Clarify July 2005

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Fw: Need coding for generating a regression model stratified by a dichotomousindvar. using the qsim command

by Gary King

-----Original Message----- From: Suzy <scott_788(a)wowway.com> Date: Fri, 15 Jul 2005 12:18:23 To:king@harvard.edu Subject: Need coding for generating a regression model stratified by a dichotomous indvar. using the qsim command Dear Sir, I am using Clarify for the first time and trying to create 2 separate regression models, stratified by the newrace2mis (0,1) variable. Based on the documentation, it looks like the following commands should have worked (see below), but they do not, as you can see. I'm certain that I've misunderstood the instructions. Could you please e-mail me and provide information/coding on how to obtain a stratified regression model. Thank you very much for your help in this matter. Best regards, Suzy tab newrace2mis newrace2mis | Freq. Percent Cum. ------------+----------------------------------- 0 | 135 11.79 11.79 1 | 1,010 88.21 100.00 ------------+----------------------------------- Total | 1,145 100.00 qsim, c("regr ln_pbb i.c_n_demo i.age_cat build_yr ") setx2 (newrace2mis 0) i.c_n_demo _Ic_n_demo_0-2 (naturally coded; _Ic_n_demo_0 omitted) i.age_cat _Iage_cat_0-2 (naturally coded; _Iage_cat_0 omitted) Source | SS df MS Number of obs = 1196 -------------+------------------------------ F( 5, 1190) = 10.69 Model | 23.5701727 5 4.71403453 Prob > F = 0.0000 Residual | 524.617798 1190 .440855292 R-squared = 0.0430 -------------+------------------------------ Adj R-squared = 0.0390 Total | 548.18797 1195 .458734703 Root MSE = .66397 ------------------------------------------------------------------------------ ln_pbb | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Ic_n_demo_1 | .0687954 .0459445 1.50 0.135 -.0213459 .1589366 _Ic_n_demo_2 | .308582 .1073778 2.87 0.004 .097911 .5192529 _Iage_cat_1 | .3598331 .065714 5.48 0.000 .2309049 .4887613 _Iage_cat_2 | .2462407 .0643693 3.83 0.000 .1199506 .3725307 build_yr | -.0037854 .0009719 -3.89 0.000 -.0056923 -.0018785 _cons | 8.665999 1.859763 4.66 0.000 5.017219 12.31478 ------------------------------------------------------------------------------ Simulating main parameters. Please wait.... % of simulations completed: 16% 33% 50% 66% 83% 100% Simulating sigma-squared. Please wait Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 regr ln_pbb i.c_n_demo i.age_cat build_yr newrace2mis was not an explanatory variable in the last estimated model. r(198); qsim, c("regr ln_pbb i.c_n_demo i.age_cat build_yr newrace2mis ") setx2( newrace2mis 0) i.c_n_demo _Ic_n_demo_0-2 (naturally coded; _Ic_n_demo_0 omitted) i.age_cat _Iage_cat_0-2 (naturally coded; _Iage_cat_0 omitted) Source | SS df MS Number of obs = 1145 -------------+------------------------------ F( 6, 1138) = 11.71 Model | 30.8684545 6 5.14474242 Prob > F = 0.0000 Residual | 500.143081 1138 .439493041 R-squared = 0.0581 -------------+------------------------------ Adj R-squared = 0.0532 Total | 531.011536 1144 .464170923 Root MSE = .66294 ------------------------------------------------------------------------------ ln_pbb | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Ic_n_demo_1 | .0474644 .0468849 1.01 0.312 -.0445262 .139455 _Ic_n_demo_2 | .2820453 .1073858 2.63 0.009 .0713489 .4927416 _Iage_cat_1 | .3398308 .0673856 5.04 0.000 .2076169 .4720447 _Iage_cat_2 | .2208686 .0661092 3.34 0.001 .091159 .3505783 build_yr | -.0037124 .0009893 -3.75 0.000 -.0056535 -.0017713 newrace2mis | .2759708 .0609784 4.53 0.000 .1563281 .3956135 _cons | 8.317301 1.894719 4.39 0.000 4.599767 12.03484 ------------------------------------------------------------------------------ . qsim, c("regr ln_pbb i.c_n_demo i.age_cat build_yr newrace2mis ") setx2( newrace2mis 1) i.c_n_demo _Ic_n_demo_0-2 (naturally coded; _Ic_n_demo_0 omitted) i.age_cat _Iage_cat_0-2 (naturally coded; _Iage_cat_0 omitted) Source | SS df MS Number of obs = 1145 -------------+------------------------------ F( 6, 1138) = 11.71 Model | 30.8684545 6 5.14474242 Prob > F = 0.0000 Residual | 500.143081 1138 .439493041 R-squared = 0.0581 -------------+------------------------------ Adj R-squared = 0.0532 Total | 531.011536 1144 .464170923 Root MSE = .66294 ------------------------------------------------------------------------------ ln_pbb | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Ic_n_demo_1 | .0474644 .0468849 1.01 0.312 -.0445262 .139455 _Ic_n_demo_2 | .2820453 .1073858 2.63 0.009 .0713489 .4927416 _Iage_cat_1 | .3398308 .0673856 5.04 0.000 .2076169 .4720447 _Iage_cat_2 | .2208686 .0661092 3.34 0.001 .091159 .3505783 build_yr | -.0037124 .0009893 -3.75 0.000 -.0056535 -.0017713 newrace2mis | .2759708 .0609784 4.53 0.000 .1563281 .3956135 -- Clarify mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=clarify

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Clarify July 2005