribution(poisson) offset(logexp)) /// > mcmc(chain(50000) seed(1)) initsb(b) initsv(V) nopause MLwiN 3.05 multilevel model Number of obs = 354 Poisson response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ region | 78 1 4.5 13 ----------------------------------------------------------- Burnin = 500 Chain = 50000 Thinning = 1 Run time (seconds) = 23.9 Deviance (dbar) = 2026.97 Deviance (thetabar) = 1965.01 Effective no. of pars (pd) = 61.95 Bayesian DIC = 2088.92 ------------------------------------------------------------------------------ obs | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- belgium | .8578639 .6401522 41 0.061 -.3351097 2.160622 wgermany | .4751676 .1148532 194 0.000 .2525713 .7008874 denmark | .4522425 .7633578 30 0.301 -.896336 1.867101 france | -.59289 .0553293 1477 0.000 -.7027236 -.4848803 uk | .5990007 .1803375 93 0.001 .2192649 .9270838 italy | .2812826 .1115665 379 0.002 .0766469 .5109113 ireland | -.531236 1.195825 67 0.342 -2.825914 1.756896 luxembourg | 6.085308 8.872184 26 0.265 -11.51869 20.57583 netherlands | -.5110252 1.008254 30 0.266 -2.617993 1.671373 belgiumXuvbi | .3166227 .2171381 41 0.049 -.0852628 .7625054 wgermanyXu~i | -.0140824 .0314208 219 0.326 -.0758529 .0479817 denmarkXuvbi | -.0580177 .1347134 30 0.363 -.2963691 .1941858 franceXuvbi | .0127516 .0178822 1753 0.235 -.0221759 .0486347 ukXuvbi | .1384196 .0372984 94 0.000 .0597328 .2060151 italyXuvbi | -.0870577 .0169275 413 0.000 -.1226269 -.0550112 irelandXuvbi | .000161 .2403333 67 0.489 -.4541734 .4650083 luxembourg~i | 2.650788 3.862278 26 0.267 -4.975717 8.972584 netherland~i | -.1517474 .2406202 30 0.229 -.6578035 .3664549 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: region | var(cons) | .0370898 .0090859 4112 .0225153 .0579267 ------------------------------------------------------------------------------ . . mcmcsum [FP1]belgium, detail [FP1]belgium ------------------------------------------------------------------------------ Percentiles Mean .8578639 0.5% -.7158128 Thinned Chain Length 50000 MCSE of Mean .0899193 2.5% -.3351097 Effective Sample Size 41 Std. Dev. .6401522 5% -.0606993 Raftery Lewis (2.5%) 334698 Mode .6586438 25% .405001 Raftery Lewis (97.5%) 219131 P(mean) 0.061 Brooks Draper (mean) 6.21e+07 P(mode) 0.061 50% .8046708 P(median) 0.061 75% 1.278879 95% 1.894291 97.5% 2.160622 99.5% 2.988487 ------------------------------------------------------------------------------ . . mcmcsum [FP1]belgium, fiveway . . runmlwin obs belgium-netherlands belgiumXuvbi-netherlandsXuvbi, /// > level2(region: cons) /// > level1(county:) /// > discrete(distribution(poisson) offset(logexp)) /// > mcmc(chain(50000) orth seed(1)) initsb(b) initsv(V) nopause MLwiN 3.05 multilevel model Number of obs = 354 Poisson response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ region | 78 1 4.5 13 ----------------------------------------------------------- Burnin = 500 Chain = 50000 Thinning = 1 Run time (seconds) = 24.3 Deviance (dbar) = 2027.64 Deviance (thetabar) = 1964.52 Effective no. of pars (pd) = 63.12 Bayesian DIC = 2090.75 ------------------------------------------------------------------------------ obs | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- belgium | .7081447 .7471533 5437 0.171 -.7509914 2.182105 wgermany | .4849175 .1228252 678 0.000 .2464764 .7261572 denmark | .3196319 .8755266 5500 0.355 -1.452078 1.999983 france | -.5943948 .0545185 1910 0.000 -.7022356 -.4884918 uk | .6125529 .2031225 1003 0.002 .2086102 1.015379 italy | .2800315 .1077527 1372 0.006 .0618239 .4855519 ireland | -.4838725 1.303247 7172 0.358 -3.016343 2.058966 luxembourg | 17.27328 16.05845 2627 0.135 -11.5248 52.00409 netherlands | -.3639671 .9235815 2421 0.344 -2.214159 1.443386 belgiumXuvbi | .2669821 .2521899 5558 0.143 -.2251311 .7626787 wgermanyXu~i | -.0116472 .0323296 1098 0.354 -.0742199 .0529623 denmarkXuvbi | -.082718 .1544327 5505 0.298 -.3939303 .2169759 franceXuvbi | .0127898 .0179311 2058 0.232 -.0221638 .0487714 ukXuvbi | .1416577 .0415661 1206 0.000 .058933 .2248391 italyXuvbi | -.0873712 .0161516 1586 0.000 -.1185462 -.0552622 irelandXuvbi | .0104176 .2626996 7049 0.486 -.4951052 .5261829 luxembourg~i | 7.536287 7.012733 2592 0.135 -4.958681 22.69113 netherland~i | -.1168564 .2214749 2444 0.297 -.5583052 .3208941 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: region | var(cons) | .03712 .0092335 4169 .0223701 .0585388 ------------------------------------------------------------------------------ . . mcmcsum [FP1]belgium, detail [FP1]belgium ------------------------------------------------------------------------------ Percentiles Mean .7081447 0.5% -1.202062 Thinned Chain Length 50000 MCSE of Mean .0095749 2.5% -.7509914 Effective Sample Size 5437 Std. Dev. .7471533 5% -.5146143 Raftery Lewis (2.5%) 15210 Mode .6965098 25% .206925 Raftery Lewis (97.5%) 15210 P(mean) 0.171 Brooks Draper (mean) 704367 P(mode) 0.171 50% .7037174 P(median) 0.171 75% 1.212943 95% 1.945702 97.5% 2.182105 99.5% 2.627333 ------------------------------------------------------------------------------ . . mcmcsum [FP1]belgium, fiveway . . . . * 23.5 An Ordered multinomial example . . . . . . . . . . . . . . . . . .368 . . // With thanks to Erik who largely contributed this syntax . . use "http://www.bristol.ac.uk/cmm/media/runmlwin/alevchem.dta", clear . . describe Contains data from http://www.bristol.ac.uk/cmm/media/runmlwin/alevchem.dta obs: 2,166 vars: 8 21 Oct 2011 12:19 ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- lea int %9.0g LEA ID estab int %9.0g Establishment ID pupil float %9.0g Pupil ID a_point byte %9.0g a_point A-level point score gcse_tot byte %9.0g Total GCSE point score gcse_no byte %9.0g Number of GCSEs taken cons byte %9.0g Constant gender byte %9.0g gender Gender ------------------------------------------------------------------------------- Sorted by: . . codebook, compact Variable Obs Unique Mean Min Max Label ------------------------------------------------------------------------------- ----------------------- lea 2166 70 777.0771 203 938 LEA ID estab 2166 156 5973.597 4001 8603 Establishment ID pupil 2166 2166 106869.5 1650 194909 Pupil ID a_point 2166 6 3.518467 1 6 A-level point score gcse_tot 2166 67 54.41736 22 92 Total GCSE point score gcse_no 2166 8 8.807941 5 12 Number of GCSEs taken cons 2166 1 1 1 1 Constant gender 2166 2 .4353647 0 1 Gender ------------------------------------------------------------------------------- ----------------------- . . generate gcseav = gcse_tot/gcse_no . . histogram gcseav (bin=33, start=3.1428571, width=.14718615) . . replace gcseav = gcseav - 6 (2,166 real changes made) . . generate gcseav2 = gcseav^2 . . egen school = group(lea estab) . // Note: Establishment codes on their own do not uniquely identify schools. . // Schools are instead uniquely identified by LEA code, establishment ID . // combination. Thus, here we generated a unique school ID. . . rename gender female . . runmlwin a_point cons (gcseav gcseav2 female, contrast(1/5)), /// > level2(school: (cons, contrast(1/5))) /// > level1(pupil:) /// > discrete(distribution(multinomial) link(ologit) denominator(cons) bas > ecategory(6) pql2) /// > nopause MLwiN 3.05 multilevel model Number of obs = 2166 Ordered multinomial logit response model (hierarchical) Estimation algorithm: IGLS, PQL2 ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ school | 220 1 9.8 94 ----------------------------------------------------------- ---------------------------------- Contrast | Log-odds -------------+-------------------- 1 | 1 vs. 2 3 4 5 6 2 | 1 2 vs. 3 4 5 6 3 | 1 2 3 vs. 4 5 6 4 | 1 2 3 4 vs. 5 6 5 | 1 2 3 4 5 vs. 6 ---------------------------------- Run time (seconds) = 1.64 Number of iterations = 10 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Contrast 1 | cons_1 | -1.96551 .1092139 -18.00 0.000 -2.179565 -1.751454 -------------+---------------------------------------------------------------- Contrast 2 | cons_2 | -.7743616 .0992623 -7.80 0.000 -.9689122 -.579811 -------------+---------------------------------------------------------------- Contrast 3 | cons_3 | .3175344 .0975968 3.25 0.001 .1262482 .5088205 -------------+---------------------------------------------------------------- Contrast 4 | cons_4 | 1.602257 .1044415 15.34 0.000 1.397556 1.806959 -------------+---------------------------------------------------------------- Contrast 5 | cons_5 | 3.431818 .1287165 26.66 0.000 3.179539 3.684098 -------------+---------------------------------------------------------------- gcseav_12345 | -2.295235 .0711587 -32.26 0.000 -2.434703 -2.155766 gcseav~12345 | -.4650907 .0467127 -9.96 0.000 -.556646 -.3735355 female_12345 | .7484153 .0938655 7.97 0.000 .5644424 .9323882 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Level 2: school | var(cons_12345) | .6596637 .1107802 .4425385 .8767888 ------------------------------------------------------------------------------ . . matrix b = e(b) . . matrix V = e(V) . . runmlwin a_point cons (gcseav gcseav2 female, contrast(1/5)), /// > level2(school: (cons, contrast(1/5))) /// > level1(pupil:) /// > discrete(distribution(multinomial) link(ologit) denominator(cons) bas > ecategory(6)) /// > mcmc(on) /// > initsb(b) initsv(V) /// > nopause MLwiN 3.05 multilevel model Number of obs = 2166 Ordered multinomial logit response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ school | 220 1 9.8 94 ----------------------------------------------------------- ---------------------------------- Contrast | Log-odds -------------+-------------------- 1 | 1 vs. 2 3 4 5 6 2 | 1 2 vs. 3 4 5 6 3 | 1 2 3 vs. 4 5 6 4 | 1 2 3 4 vs. 5 6 5 | 1 2 3 4 5 vs. 6 ---------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 14.4 Deviance (dbar) = 5814.59 Deviance (thetabar) = 5692.04 Effective no. of pars (pd) = 122.55 Bayesian DIC = 5937.14 ------------------------------------------------------------------------------ | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- Contrast 1 | cons_1 | -1.923212 .1229307 63 0.000 -2.152823 -1.671016 -------------+---------------------------------------------------------------- Contrast 2 | cons_2 | -.767638 .1160738 48 0.000 -.9890748 -.5330881 -------------+---------------------------------------------------------------- Contrast 3 | cons_3 | .300251 .1138173 48 0.003 .0875123 .5349658 -------------+---------------------------------------------------------------- Contrast 4 | cons_4 | 1.56449 .1215162 55 0.000 1.346591 1.81906 -------------+---------------------------------------------------------------- Contrast 5 | cons_5 | 3.363311 .140462 73 0.000 3.099479 3.653081 -------------+---------------------------------------------------------------- gcseav_12345 | -2.263649 .0684852 337 0.000 -2.405055 -2.122373 gcseav~12345 | -.4578595 .0504218 218 0.000 -.5584839 -.3565204 female_12345 | .7412699 .0985752 210 0.000 .5442226 .9299094 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: school | var(cons_12345) | .6442749 .1402424 208 .3916605 .9493606 ------------------------------------------------------------------------------ . . mcmcsum, trajectories . . mcmcsum ------------------------------------------------------------------------------ | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- [FP1]cons_1 | -1.923212 .1229307 63 0.000 -2.152823 -1.671016 [FP2]cons_2 | -.767638 .1160738 48 0.000 -.9890748 -.5330881 [FP3]cons_3 | .300251 .1138173 48 0.003 .0875123 .5349658 [FP4]cons_4 | 1.56449 .1215162 55 0.000 1.346591 1.81906 [FP5]cons_5 | 3.363311 .140462 73 0.000 3.099479 3.653081 [FP6]gc~12345| -2.263649 .0684852 337 0.000 -2.405055 -2.122373 [FP6]gc~12345| -.4578595 .0504218 218 0.000 -.5584839 -.3565204 [FP6]fe~12345| .7412699 .0985752 210 0.000 .5442225 .9299093 [RP2]v~12345)| .6442749 .1402424 208 0.000 .3916605 .9493606 [OD]bcons_1 | 1 0 0 0.000 1 1 ------------------------------------------------------------------------------ . . runmlwin a_point cons (gcseav gcseav2 female, contrast(1/5)), /// > level2(school: (cons, contrast(1/5))) /// > level1(pupil:) /// > discrete(distribution(multinomial) link(ologit) denominator(cons) bas > ecategory(6)) /// > mcmc(orth) /// > initsb(b) initsv(V) /// > nopause MLwiN 3.05 multilevel model Number of obs = 2166 Ordered multinomial logit response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ school | 220 1 9.8 94 ----------------------------------------------------------- ---------------------------------- Contrast | Log-odds -------------+-------------------- 1 | 1 vs. 2 3 4 5 6 2 | 1 2 vs. 3 4 5 6 3 | 1 2 3 vs. 4 5 6 4 | 1 2 3 4 vs. 5 6 5 | 1 2 3 4 5 vs. 6 ---------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 14.1 Deviance (dbar) = 5814.23 Deviance (thetabar) = 5692.49 Effective no. of pars (pd) = 121.74 Bayesian DIC = 5935.97 ------------------------------------------------------------------------------ | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- Contrast 1 | cons_1 | -1.912577 .1051228 162 0.000 -2.115588 -1.705218 -------------+---------------------------------------------------------------- Contrast 2 | cons_2 | -.7525771 .0984726 148 0.000 -.9518387 -.5668233 -------------+---------------------------------------------------------------- Contrast 3 | cons_3 | .3214777 .0974352 151 0.000 .1306047 .5160795 -------------+---------------------------------------------------------------- Contrast 4 | cons_4 | 1.585137 .108236 168 0.000 1.380164 1.805148 -------------+---------------------------------------------------------------- Contrast 5 | cons_5 | 3.382944 .1361485 208 0.000 3.112653 3.659518 -------------+---------------------------------------------------------------- gcseav_12345 | -2.264433 .07167 280 0.000 -2.404244 -2.126164 gcseav~12345 | -.4584018 .0474596 811 0.000 -.5542965 -.3683149 female_12345 | .7356934 .0974959 768 0.000 .5538487 .9231218 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: school | var(cons_12345) | .6401469 .1346986 228 .4071727 .9310268 ------------------------------------------------------------------------------ . . mcmcsum, trajectories . . mcmcsum ------------------------------------------------------------------------------ | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- [FP1]cons_1 | -1.912577 .1051228 162 0.000 -2.115588 -1.705218 [FP2]cons_2 | -.7525771 .0984726 148 0.000 -.9518387 -.5668233 [FP3]cons_3 | .3214777 .0974352 151 0.000 .1306047 .5160795 [FP4]cons_4 | 1.585137 .108236 168 0.000 1.380164 1.805148 [FP5]cons_5 | 3.382944 .1361485 208 0.000 3.112653 3.659518 [FP6]gc~12345| -2.264433 .07167 280 0.000 -2.404244 -2.126164 [FP6]gc~12345| -.4584018 .0474596 811 0.000 -.5542965 -.3683149 [FP6]fe~12345| .7356934 .0974959 768 0.000 .5538487 .9231218 [RP2]v~12345)| .6401469 .1346986 228 0.000 .4071727 .9310268 [OD]bcons_1 | 1 0 0 0.000 1 1 ------------------------------------------------------------------------------ . . . * 23.6 The WinBUGS interface . . . . . . . . . . . . . . . . . . . . . . 372 . . use "http://www.bristol.ac.uk/cmm/media/runmlwin/bang1.dta", clear . . gen onekid = (lc==1) . . gen twokids = (lc==2) . . gen threepluskids = (lc==3) . . quietly runmlwin use cons age onekid twokids threepluskids urban, /// > level2(district: cons urban) /// > level1(woman:) /// > discrete(distribution(binomial) link(logit) denom(denomb)) nopause . . matrix b = e(b) . . matrix V = e(V) . . runmlwin use cons age onekid twokids threepluskids urban, /// > level2(district: cons urban) /// > level1(woman:) /// > discrete(distribution(binomial) link(logit) denom(denomb)) /// > mcmc(orth savewinbugs( /// > model("orthog_model.txt", replace) /// > inits("orthog_inits.txt", replace) /// > data("orthog_data.txt", replace) /// > )) /// > initsb(b) initsv(V) /// > nopause MLwiN 3.05 multilevel model Number of obs = 1934 Binomial logit response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ district | 60 2 32.2 118 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 8.09 Deviance (dbar) = 2329.93 Deviance (thetabar) = 2274.25 Effective no. of pars (pd) = 55.69 Bayesian DIC = 2385.62 ------------------------------------------------------------------------------ use | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | -1.725307 .1596834 242 0.000 -2.046332 -1.416695 age | -.0268184 .0079077 1069 0.000 -.0420135 -.0112826 onekid | 1.137847 .1608216 1152 0.000 .8388709 1.45232 twokids | 1.362508 .1793792 986 0.000 1.014565 1.705076 threeplusk~s | 1.362581 .1845054 1015 0.000 1.006143 1.724028 urban | .8198761 .1775268 151 0.000 .4960341 1.182494 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: district | var(cons) | .4147928 .1309379 173 .211906 .7276892 cov(cons,urban) | -.4101084 .1750757 81 -.8206579 -.148975 var(urban) | .6757473 .319791 57 .2427979 1.467602 ------------------------------------------------------------------------------ . . wbscript , /// > model("`c(pwd)'\orthog_model.txt") /// > data("`c(pwd)'\orthog_data.txt") /// > inits("`c(pwd)'\orthog_inits.txt") /// > coda("`c(pwd)'\out") /// > set(beta sigma2.u2) /// > burn(500) update(5000) /// > saving("`c(pwd)'\script.txt", replace) /// > quit display('log') check('Q:/C-modelling/runmlwin/orthog_model.txt') data('Q:/C-modelling/runmlwin/orthog_data.txt') compile(1) inits(1,'Q:/C-modelling/runmlwin/orthog_inits.txt') gen.inits() update(500) set('beta') set('sigma2.u2') update(5000) coda(*,'Q:/C-modelling/runmlwin/out') quit() . . wbrun, script("`c(pwd)'\script.txt") /// > winbugs("C:\WinBUGS14\winbugs14.exe") . . wbcoda, root("`c(pwd)'\out") clear . . mcmcsum beta_1, fiveway variables . . mcmcsum beta_1, detail variables beta_1 ------------------------------------------------------------------------------ Percentiles Mean -1.723969 0.5% -2.16202 Thinned Chain Length 5000 MCSE of Mean .0038028 2.5% -2.055 Effective Sample Size 1024 Std. Dev. .1675432 5% -1.995 Raftery Lewis (2.5%) 10204 Mode -1.722068 25% -1.833 Raftery Lewis (97.5%) 8720 P(mean) 0.000 Brooks Draper (mean) 112 P(mode) 0.000 50% -1.725 P(median) 0.000 75% -1.612 95% -1.45095 97.5% -1.391975 99.5% -1.280995 ------------------------------------------------------------------------------ . . mcmcsum, variables ------------------------------------------------------------------------------ | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- order | 3000.5 1443.376 3 0.000 625.975 5375.025 beta_1 | -1.723969 .1675432 1024 0.000 -2.055 -1.391975 beta_2 | -.0265096 .0080105 4189 0.001 -.0423608 -.01127 beta_3 | 1.13405 .1608499 3718 0.000 .8134975 1.452025 beta_4 | 1.359876 .1757908 4224 0.000 1.017975 1.704 beta_5 | 1.360473 .1807651 3649 0.000 1.014975 1.71 beta_6 | .8249551 .1794708 702 0.000 .4725975 1.18 sigma2_u2_1_1| .4258588 .1380137 639 0.000 .217795 .7534425 sigma2_u2_1_2| -.4412842 .1873922 410 0.000 -.8795025 -.1552975 sigma2_u2_2_1| -.4412842 .1873922 410 0.000 -.8795025 -.1552975 sigma2_u2_2_2| .7401441 .3378473 316 0.000 .2696925 1.575 ------------------------------------------------------------------------------ . . * Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . .379 . . . . . . **************************************************************************** . exit end of do-file