------------------------------------------------------------------------------------------------------------------ log: C:\Documents and Settings\gillian raab\My Documents\aprojects\peas\ex3datafiles\program_code\ex3.log log type: text opened on: 28 Aug 2005, 14:02:10 . do "C:\DOCUME~1\GILLIA~1\LOCALS~1\Temp\STD03000000.tmp" . /*----------------------------------------------- > To investigate the effect of other survey designs > one can redo the svyset command > BUT before rerunning we need to clear previous settings > --------------------------------------------------------------------*/ . /*-----------first just weights---------------*/ . svyset, clear(all) no variables are set . svyset [pwei=weighta] pweight is weighta . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata: Number of strata = 1 PSU: Number of PSUs = 9014 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0057008 1.318523 1.14827 ------------------------------------------------------------------------------ . /*-----------then add strata---------------*/ . svyset, clear(all) no variables are set . svyset [pwei=weighta],strata(regstrat) pweight is weighta strata is regstrat . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: Number of PSUs = 9014 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0056322 1.286988 1.134455 ------------------------------------------------------------------------------ . *--------------now psus no strata----------------*/ . /*-----------first just weights---------------*/ . svyset, clear(all) no variables are set . svyset [pwei=weighta],psu(psu) pweight is weighta psu is psu . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata: Number of strata = 1 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0074961 2.279774 1.509892 ------------------------------------------------------------------------------ . /*-----------now the full design as before---------------*/ . svyset, clear(all) no variables are set . svyset [pwei=weighta],strata(regstrat) psu(psu) pweight is weighta strata is regstrat psu is psu . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0060102 1.465561 1.210604 ------------------------------------------------------------------------------ . . /*---------------------------------------------------------- > now looking at rates by sex > -------------------------------------------------------------*/ . svymean smoker, by(sex) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | male | .3419507 .0084952 .3251718 .3587296 1.419651 female | .3245964 .0078806 .3090315 .3401614 1.29928 ------------------------------------------------------------------------------ . /*--------- to get a test of differrences by sex use lincom > for linear combinations-----------------*/ . lincom [smoker]male-[smoker]female ( 1) [smoker]male - [smoker]female = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0173543 .0111258 1.56 0.121 -.0046203 .0393288 ------------------------------------------------------------------------------ . /*---------------------------------------------------------- > and by adults in the household > -------------------------------------------------------------*/ . svymean smoker, by(nofad) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | nofad==1 | .4408193 .0099747 .4211183 .4605202 .6521785 nofad==2 | .3189418 .0073425 .3044398 .3334438 1.219789 nofad==3 | .2887937 .0145437 .2600685 .3175188 1.607311 nofad==4 | .2894893 .0283638 .2334682 .3455104 2.801671 nofad==5 | .3479849 .0740851 .2016601 .4943097 3.931923 nofad==6 | 0 0 0 0 . nofad==7 | 0 0 0 0 . nofad==8 | .617777 .3339362 -.0417778 1.277332 6.100542 nofad==9 | 0 0 0 0 . ------------------------------------------------------------------------------ . /*-----and compare nofad=1 with nofad=2-----------------*/ . lincom [smoker]1-[smoker]2 ( 1) [smoker]1 - [smoker]2 = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .1218775 .012969 9.40 0.000 .0962625 .1474925 ------------------------------------------------------------------------------ . /*------------------------------------------------- > smoking rates by region or health board are also easily calculated > and lincom can give the comparisons between any pair > or other combination > > -------------------------------------------------------*/ . svymean smoker, by(region) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | Highland | .3278274 .0250704 .2783111 .3773436 1.375797 Grampian | .3267013 .0160343 .295032 .3583705 1.900358 Lothian_ | .3213779 .0114178 .2988266 .3439292 1.198679 Borders, | .2893518 .0228964 .2441293 .3345743 1.128549 Glagow | .3633258 .0164955 .3307457 .395906 1.845421 Lanarksh | .3425938 .0131008 .3167184 .3684691 1.256116 Forth_Va | .3273929 .0154412 .296895 .3578907 1.342229 ------------------------------------------------------------------------------ . svymean smoker, by(hboard) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | Ayreshir | .3405227 .0249012 .2913406 .3897047 2.091303 Borders | .2751782 .0339687 .2080869 .3422696 1.216756 Argyll_& | .3377994 .0226662 .2930315 .3825673 1.405142 Fife | .3514359 .0179934 .3158974 .3869745 1.048564 Greater_ | .3633258 .0164955 .3307457 .395906 1.845421 Highland | .3594827 .0311305 .2979971 .4209684 1.563275 Lanarksh | .3443544 .0187207 .3073794 .3813295 1.382976 Grampian | .2982887 .0189685 .2608242 .3357531 1.442587 Orkney | .2323456 .0107839 .2110463 .2536448 .0225086 Lothian | .3038667 .0152057 .2738341 .3338993 1.385 Tayside | .3570112 .024537 .3085483 .4054741 2.063279 Forth_Va | .317252 .0228504 .2721205 .3623836 1.513442 Western_ | .2505246 .0263358 .198509 .3025402 .1420666 Dumfries | .3021828 .0269545 .2489452 .3554204 .8004963 Shetland | .1831689 .0670901 .0506597 .3156781 1.141379 ------------------------------------------------------------------------------ . . lincom [smoker]Fife-[smoker]Lothian ( 1) [smoker]Fife - [smoker]Lothian = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0475692 .0237606 2.00 0.047 .0006399 .0944985 ------------------------------------------------------------------------------ . lincom [smoker]Lanarksh-[smoker]Ayreshir ( 1) - [smoker]Ayreshir + [smoker]Lanarksh = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0038318 .0349296 0.11 0.913 -.0651573 .0728209 ------------------------------------------------------------------------------ . . /*--------- sorry about spelling mistake - in original file-----*/ . /*----------------------------------------------------- > now logistic regressions to predict smoking > > To use categorical variables you must first generate a set of dummy variables > here for number of adults > --------------------------------------------------*/ . tabulate nofad,generate(nofad) Number of | adults. | Freq. Percent Cum. ------------+----------------------------------- 1 | 3,046 33.67 33.67 2 | 4,613 50.99 84.66 3 | 992 10.96 95.62 4 | 330 3.65 99.27 5 | 56 0.62 99.89 6 | 6 0.07 99.96 7 | 1 0.01 99.97 8 | 2 0.02 99.99 9 | 1 0.01 100.00 ------------+----------------------------------- Total | 9,047 100.00 . /*---------------------------------------------- > check the data set to see the new variables > as there are so few households of more than 5 > it seems sensible to group them together > and then to carry out the regression > ---------------------------------------------------*/ . replace nofad5=1 if nofad>5 (10 real changes made) . /*---regressions include the comparisons with nofad1 only--------*/ . svylogit smoker nofad2 nofad3 nofad4 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 3, 156) = 25.41 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.4625912 .0598125 -7.73 0.000 -.5807264 -.3444561 nofad3 | -.6052021 .0829158 -7.30 0.000 -.7689683 -.4414358 nofad4 | -.6018177 .147868 -4.07 0.000 -.8938706 -.3097647 _cons | -.296048 .0465228 -6.36 0.000 -.3879349 -.2041611 ------------------------------------------------------------------------------ . /*------------ we can compare with simple logistic regression--------- > --------------use coef to get comaparable results to the svy command----*/ . logistic smoker nofad2 nofad3 nofad4,coef Logistic regression Number of obs = 9014 LR chi2(3) = 143.44 Prob > chi2 = 0.0000 Log likelihood = -5752.579 Pseudo R2 = 0.0123 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5172813 .0482623 -10.72 0.000 -.6118737 -.4226889 nofad3 | -.6473839 .0795895 -8.13 0.000 -.8033764 -.4913914 nofad4 | -.6380871 .1279033 -4.99 0.000 -.8887729 -.3874012 _cons | -.2803519 .0362628 -7.73 0.000 -.3514257 -.2092781 ------------------------------------------------------------------------------ . /*-------------- and we can get more complicated models > looking at joint effect of age group sex > and number of adults > Test commands can be used to check if variables are significant in > the larger models > --------------------------------------------------------------*/ . tabulate hboard,generate(hboard) Health Board | Freq. Percent Cum. --------------------+----------------------------------- Ayreshire & Arran | 744 8.22 8.22 Borders | 388 4.29 12.51 Argyll & Clyde | 614 6.79 19.30 Fife | 662 7.32 26.62 Greater Glasgow | 1,294 14.30 40.92 Highland | 681 7.53 48.45 Lanarkshire | 871 9.63 58.07 Grampian | 726 8.02 66.10 Orkney | 63 0.70 66.80 Lothian | 1,174 12.98 79.77 Tayside | 725 8.01 87.79 Forth Valley | 509 5.63 93.41 Western Isles | 98 1.08 94.50 Dumfries & Galloway | 438 4.84 99.34 Shetland | 60 0.66 100.00 --------------------+----------------------------------- Total | 9,047 100.00 . tabulate ageg,generate(ageg) ageg | Freq. Percent Cum. ------------+----------------------------------- 16-19 | 391 4.32 4.32 25-29 | 536 5.92 10.25 35-39 | 765 8.46 18.70 45-49 | 973 10.75 29.46 55-59 | 984 10.88 40.33 65-69 | 852 9.42 49.75 70-74 | 759 8.39 58.14 60-64 | 831 9.19 67.33 50-54 | 742 8.20 75.53 40-44 | 750 8.29 83.82 30-34 | 760 8.40 92.22 20-24 | 704 7.78 100.00 ------------+----------------------------------- Total | 9,047 100.00 . tabulate sex,generate(sex) Sex of | respondent | from | household | grid. O | Freq. Percent Cum. ------------+----------------------------------- male | 3,941 43.56 43.56 female | 5,106 56.44 100.00 ------------+----------------------------------- Total | 9,047 100.00 . svylogit smoker nofad2 nofad3 nofad4 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 3, 156) = 25.41 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.4625912 .0598125 -7.73 0.000 -.5807264 -.3444561 nofad3 | -.6052021 .0829158 -7.30 0.000 -.7689683 -.4414358 nofad4 | -.6018177 .147868 -4.07 0.000 -.8938706 -.3097647 _cons | -.296048 .0465228 -6.36 0.000 -.3879349 -.2041611 ------------------------------------------------------------------------------ . svylogit smoker nofad2 nofad3 nofad4 sex2 ageg2-ageg12 hboard2-hboard15 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 29, 130) = 11.94 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5612387 .0609933 -9.20 0.000 -.681706 -.4407714 nofad3 | -.7485357 .0909213 -8.23 0.000 -.9281135 -.5689578 nofad4 | -.7860213 .1571321 -5.00 0.000 -1.096372 -.475671 sex2 | -.1193292 .0521901 -2.29 0.024 -.2224095 -.0162489 ageg2 | .4888972 .1821246 2.68 0.008 .1291843 .8486101 ageg3 | .2063644 .1559656 1.32 0.188 -.1016821 .5144109 ageg4 | .3205004 .1600491 2.00 0.047 .0043888 .6366121 ageg5 | .1117259 .1457159 0.77 0.444 -.1760764 .3995282 ageg6 | .2468631 .1576949 1.57 0.119 -.064599 .5583251 ageg7 | .1650847 .1686705 0.98 0.329 -.1680551 .4982244 ageg8 | .1918504 .1501647 1.28 0.203 -.1047388 .4884395 ageg9 | .1454189 .1645114 0.88 0.378 -.1795063 .4703441 ageg10 | -.1560555 .1598613 -0.98 0.330 -.4717963 .1596854 ageg11 | -.4393657 .1757703 -2.50 0.013 -.7865283 -.0922032 ageg12 | -.7704922 .1644425 -4.69 0.000 -1.095281 -.4457032 hboard2 | -.2949776 .203661 -1.45 0.149 -.6972269 .1072717 hboard3 | -.0308084 .145793 -0.21 0.833 -.318763 .2571462 hboard4 | .017459 .1351148 0.13 0.897 -.2494052 .2843233 hboard5 | .0521162 .1321553 0.39 0.694 -.2089026 .3131351 hboard6 | .0988347 .1723596 0.57 0.567 -.2415914 .4392609 hboard7 | .0257908 .1519292 0.17 0.865 -.2742833 .3258648 hboard8 | -.2064544 .1482992 -1.39 0.166 -.4993589 .0864502 hboard9 | -.5131615 .1361878 -3.77 0.000 -.7821449 -.2441781 hboard10 | -.2055899 .1291238 -1.59 0.113 -.4606214 .0494415 hboard11 | .0477432 .1538141 0.31 0.757 -.2560537 .3515401 hboard12 | -.1433606 .1576678 -0.91 0.365 -.4547691 .1680478 hboard13 | -.4630294 .1779328 -2.60 0.010 -.814463 -.1115958 hboard14 | -.1637861 .1662252 -0.99 0.326 -.4920962 .164524 hboard15 | -.8051732 .4804435 -1.68 0.096 -1.754093 .1437469 _cons | -.196425 .1790967 -1.10 0.274 -.5501576 .1573076 ------------------------------------------------------------------------------ . test sex2 Adjusted Wald test ( 1) sex2 = 0 F( 1, 158) = 5.23 Prob > F = 0.0236 . test ageg2 ageg3 ageg4 ageg5 ageg6 ageg7 ageg8 ageg9 ageg10 ageg11 ageg12 Adjusted Wald test ( 1) ageg2 = 0 ( 2) ageg3 = 0 ( 3) ageg4 = 0 ( 4) ageg5 = 0 ( 5) ageg6 = 0 ( 6) ageg7 = 0 ( 7) ageg8 = 0 ( 8) ageg9 = 0 ( 9) ageg10 = 0 (10) ageg11 = 0 (11) ageg12 = 0 F( 11, 148) = 13.25 Prob > F = 0.0000 . /*----------------------------------------------------------------- > get dummies for the age sex interaction > --------------------------------------------------------------------*/ . generate ageg2s=ageg2*(sex==1) . generate ageg3s=ageg3*(sex==1) . generate ageg4s=ageg4*(sex==1) . generate ageg5s=ageg5*(sex==1) . generate ageg6s=ageg6*(sex==1) . generate ageg7s=ageg7*(sex==1) . generate ageg8s=ageg8*(sex==1) . generate ageg9s=ageg9*(sex==1) . generate ageg10s=ageg10*(sex==1) . generate ageg11s=ageg11*(sex==1) . generate ageg12s=ageg12*(sex==1) . svylogit smoker nofad2 nofad3 nofad4 sex2 ageg2-ageg12 hboard2-hboard15 ageg2s-ageg12s Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 40, 119) = 7.71 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5649544 .0608856 -9.28 0.000 -.6852091 -.4446997 nofad3 | -.7536616 .0926128 -8.14 0.000 -.9365805 -.5707427 nofad4 | -.8098296 .1577345 -5.13 0.000 -1.12137 -.4982894 sex2 | .2744983 .2710147 1.01 0.313 -.2607807 .8097773 ageg2 | .0620744 .2462163 0.25 0.801 -.4242254 .5483743 ageg3 | .0229399 .2295454 0.10 0.921 -.4304334 .4763131 ageg4 | -.0247639 .2006926 -0.12 0.902 -.4211503 .3716225 ageg5 | -.1542414 .1982643 -0.78 0.438 -.5458315 .2373488 ageg6 | .0865896 .2108846 0.41 0.682 -.3299268 .5031061 ageg7 | -.0092496 .224278 -0.04 0.967 -.4522192 .4337201 ageg8 | .0533689 .1981527 0.27 0.788 -.3380009 .4447388 ageg9 | .1510162 .2365119 0.64 0.524 -.3161166 .618149 ageg10 | -.5590369 .2192275 -2.55 0.012 -.9920313 -.1260425 ageg11 | -.5204729 .2332741 -2.23 0.027 -.9812107 -.0597351 ageg12 | -.8953448 .2270752 -3.94 0.000 -1.343839 -.4468504 hboard2 | -.3028365 .207801 -1.46 0.147 -.7132627 .1075896 hboard3 | -.0337248 .1478331 -0.23 0.820 -.3257087 .2582592 hboard4 | .0032226 .1385716 0.02 0.981 -.2704691 .2769143 hboard5 | .0453948 .1320654 0.34 0.732 -.2154465 .306236 hboard6 | .0938063 .1739568 0.54 0.590 -.2497744 .437387 hboard7 | .0224807 .1529716 0.15 0.883 -.2796522 .3246136 hboard8 | -.2122447 .1511458 -1.40 0.162 -.5107715 .0862821 hboard9 | -.5275344 .1574306 -3.35 0.001 -.8384743 -.2165945 hboard10 | -.2124226 .1302862 -1.63 0.105 -.4697497 .0449046 hboard11 | .0445227 .1562242 0.28 0.776 -.2640345 .35308 hboard12 | -.1588901 .1584109 -1.00 0.317 -.4717662 .153986 hboard13 | -.4628882 .1811377 -2.56 0.012 -.8206519 -.1051246 hboard14 | -.1826604 .1682906 -1.09 0.279 -.5150499 .1497291 hboard15 | -.8062307 .4707845 -1.71 0.089 -1.736073 .1236121 ageg2s | .824665 .3715844 2.22 0.028 .0907517 1.558578 ageg3s | .3504848 .3230779 1.08 0.280 -.2876237 .9885933 ageg4s | .6673566 .2912065 2.29 0.023 .092197 1.242516 ageg5s | .5117199 .2978448 1.72 0.088 -.076551 1.099991 ageg6s | .3084944 .3122679 0.99 0.325 -.3082634 .9252521 ageg7s | .3364531 .3147512 1.07 0.287 -.2852094 .9581156 ageg8s | .2614396 .3404926 0.77 0.444 -.4110647 .9339438 ageg9s | -.0546145 .3289639 -0.17 0.868 -.7043484 .5951195 ageg10s | .7881987 .3413227 2.31 0.022 .114055 1.462342 ageg11s | .1063126 .3237929 0.33 0.743 -.5332082 .7458333 ageg12s | .1880666 .3350127 0.56 0.575 -.4736143 .8497476 _cons | -.3746603 .2330058 -1.61 0.110 -.8348683 .0855476 ------------------------------------------------------------------------------ . test ageg2s ageg3s ageg4s ageg5s ageg6s ageg7s ageg8s ageg9s ageg10s ageg11s ageg12s Adjusted Wald test ( 1) ageg2s = 0 ( 2) ageg3s = 0 ( 3) ageg4s = 0 ( 4) ageg5s = 0 ( 5) ageg6s = 0 ( 6) ageg7s = 0 ( 7) ageg8s = 0 ( 8) ageg9s = 0 ( 9) ageg10s = 0 (10) ageg11s = 0 (11) ageg12s = 0 F( 11, 148) = 2.02 Prob > F = 0.0303 . . . . . . /*--------------------------------------------------- > now replication methods > YOU WILL NEED ONE OF THE LARGER VERSIONS OF > Stata (SE or INTERCOOLED) to run this exemplar > > You need to do increase the memory to run this analysis > ------------------------------------------------------------------------------------------------------*/ . clear . set maxvar 3000 Current memory allocation current memory usage settable value description (1M = 1024k) -------------------------------------------------------------------- set maxvar 3000 max. variables allowed 1.040M set memory 10M max. data space 10.000M set matsize 400 max. RHS vars in models 1.254M ----------- 12.294M . set virtual on . set memory 800M Current memory allocation current memory usage settable value description (1M = 1024k) -------------------------------------------------------------------- set maxvar 3000 max. variables allowed 1.040M set memory 800M max. data space 800.000M set matsize 400 max. RHS vars in models 1.254M ----------- 802.294M . /*--------- now reopen your saved data file----------------------- > first changing directory to whee your file is located > read in data and redine design, just to be sure all OK > ---------------------------------------------------------------------------------------*/ . cd "C:\Documents and Settings\gillian raab\My Documents\aprojects\peas\ex3datafiles\datax" C:\Documents and Settings\gillian raab\My Documents\aprojects\peas\ex3datafiles\datax . use ex3,clear . . /*----------------------------------------------------------------------------------------------- > the next bit of code adds the sum of the weights by region and age sex groups > so that they are added to the data file ready to use for post startiofication > > This sample has already been poststratified, but to get the right SEs we need to redo the > psotstaritification and carry it out on each of the replicates > --------------------------------------------------------------------------------------------------*/ . collapse (sum) rtot=weight, by (region) /* make data file with totals*/ . sort region . save rtots,replace /* sort and save it*/ file rtots.dta saved . use ex3,clear . sort region /* get original file and sort by region*/ . merge region using rtots /*----------merge with totals and save as new file--------------*/ (label region already defined) . save ex3new,replace file ex3new.dta saved . drop _merge /*---------- or next bit will fail------------------*/ . tab ageg ageg | Freq. Percent Cum. ------------+----------------------------------- 16-19 | 391 4.32 4.32 25-29 | 536 5.92 10.25 35-39 | 765 8.46 18.70 45-49 | 973 10.75 29.46 55-59 | 984 10.88 40.33 65-69 | 852 9.42 49.75 70-74 | 759 8.39 58.14 60-64 | 831 9.19 67.33 50-54 | 742 8.20 75.53 40-44 | 750 8.29 83.82 30-34 | 760 8.40 92.22 20-24 | 704 7.78 100.00 ------------+----------------------------------- Total | 9,047 100.00 . gen agesex=ageg*100+sex/* make an age sex varaible---*/ . save ex3new,replace file ex3new.dta saved . tab agesex agesex | Freq. Percent Cum. ------------+----------------------------------- 101 | 188 2.08 2.08 102 | 203 2.24 4.32 201 | 211 2.33 6.65 202 | 325 3.59 10.25 301 | 344 3.80 14.05 302 | 421 4.65 18.70 401 | 420 4.64 23.34 402 | 553 6.11 29.46 501 | 443 4.90 34.35 502 | 541 5.98 40.33 601 | 385 4.26 44.59 602 | 467 5.16 49.75 701 | 349 3.86 53.61 702 | 410 4.53 58.14 801 | 345 3.81 61.95 802 | 486 5.37 67.33 901 | 349 3.86 71.18 902 | 393 4.34 75.53 1001 | 334 3.69 79.22 1002 | 416 4.60 83.82 1101 | 303 3.35 87.17 1102 | 457 5.05 92.22 1201 | 270 2.98 95.20 1202 | 434 4.80 100.00 ------------+----------------------------------- Total | 9,047 100.00 . collapse (sum) asext=weight, by (agesex) /* make data file with totals*/ . sort agesex . save astots,replace /* sort and save it*/ file astots.dta saved . use ex3new,clear . sort agesex /* get original file and sort by ctband*/ . merge agesex using astots /*----------merge with totals and save--------------*/ . save ex3new,replace file ex3new.dta saved . /*----------------------------------------------- > you now have a file with agesex and region totals > -----------------------------------------------------*/ . . . /*------------------- ------------------------------------------------------------------ > and make a set of jacknife weights for this survey > This next command will create 312 new variables (one for each replicate) where one > of the 312 PSUs is dropped from each replication. Look at the data to check this > ----------------------------------------------------------------------------------------------------------*/ . survwgt create jkn, psu(psu) weight(weight) strata(regstrat) Generating replicate weights...................................................................................... > ................................................................................................................ > ................................................................................................................ > .. Created weights and set svr values: meth jkn pw weighta rw jkn_1 jkn_2 jkn_3 jkn_4 jkn_5 jkn_6 jkn_7 jkn_8 jkn_9 jkn_10 jkn_11 jkn_12 jkn_13 jkn_14 jkn_15 jkn_16 jkn_17 jkn_18 jkn_19 jkn_20 jkn_21 jkn_22 jkn_23 jkn_24 jkn_25 jkn_26 jkn_27 jkn_28 jkn_29 jkn_30 jkn_31 jkn_32 jkn_33 jkn_34 jkn_35 jkn_36 jkn_37 jkn_38 jkn_39 jkn_40 jkn_41 jkn_42 jkn_43 jkn_44 jkn_45 jkn_46 jkn_47 jkn_48 jkn_49 jkn_50 jkn_51 jkn_52 jkn_53 jkn_54 jkn_55 jkn_56 jkn_57 jkn_58 jkn_59 jkn_60 jkn_61 jkn_62 jkn_63 jkn_64 jkn_65 jkn_66 jkn_67 jkn_68 jkn_69 jkn_70 jkn_71 jkn_72 jkn_73 jkn_74 jkn_75 jkn_76 jkn_77 jkn_78 jkn_79 jkn_80 jkn_81 jkn_82 jkn_83 jkn_84 jkn_85 jkn_86 jkn_87 jkn_88 jkn_89 jkn_90 jkn_91 jkn_92 jkn_93 jkn_94 jkn_95 jkn_96 jkn_97 jkn_98 jkn_99 jkn_100 jkn_101 jkn_102 jkn_103 jkn_104 jkn_105 jkn_106 jkn_107 jkn_108 jkn_109 jkn_110 jkn_111 jkn_112 jkn_113 jkn_114 jkn_115 jkn_116 jkn_117 jkn_118 jkn_119 jkn_120 jkn_121 jkn_122 jkn_123 jkn_124 jkn_125 jkn_126 jkn_127 jkn_128 jkn_129 jkn_130 jkn_131 jkn_132 jkn_133 jkn_134 jkn_135 jkn_136 jkn_137 jkn_138 jkn_139 jkn_140 jkn_141 jkn_142 jkn_143 jkn_144 jkn_145 jkn_146 jkn_147 jkn_148 jkn_149 jkn_150 jkn_151 jkn_152 jkn_153 jkn_154 jkn_155 jkn_156 jkn_157 jkn_158 jkn_159 jkn_160 jkn_161 jkn_162 jkn_163 jkn_164 jkn_165 jkn_166 jkn_167 jkn_168 jkn_169 jkn_170 jkn_171 jkn_172 jkn_173 jkn_174 jkn_175 jkn_176 jkn_177 jkn_178 jkn_179 jkn_180 jkn_181 jkn_182 jkn_183 jkn_184 jkn_185 jkn_186 jkn_187 jkn_188 jkn_189 jkn_190 jkn_191 jkn_192 jkn_193 jkn_194 jkn_195 jkn_196 jkn_197 jkn_198 jkn_199 jkn_200 jkn_201 jkn_202 jkn_203 jkn_204 jkn_205 jkn_206 jkn_207 jkn_208 jkn_209 jkn_210 jkn_211 jkn_212 jkn_213 jkn_214 jkn_215 jkn_216 jkn_217 jkn_218 jkn_219 jkn_220 jkn_221 jkn_222 jkn_223 jkn_224 jkn_225 jkn_226 jkn_227 jkn_228 jkn_229 jkn_230 jkn_231 jkn_232 jkn_233 jkn_234 jkn_235 jkn_236 jkn_237 jkn_238 jkn_239 jkn_240 jkn_241 jkn_242 jkn_243 jkn_244 jkn_245 jkn_246 jkn_247 jkn_248 jkn_249 jkn_250 jkn_251 jkn_252 jkn_253 jkn_254 jkn_255 jkn_256 jkn_257 jkn_258 jkn_259 jkn_260 jkn_261 jkn_262 jkn_263 jkn_264 jkn_265 jkn_266 jkn_267 jkn_268 jkn_269 jkn_270 jkn_271 jkn_272 jkn_273 jkn_274 jkn_275 jkn_276 jkn_277 jkn_278 jkn_279 jkn_280 jkn_281 jkn_282 jkn_283 jkn_284 jkn_285 jkn_286 jkn_287 jkn_288 jkn_289 jkn_290 jkn_291 jkn_292 jkn_293 jkn_294 jkn_295 jkn_296 jkn_297 jkn_298 jkn_299 jkn_300 jkn_301 jkn_302 jkn_303 jkn_304 jkn_305 jkn_306 jkn_307 jkn_308 jkn_309 jkn_310 jkn_311 jkn_312 dof 158 fay 0 psun 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 . /*---------------- now use the survey replication commands--------------------------------*/ . survwgt rake [all] , by(agesex region) totvars( asext rtot) replace SVR settings updated: pw weighta rw jkn_1 jkn_2 jkn_3 jkn_4 jkn_5 jkn_6 jkn_7 jkn_8 jkn_9 jkn_10 jkn_11 jkn_12 jkn_13 jkn_14 jkn_15 jkn_16 jkn_17 jkn_18 jkn_19 jkn_20 jkn_21 jkn_22 jkn_23 jkn_24 jkn_25 jkn_26 jkn_27 jkn_28 jkn_29 jkn_30 jkn_31 jkn_32 jkn_33 jkn_34 jkn_35 jkn_36 jkn_37 jkn_38 jkn_39 jkn_40 jkn_41 jkn_42 jkn_43 jkn_44 jkn_45 jkn_46 jkn_47 jkn_48 jkn_49 jkn_50 jkn_51 jkn_52 jkn_53 jkn_54 jkn_55 jkn_56 jkn_57 jkn_58 jkn_59 jkn_60 jkn_61 jkn_62 jkn_63 jkn_64 jkn_65 jkn_66 jkn_67 jkn_68 jkn_69 jkn_70 jkn_71 jkn_72 jkn_73 jkn_74 jkn_75 jkn_76 jkn_77 jkn_78 jkn_79 jkn_80 jkn_81 jkn_82 jkn_83 jkn_84 jkn_85 jkn_86 jkn_87 jkn_88 jkn_89 jkn_90 jkn_91 jkn_92 jkn_93 jkn_94 jkn_95 jkn_96 jkn_97 jkn_98 jkn_99 jkn_100 jkn_101 jkn_102 jkn_103 jkn_104 jkn_105 jkn_106 jkn_107 jkn_108 jkn_109 jkn_110 jkn_111 jkn_112 jkn_113 jkn_114 jkn_115 jkn_116 jkn_117 jkn_118 jkn_119 jkn_120 jkn_121 jkn_122 jkn_123 jkn_124 jkn_125 jkn_126 jkn_127 jkn_128 jkn_129 jkn_130 jkn_131 jkn_132 jkn_133 jkn_134 jkn_135 jkn_136 jkn_137 jkn_138 jkn_139 jkn_140 jkn_141 jkn_142 jkn_143 jkn_144 jkn_145 jkn_146 jkn_147 jkn_148 jkn_149 jkn_150 jkn_151 jkn_152 jkn_153 jkn_154 jkn_155 jkn_156 jkn_157 jkn_158 jkn_159 jkn_160 jkn_161 jkn_162 jkn_163 jkn_164 jkn_165 jkn_166 jkn_167 jkn_168 jkn_169 jkn_170 jkn_171 jkn_172 jkn_173 jkn_174 jkn_175 jkn_176 jkn_177 jkn_178 jkn_179 jkn_180 jkn_181 jkn_182 jkn_183 jkn_184 jkn_185 jkn_186 jkn_187 jkn_188 jkn_189 jkn_190 jkn_191 jkn_192 jkn_193 jkn_194 jkn_195 jkn_196 jkn_197 jkn_198 jkn_199 jkn_200 jkn_201 jkn_202 jkn_203 jkn_204 jkn_205 jkn_206 jkn_207 jkn_208 jkn_209 jkn_210 jkn_211 jkn_212 jkn_213 jkn_214 jkn_215 jkn_216 jkn_217 jkn_218 jkn_219 jkn_220 jkn_221 jkn_222 jkn_223 jkn_224 jkn_225 jkn_226 jkn_227 jkn_228 jkn_229 jkn_230 jkn_231 jkn_232 jkn_233 jkn_234 jkn_235 jkn_236 jkn_237 jkn_238 jkn_239 jkn_240 jkn_241 jkn_242 jkn_243 jkn_244 jkn_245 jkn_246 jkn_247 jkn_248 jkn_249 jkn_250 jkn_251 jkn_252 jkn_253 jkn_254 jkn_255 jkn_256 jkn_257 jkn_258 jkn_259 jkn_260 jkn_261 jkn_262 jkn_263 jkn_264 jkn_265 jkn_266 jkn_267 jkn_268 jkn_269 jkn_270 jkn_271 jkn_272 jkn_273 jkn_274 jkn_275 jkn_276 jkn_277 jkn_278 jkn_279 jkn_280 jkn_281 jkn_282 jkn_283 jkn_284 jkn_285 jkn_286 jkn_287 jkn_288 jkn_289 jkn_290 jkn_291 jkn_292 jkn_293 jkn_294 jkn_295 jkn_296 jkn_297 jkn_298 jkn_299 jkn_300 jkn_301 jkn_302 jkn_303 jkn_304 jkn_305 jkn_306 jkn_307 jkn_308 jkn_309 jkn_310 jkn_311 jkn_312 . save ex3reps,replace file ex3reps.dta saved . . /*----------------------------------------------------------- > recalculate smoker variable, as above > -----------------------------------------------------------*/ . recode cigst1 (-9 -8 -6 =.) (-1 1 2 3=0) (4=1),gen(smoker) (9047 differences between cigst1 and smoker) . . /*----------------------------------------------- > now use the command to get the mean and design effect > for smokers using a jacknife method > ------------------------------------------------------*/ . svrmean smoker Survey mean estimation, replication (jkn) variance method Analysis weight: weighta Number of obs = 9014 Replicate weights: jkn_1... Population size = 8964.0037 Number of replicates: 312 Degrees of freedom = 158 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. [95% Conf. Interval] Deff ---------+-------------------------------------------------------------------- smoker | .3331194 .0059549 .321358 .3448808 1.438681 ------------------------------------------------------------------------------ . . . . . end of do-file . log close log: C:\Documents and Settings\gillian raab\My Documents\aprojects\peas\ex3datafiles\program_code\ex3.log log type: text closed on: 28 Aug 2005, 14:07:00 ------------------------------------------------------------------------------------------------------------------