------------------------------------------------------------------------------- name: log: Q:\C-modelling\runmlwin\website\logfiles\2024-10-11\18\16_Multiple > _Membership_Models.smcl log type: smcl opened on: 11 Oct 2024, 17:40:34 . **************************************************************************** . * MLwiN MCMC Manual . * . * 16 Multiple Membership Models . . . . . . . . . . . . . . . . . . . . 231 . * . * 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 . * https://www.bristol.ac.uk/cmm/software/runmlwin/ . **************************************************************************** . . * 16.1 Notation and weightings . . . . . . . . . . . . . . . . . . . . . 232 . . * 16.2 Office workers salary dataset . . . . . . . . . . . . . . . . . . 232 . . use "https://www.bristol.ac.uk/cmm/media/runmlwin/wage1.dta", clear . . describe Contains data from https://www.bristol.ac.uk/cmm/media/runmlwin/wage1.dta Observations: 3,022 Variables: 21 21 Oct 2011 12:19 ------------------------------------------------------------------------------- Variable Storage Display Value name type format label Variable label ------------------------------------------------------------------------------- id int %9.0g company int %9.0g company2 int %9.0g company3 int %9.0g company4 int %9.0g age byte %9.0g parttime byte %9.0g sex byte %9.0g cons byte %9.0g earnings float %9.0g logearn float %9.0g numjobs byte %9.0g weight1 float %9.0g weight2 float %9.0g weight3 float %9.0g weight4 float %9.0g ew1 float %9.0g ew2 float %9.0g ew3 float %9.0g ew4 float %9.0g age_40 byte %9.0g ------------------------------------------------------------------------------- Sorted by: . . histogram earnings (bin=34, start=2.4000001, width=3.9176472) . . histogram logearn (bin=34, start=.87546879, width=.11865414) . . . . * 16.3 Models for the earnings data . . . . . . . . . . . . . . . . . . .235 . . quietly runmlwin logearn cons age_40 numjobs, /// > level2(company:) /// > level1(id: cons) /// > nopause . . runmlwin logearn cons age_40 numjobs, /// > level2(company:) /// > level1(id: cons) /// > mcmc(on) initsprevious /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 7.8 Deviance (dbar) = 5199.63 Deviance (thetabar) = 5195.62 Effective no. of pars (pd) = 4.01 Bayesian DIC = 5203.64 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.079358 .0302009 4951 0.000 3.020561 3.138446 age_40 | .0121495 .0010487 4640 0.000 .0101458 .0142276 numjobs | -.1292604 .0237046 4937 0.000 -.1759341 -.0836415 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .3272539 .0084362 4805 .3114399 .3444208 ------------------------------------------------------------------------------ . . quietly runmlwin logearn cons age_40 numjobs sex parttime, /// > level2(company:) /// > level1(id: cons) /// > nopause . . runmlwin logearn cons age_40 numjobs sex parttime, /// > level2(company:) /// > level1(id: cons) /// > mcmc(on) initsprevious /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 8.54 Deviance (dbar) = 4989.95 Deviance (thetabar) = 4983.97 Effective no. of pars (pd) = 5.98 Bayesian DIC = 4995.93 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.084506 .0299655 5037 0.000 3.025153 3.142146 age_40 | .0107981 .0010155 4676 0.000 .0088079 .0127735 numjobs | -.0319671 .0239338 4897 0.094 -.0781933 .0148621 sex | -.2120529 .0209641 5566 0.000 -.253383 -.1716581 parttime | -.3884346 .0363231 4853 0.000 -.4589145 -.3175621 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .3051752 .0078155 5042 .2904291 .3210011 ------------------------------------------------------------------------------ . . correlate parttime sex numjobs (obs=3,022) | parttime sex numjobs -------------+--------------------------- parttime | 1.0000 sex | 0.0399 1.0000 numjobs | 0.2908 0.1014 1.0000 . . . . . * 16.4 Fitting multiple membership models to the dataset . . . . . . . . 237 . . tabulate numjobs numjobs | Freq. Percent Cum. ------------+----------------------------------- 1 | 2,496 82.59 82.59 2 | 472 15.62 98.21 3 | 52 1.72 99.93 4 | 2 0.07 100.00 ------------+----------------------------------- Total | 3,022 100.00 . . quietly runmlwin logearn cons age_40 sex parttime, /// > level2(company: cons) /// > level1(id: cons) /// > nopause . . matrix b = e(b) . . matrix V = e(V) . . runmlwin logearn cons age_40 sex parttime, /// > level2(company: cons) /// > level1(id: cons) /// > mcmc(on) initsb(b) initsv(V) /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (hierarchical) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 11.2 Deviance (dbar) = 4421.94 Deviance (thetabar) = 4312.47 Effective no. of pars (pd) = 109.47 Bayesian DIC = 4531.41 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.038998 .0225497 644 0.000 2.994182 3.083892 age_40 | .0113139 .0009288 4322 0.000 .009478 .0131428 sex | -.2120948 .0191524 4907 0.000 -.2497896 -.1746422 parttime | -.3995344 .0325715 4367 0.000 -.4636166 -.3365876 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: company | var(cons) | .0510972 .008027 2076 .0374119 .0689632 -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .253052 .0066259 4736 .2402109 .2666489 ------------------------------------------------------------------------------ . . . runmlwin logearn cons age_40 sex parttime, /// > level2(company: cons, mmids(company-company4) mmweights(weight1-weigh > t4) residuals(u)) /// > level1(id: cons) /// > mcmc(on) initsb(b) initsv(V) /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (cross-classified) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 11.7 Deviance (dbar) = 4354.74 Deviance (thetabar) = 4241.14 Effective no. of pars (pd) = 113.59 Bayesian DIC = 4468.33 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.040285 .0233908 590 0.000 2.993727 3.086772 age_40 | .0113835 .0009185 4348 0.000 .0095648 .0131809 sex | -.2171023 .0189535 4921 0.000 -.2545846 -.1799667 parttime | -.4043283 .0320838 4480 0.000 -.4673991 -.3421338 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: company | var(cons) | .0584859 .0088572 2288 .0432511 .0781998 -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .2474846 .0064772 4746 .2349488 .260777 ------------------------------------------------------------------------------ . . . . * 16.5 Residuals in multiple membership models . . . . . . . . . . . . . 240 . . preserve . . rename company company1 . . keep company? id u0_? u0se_? . . reshape long company u0_ u0se_, i(id) j(order) (j = 1 2 3 4) Data Wide -> Long ----------------------------------------------------------------------------- Number of observations 3,022 -> 12,088 Number of variables 13 -> 5 j variable (4 values) -> order xij variables: company1 company2 ... company4 -> company u0_1 u0_2 ... u0_4 -> u0_ u0se_1 u0se_2 ... u0se_4 -> u0se_ ----------------------------------------------------------------------------- . . drop id order . . rename u0_ u0 . . rename u0se_ u0se . . drop if u0==. (8,484 observations deleted) . . duplicates drop Duplicates in terms of all variables (3,463 observations deleted) . . egen u0rank = rank(u0) . . serrbar u0 u0se u0rank, yline(0) scale(1.4) . . restore . . gen companyno54 = (company==54) + (company2==54) + (company3==54) + (company4 > ==54) . . gen companyno67 = (company==67) + (company2==67) + (company3==67) + (company4 > ==67) . . quietly runmlwin logearn cons age_40 sex parttime companyno54 companyno67, // > / > level2(company: cons) /// > level1(id: cons) /// > nopause . . matrix b = e(b) . . matrix V = e(V) . . runmlwin logearn cons age_40 sex parttime companyno54 companyno67, /// > level2(company: cons, mmids(company-company4) mmweights(weight1-weigh > t4)) /// > level1(id: cons) /// > mcmc(on) initsb(b) initsv(V) /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (cross-classified) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 12.2 Deviance (dbar) = 4356.92 Deviance (thetabar) = 4249.02 Effective no. of pars (pd) = 107.90 Bayesian DIC = 4464.82 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.026183 .022553 657 0.000 2.980411 3.06955 age_40 | .0114089 .0009284 4423 0.000 .0096208 .01321 sex | -.217623 .018781 4747 0.000 -.2549375 -.1809531 parttime | -.4108219 .0323594 4803 0.000 -.4723526 -.3479904 companyno54 | .7584098 .1882626 579 0.000 .3861862 1.112854 companyno67 | .8725464 .2212848 514 0.000 .4534037 1.311709 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: company | var(cons) | .0446218 .0072112 1824 .0322859 .0608833 -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .2477224 .0064546 4669 .2356129 .2606138 ------------------------------------------------------------------------------ . . . . * 16.6 Alternative weights for multiple membership models . . . . . . . .243 . . runmlwin logearn cons age_40 sex parttime companyno54 companyno67, /// > level2(company: cons, mmids(company-company4) mmweights(ew1-ew4)) /// > level1(id: cons) /// > mcmc(on) initsb(b) initsv(V) /// > nopause MLwiN 3.13 multilevel model Number of obs = 3022 Normal response model (cross-classified) Estimation algorithm: MCMC ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ company | 141 2 21.4 49 ----------------------------------------------------------- Burnin = 500 Chain = 5000 Thinning = 1 Run time (seconds) = 12.6 Deviance (dbar) = 4369.58 Deviance (thetabar) = 4262.31 Effective no. of pars (pd) = 107.27 Bayesian DIC = 4476.85 ------------------------------------------------------------------------------ logearn | Mean Std. Dev. ESS P [95% Cred. Interval] -------------+---------------------------------------------------------------- cons | 3.02483 .0226173 660 0.000 2.979164 3.068581 age_40 | .011398 .0009306 4406 0.000 .0096131 .0132134 sex | -.2165472 .0188181 4741 0.000 -.2537445 -.1797589 parttime | -.4103553 .0324274 4836 0.000 -.4720493 -.3473305 companyno54 | .7856056 .1943179 540 0.000 .3983148 1.151921 companyno67 | .8710226 .2241333 506 0.000 .4459395 1.314418 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int] -----------------------------+------------------------------------------------ Level 2: company | var(cons) | .0448761 .0072815 1809 .0324815 .0611738 -----------------------------+------------------------------------------------ Level 1: id | var(cons) | .2487619 .0064802 4669 .2366046 .2616759 ------------------------------------------------------------------------------ . . . . * 16.7 Multiple membership multiple classification (MMMC) models . . . . 244 . . * Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . .245 . . . . . . **************************************************************************** . exit end of do-file