. use "C:\Users\gai\Desktop\国立成育医療研究センター仕事関連\JICA SMPP Bangladesh\kalaroa row data\データ解析\comb > ined data_1.dta", clear . by income, sort: meglm skilled_ANC age education religion housewife income n_child i.intervention##i.time || cli > nic:,family(bin) link(logit) eform ------------------------------------------------------------------------------------------------------------------ -> income = 1 note: income omitted because of collinearity Fitting fixed-effects model: Iteration 0: log likelihood = -606.28866 Iteration 1: log likelihood = -605.67615 Iteration 2: log likelihood = -605.67563 Iteration 3: log likelihood = -605.67563 Refining starting values: Grid node 0: log likelihood = -601.92436 Fitting full model: Iteration 0: log likelihood = -601.92436 (not concave) Iteration 1: log likelihood = -598.59301 Iteration 2: log likelihood = -597.26952 Iteration 3: log likelihood = -597.02365 Iteration 4: log likelihood = -596.91455 Iteration 5: log likelihood = -596.91392 Iteration 6: log likelihood = -596.91392 Mixed-effects GLM Number of obs = 1,227 Family: binomial Link: logit Group variable: clinic Number of groups = 21 Obs per group: min = 28 avg = 58.4 max = 125 Integration method: mvaghermite Integration pts. = 7 Wald chi2(8) = 16.03 Log likelihood = -596.91392 Prob > chi2 = 0.0420 ----------------------------------------------------------------------------------- skilled_ANC | exp(b) Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- age | .787571 .1225947 -1.53 0.125 .580484 1.068536 education | 1.098069 .1469218 0.70 0.484 .8447704 1.427317 religion | .6292444 .1343119 -2.17 0.030 .4141244 .9561102 housewife | .8913796 .3656428 -0.28 0.779 .3989339 1.991703 income | 1 (omitted) n_child | 1.05331 .1329527 0.41 0.681 .8224594 1.348957 1.intervention | .5863274 .1694845 -1.85 0.065 .3327291 1.033212 1.time | .651166 .1485047 -1.88 0.060 .4164539 1.018161 | intervention#time | 1 1 | 2.285807 .71728 2.63 0.008 1.235762 4.22809 | _cons | 15.00903 9.797404 4.15 0.000 4.175571 53.94972 ------------------+---------------------------------------------------------------- clinic | var(_cons)| .2276981 .1172631 .0829843 .6247737 ----------------------------------------------------------------------------------- LR test vs. logistic model: chibar2(01) = 17.52 Prob >= chibar2 = 0.0000 ------------------------------------------------------------------------------------------------------------------ -> income = 2 note: income omitted because of collinearity Fitting fixed-effects model: Iteration 0: log likelihood = -338.34575 Iteration 1: log likelihood = -336.91567 Iteration 2: log likelihood = -336.9091 Iteration 3: log likelihood = -336.9091 Refining starting values: Grid node 0: log likelihood = -341.96705 Fitting full model: Iteration 0: log likelihood = -341.96705 (not concave) Iteration 1: log likelihood = -338.01625 (not concave) Iteration 2: log likelihood = -336.46782 Iteration 3: log likelihood = -336.45793 Iteration 4: log likelihood = -336.18363 Iteration 5: log likelihood = -336.17983 Iteration 6: log likelihood = -336.17982 Mixed-effects GLM Number of obs = 770 Family: binomial Link: logit Group variable: clinic Number of groups = 21 Obs per group: min = 18 avg = 36.7 max = 49 Integration method: mvaghermite Integration pts. = 7 Wald chi2(8) = 38.49 Log likelihood = -336.17982 Prob > chi2 = 0.0000 ----------------------------------------------------------------------------------- skilled_ANC | exp(b) Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- age | 1.414604 .307258 1.60 0.110 .9241668 2.165307 education | .88847 .1661919 -0.63 0.527 .6157751 1.281927 religion | .5781904 .1697853 -1.87 0.062 .3251742 1.028077 housewife | 1.045136 .5461847 0.08 0.933 .3752645 2.91077 income | 1 (omitted) n_child | .7194576 .1163639 -2.04 0.042 .5240023 .9878185 1.intervention | .4560792 .1433522 -2.50 0.012 .2463176 .8444717 1.time | .2374448 .0662394 -5.15 0.000 .1374375 .4102232 | intervention#time | 1 1 | 8.388389 3.408668 5.23 0.000 3.78258 18.6024 | _cons | 14.28012 12.70598 2.99 0.003 2.496666 81.67764 ------------------+---------------------------------------------------------------- clinic | var(_cons)| .0989386 .1065379 .0119891 .8164814 ----------------------------------------------------------------------------------- LR test vs. logistic model: chibar2(01) = 1.46 Prob >= chibar2 = 0.1136 ------------------------------------------------------------------------------------------------------------------ -> income = 3 note: income omitted because of collinearity Fitting fixed-effects model: Iteration 0: log likelihood = -477.62148 Iteration 1: log likelihood = -475.37025 Iteration 2: log likelihood = -475.35746 Iteration 3: log likelihood = -475.35746 Refining starting values: Grid node 0: log likelihood = -480.7014 Fitting full model: Iteration 0: log likelihood = -480.7014 (not concave) Iteration 1: log likelihood = -474.6291 Iteration 2: log likelihood = -473.805 Iteration 3: log likelihood = -473.60123 Iteration 4: log likelihood = -473.59622 Iteration 5: log likelihood = -473.59621 Mixed-effects GLM Number of obs = 1,065 Family: binomial Link: logit Group variable: clinic Number of groups = 21 Obs per group: min = 20 avg = 50.7 max = 100 Integration method: mvaghermite Integration pts. = 7 Wald chi2(8) = 23.60 Log likelihood = -473.59621 Prob > chi2 = 0.0027 ----------------------------------------------------------------------------------- skilled_ANC | exp(b) Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- age | 1.073392 .2071767 0.37 0.714 .7353038 1.566931 education | 1.121886 .1890441 0.68 0.495 .8063371 1.560922 religion | 1.141028 .421685 0.36 0.721 .5529953 2.354352 housewife | 1.285695 .5984139 0.54 0.589 .5163624 3.201262 income | 1 (omitted) n_child | .8637495 .1314074 -0.96 0.336 .6410463 1.163821 1.intervention | .3807187 .1175508 -3.13 0.002 .2078677 .6973027 1.time | .2957891 .079077 -4.56 0.000 .1751539 .4995104 | intervention#time | 1 1 | 3.695165 1.315272 3.67 0.000 1.839305 7.423588 | _cons | 6.412846 5.214185 2.29 0.022 1.303023 31.56091 ------------------+---------------------------------------------------------------- clinic | var(_cons)| .1071731 .0860912 .0221984 .5174267 ----------------------------------------------------------------------------------- LR test vs. logistic model: chibar2(01) = 3.52 Prob >= chibar2 = 0.0303 ------------------------------------------------------------------------------------------------------------------ -> income = 4 note: income omitted because of collinearity Fitting fixed-effects model: Iteration 0: log likelihood = -276.23079 Iteration 1: log likelihood = -272.32138 Iteration 2: log likelihood = -272.25421 Iteration 3: log likelihood = -272.2542 Refining starting values: Grid node 0: log likelihood = -279.38052 Fitting full model: Iteration 0: log likelihood = -279.38052 (not concave) Iteration 1: log likelihood = -272.38782 Iteration 2: log likelihood = -272.243 Iteration 3: log likelihood = -272.24171 Iteration 4: log likelihood = -272.2417 Mixed-effects GLM Number of obs = 738 Family: binomial Link: logit Group variable: clinic Number of groups = 21 Obs per group: min = 15 avg = 35.1 max = 66 Integration method: mvaghermite Integration pts. = 7 Wald chi2(8) = 22.18 Log likelihood = -272.2417 Prob > chi2 = 0.0046 ----------------------------------------------------------------------------------- skilled_ANC | exp(b) Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- age | 1.682319 .4039305 2.17 0.030 1.050829 2.6933 education | 1.283778 .2990989 1.07 0.284 .8131562 2.026775 religion | 1.181537 .4332541 0.45 0.649 .5758681 2.424219 housewife | .6295781 .2831542 -1.03 0.304 .2607472 1.520126 income | 1 (omitted) n_child | .6010029 .1102355 -2.78 0.006 .4195172 .8610004 1.intervention | .2729136 .1136683 -3.12 0.002 .1206425 .6173762 1.time | .3311327 .1344911 -2.72 0.007 .1493766 .7340431 | intervention#time | 1 1 | 3.406401 1.701317 2.45 0.014 1.279869 9.066214 | _cons | 9.914059 9.508922 2.39 0.017 1.512984 64.96341 ------------------+---------------------------------------------------------------- clinic | var(_cons)| .0111596 .073984 2.54e-08 4906.801 ----------------------------------------------------------------------------------- LR test vs. logistic model: chibar2(01) = 0.02 Prob >= chibar2 = 0.4372 ------------------------------------------------------------------------------------------------------------------ -> income = 5 note: income omitted because of collinearity Fitting fixed-effects model: Iteration 0: log likelihood = -231.79591 Iteration 1: log likelihood = -215.29826 Iteration 2: log likelihood = -214.94467 Iteration 3: log likelihood = -214.94392 Iteration 4: log likelihood = -214.94392 Refining starting values: Grid node 0: log likelihood = -217.76383 Fitting full model: Iteration 0: log likelihood = -217.76383 (not concave) Iteration 1: log likelihood = -215.41592 Iteration 2: log likelihood = -214.42021 Iteration 3: log likelihood = -214.30112 Iteration 4: log likelihood = -214.29963 Iteration 5: log likelihood = -214.29963 Mixed-effects GLM Number of obs = 871 Family: binomial Link: logit Group variable: clinic Number of groups = 21 Obs per group: min = 16 avg = 41.5 max = 76 Integration method: mvaghermite Integration pts. = 7 Wald chi2(8) = 33.89 Log likelihood = -214.29963 Prob > chi2 = 0.0000 ----------------------------------------------------------------------------------- skilled_ANC | exp(b) Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- age | .9677583 .2769103 -0.11 0.909 .5523415 1.695611 education | 2.359926 .577515 3.51 0.000 1.460813 3.812433 religion | 1.312979 .9919736 0.36 0.719 .2986507 5.772343 housewife | .7883573 .41588 -0.45 0.652 .2803439 2.216946 income | 1 (omitted) n_child | 1.503384 .386021 1.59 0.112 .9088846 2.486746 1.intervention | .2210935 .1349366 -2.47 0.013 .066846 .7312684 1.time | .1407181 .0774778 -3.56 0.000 .0478286 .4140111 | intervention#time | 1 1 | 19.60444 13.46314 4.33 0.000 5.102712 75.31959 | _cons | 3.346293 4.518785 0.89 0.371 .2371996 47.20783 ------------------+---------------------------------------------------------------- clinic | var(_cons)| .1584409 .1784418 .0174266 1.440525 ----------------------------------------------------------------------------------- LR test vs. logistic model: chibar2(01) = 1.29 Prob >= chibar2 = 0.1282 ------------------------------------------------------------------------------------------------------------------