GLM:
glm total_cost_2015 pcp_vs_er_index spine_surgery mental sex_rc age neck chronic_pain smoking obesity i.lumbar_surgery_history i.cervical_surgery_history i.sud ///
median_incom elixhauser_score i.married_rc i.latino i.urban i.hsvscoll ///
i.high_dp_rc i.muscle_relaxants i.nsaids i.opioid i.oral_steroid m_total_visit [pweight=_webal] if insur == 0 & education < 5 & high_dp_rc <2 ///
& married_rc <2 & latino < 2 & urban < 2, link(log) family(gamma)
Pred means:
adjust, by(pcp_vs_er_index) exp ci
ERROR: 0b: operator invalid
When I don't include the covariates and run the glm with the weights, I get the predicted costs just fine but not when I run the glm with the covariates. Below are partial variables limited by the number of accepted variables by dataex.
I am looking for help on the ERROR on getting the predicted mean costs. Thanks for your help. Please let me know if more information is needed. I consulted Stata help but am still stymied.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(total_cost_2015 pcp_vs_er_index spine_surgery mental sex_rc age neck chronic_pain smoking obesity lumbar_surgery_history cervical_surgery_history sud) long median_income float elixhauser_score 1759.808 1 0 1 0 63 0 0 1 0 0 0 1 52517 5 2053.89 1 0 0 1 29 0 0 0 0 0 0 0 63910 0 76.44 0 0 0 0 49 1 0 0 0 0 0 0 67037 0 0 1 0 0 1 41 0 0 0 0 0 0 0 53324 0 447.5552 0 0 1 0 44 0 1 0 0 0 0 0 60172 1 0 1 0 0 1 64 0 0 0 0 0 0 0 45605 1 1438.041 1 0 0 1 63 0 0 0 0 0 0 0 63155 0 0 0 0 0 0 55 1 0 0 0 0 0 0 41336 2 2090.4058 0 0 0 0 50 0 0 0 0 0 0 0 56883 1 5298.303 1 0 0 0 53 1 0 0 0 0 0 0 96005 0 0 1 0 0 0 41 1 0 0 0 0 0 0 59393 0 3474.8455 1 0 0 1 30 0 0 0 0 0 0 0 79000 0 0 1 0 0 0 50 0 0 1 0 0 0 1 35859 2 6101.723 1 1 0 1 58 0 0 0 0 0 0 0 58915 0 220.09624 1 0 0 0 50 0 0 0 0 0 0 0 34350 1 42.63 1 0 0 1 47 0 0 0 0 0 0 0 61393 0 856.7705 0 0 0 0 23 0 0 0 0 0 0 0 44668 0 0 1 0 0 1 46 0 0 0 0 0 0 0 70294 0 12.09991 1 0 1 0 33 0 0 0 0 0 0 0 39460 1 160.26825 1 0 0 0 21 0 0 0 0 0 0 0 28837 1 0 0 0 1 1 18 0 0 1 0 0 0 1 58594 0 0 1 0 0 0 54 1 0 0 0 0 0 0 74133 1 173.51897 1 0 0 1 62 0 1 0 0 0 0 0 66019 1 647.1835 1 0 1 0 52 0 0 1 0 0 0 1 66941 1 0 1 0 0 1 37 0 0 0 0 0 0 0 69194 0 0 1 0 1 1 37 0 0 0 0 0 0 0 91660 0 3.636033 1 0 0 1 61 0 0 0 0 0 0 0 48348 0 0 1 0 0 0 61 0 0 1 0 0 0 1 44536 1 0 1 0 1 0 47 0 0 0 0 0 0 0 60671 0 0 1 0 1 0 50 0 0 0 0 0 0 0 26305 3 0 1 0 0 0 30 0 0 0 0 0 0 0 67564 0 0 1 0 1 1 44 0 0 0 0 0 0 0 45909 1 0 0 0 1 0 51 0 1 1 0 0 0 1 23618 6 727.45 1 0 0 0 29 0 0 0 0 0 0 0 46624 0 53.26788 1 0 0 0 21 0 0 0 0 0 0 0 64293 0 0 1 0 1 0 47 0 0 0 0 0 0 0 38779 2 0 1 0 1 1 38 0 0 0 0 0 0 0 32444 2 280.10883 1 0 1 0 59 1 1 0 0 0 0 0 46624 4 0 1 0 0 1 46 0 0 0 0 0 0 0 61324 0 0 1 0 1 0 22 0 0 0 0 0 0 0 49493 0 1661.5273 1 0 1 0 59 0 0 0 0 0 0 0 76442 2 304.65915 1 0 0 0 23 0 0 0 0 0 0 0 64828 0 82.95205 1 0 1 0 33 0 0 0 1 0 0 0 71736 3 410.8057 1 0 1 0 38 0 0 0 0 0 0 1 66941 2 242.9 0 0 0 0 26 0 0 1 0 0 0 1 43635 0 3020.458 1 0 0 0 55 1 0 0 0 1 1 0 63077 0 0 1 0 1 0 47 1 0 0 0 0 0 0 42981 1 0 0 0 0 0 39 0 0 0 0 0 0 0 71048 0 691.6946 0 0 0 1 38 0 1 0 0 0 0 0 35966 0 15.10061 1 0 1 0 44 1 0 0 0 0 0 0 40040 0 0 0 0 0 0 31 1 0 0 0 0 0 0 58482 0 0 0 0 1 0 30 0 0 0 0 0 0 0 59390 1 105.47048 0 0 0 1 59 0 1 0 0 0 0 0 49479 2 0 1 0 0 0 41 0 0 0 0 0 0 0 67564 1 0 1 0 1 1 35 0 1 0 1 0 0 1 64583 5 0 1 0 0 0 54 0 0 1 0 0 0 1 43241 3 92.43604 1 0 0 1 43 0 0 0 0 0 0 0 43024 0 180.71083 1 0 1 0 38 1 0 0 0 0 0 0 70240 1 0 1 0 0 0 55 0 0 0 0 0 0 0 45679 1 0 1 0 0 1 32 1 0 0 0 0 0 0 53524 0 0 0 0 0 1 63 0 1 0 0 0 0 1 44797 5 2275.39 1 1 1 0 43 0 0 0 0 0 0 0 63345 1 0 1 0 0 1 18 0 0 0 0 0 0 0 91071 0 0 1 0 0 0 57 0 0 0 0 0 0 0 64069 1 0 1 0 0 0 33 0 0 0 0 0 0 0 51364 0 0 1 0 0 1 36 0 0 0 0 0 0 0 81103 0 110.09 1 0 0 0 24 0 0 1 0 0 0 0 31667 1 832.7615 1 0 1 0 58 0 0 0 0 0 0 0 64792 1 3780.7544 0 0 0 0 47 0 0 0 0 0 0 0 62768 0 508.4539 1 0 0 0 56 1 0 0 0 0 0 0 108294 0 128.44 1 0 0 1 44 0 0 0 0 0 0 0 41087 0 0 0 0 1 1 58 0 0 0 0 0 0 0 74178 1 86.07298 1 0 0 0 23 0 0 0 0 0 0 0 48821 0 0 1 0 0 1 63 1 0 0 0 0 0 0 97500 1 278.7536 1 0 1 1 27 0 0 0 0 0 0 0 53478 1 944.8231 1 0 0 1 24 0 0 0 0 0 0 0 46017 0 258.98654 1 0 0 0 62 0 0 0 0 0 0 0 53324 2 0 1 0 1 0 57 0 0 0 0 0 0 0 68257 3 0 0 0 1 1 38 1 0 1 0 0 0 1 33782 2 0 1 0 0 1 47 0 0 0 0 0 0 0 83260 1 128.13986 1 0 0 1 50 1 0 0 0 0 0 0 59393 0 0 1 0 0 1 48 1 0 0 0 0 0 0 48455 0 128.72566 1 0 0 1 41 1 0 0 0 0 0 0 55652 0 240.3777 1 0 0 0 41 0 1 0 0 0 0 0 80795 0 0 1 0 0 0 26 0 0 0 0 0 0 0 90341 0 0 0 0 0 0 42 0 0 0 0 0 0 0 71402 0 0 1 0 0 0 49 0 0 0 0 0 0 0 42891 0 0 1 0 1 0 47 0 0 0 0 0 0 0 54368 1 0 1 0 0 1 18 0 0 0 0 0 0 0 90208 0 153.784 1 0 0 0 58 0 0 0 0 0 0 0 53524 5 0 1 0 0 0 61 0 0 0 0 0 0 0 51682 2 0 1 0 0 0 58 1 0 0 1 0 0 0 42981 7 1432.617 1 0 0 1 40 1 0 0 0 0 0 0 43175 0 2452.15 1 0 1 0 29 0 0 0 0 0 0 0 71048 1 0 1 0 0 1 19 0 0 0 0 0 0 0 95106 0 3409.565 1 0 0 0 58 0 0 0 1 0 0 0 50327 2 4386.179 1 0 0 1 32 1 0 0 0 0 0 0 63910 0 0 0 0 0 1 24 0 0 1 0 0 0 1 57400 0 33.596954 1 0 0 0 57 1 0 0 0 0 0 0 80795 0 376.05 1 0 0 1 55 0 0 1 0 0 0 1 32353 0 end label values sex_rc sexlab label def sexlab 0 "Female", modify label def sexlab 1 "Male", modify
0 Response to error with GLM modeling costs and obtaining predicted means.
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