I am dealing with DHS dataset. I have individual sample weight (6 decimals) in my dataset. I have divide it to 1000000. I named it as"wgt"
I try to produce sum stats. When I do this, I use sum x1 x2 x3 .... [aw=wgt]. No problem with that. It gives mt stats.
However when I try to export it to MS word, I use asdoc sum x1 x2....[aw=wgt] it says "aweights" are not allowed. As far as I know, when reporting descriptive stats, you need to use aweights. But, I dont understand why I could not export them
This code works so well: sum bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5 [aw=wgt]
But this does not: asdoc sum bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5 [aw=wgt]
Thank you so much.
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5) double wgt 30 2018 1 2014 9 3.2 28 4 1 1 2.356055974960327 12 2018 1 2016 1 3.56 30 5 1 1 2.356055974960327 8 2018 0 2018 2 3.95 29 5 1 1 2.356055974960327 18 2018 0 2014 2 3.6 35 5 1 1 2.356055974960327 19 2018 1 2014 3 2.1 30 4 1 1 2.356055974960327 18 2018 0 2014 4 3.3 41 3 2 1 2.444962978363037 18 2018 1 2014 2 2.5 35 1 2 1 2.444962978363037 3 2018 1 2015 6 3.5 26 1 2 1 2.444962978363037 10 2018 1 2016 8 . 43 1 2 1 2.444962978363037 2 2018 1 2018 8 2.77 32 2 2 1 2.444962978363037 36 2018 1 2014 10 4.2 37 2 2 1 2.444962978363037 18 2018 1 2013 11 3.4 23 3 2 1 2.444962978363037 24 2018 0 2015 7 3.26 42 3 1 1 2.356055974960327 24 2018 0 2016 10 4.8 22 5 1 1 2.356055974960327 8 2018 0 2014 4 3.2 28 1 1 1 2.356055974960327 1 2018 0 2018 9 2.78 32 3 1 1 2.356055974960327 20 2018 1 2016 3 3.64 44 5 1 1 2.356055974960327 10 2018 1 2017 12 4.23 31 4 1 1 2.356055974960327 7 2018 0 2016 11 3.8 32 5 1 1 2.356055974960327 24 2018 1 2013 12 3.14 35 5 1 1 2.356055974960327 6 2018 1 2018 4 2.9 24 3 1 1 2.356055974960327 14 2018 1 2017 8 3 29 2 1 1 2.356055974960327 27 2018 0 2015 9 4.08 29 4 1 1 2.356055974960327 26 2018 0 2016 9 3.48 35 5 1 1 2.356055974960327 18 2018 1 2015 7 3.1 26 2 1 1 2.356055974960327 9 2018 1 2017 4 3 33 2 1 1 2.356055974960327 15 2018 1 2017 2 3.6 36 5 1 1 2.356055974960327 21 2018 1 2017 1 4.1 26 5 1 1 2.356055974960327 13 2018 1 2017 9 2.9 32 4 1 1 2.356055974960327 13 2018 1 2017 10 3.9 34 5 1 1 2.356055974960327 35 2018 1 2015 12 3.01 32 5 1 1 2.356055974960327 6 2018 1 2018 1 3.295 33 3 1 1 2.356055974960327 3 2018 1 2018 8 3.5 27 4 1 1 2.356055974960327 2 2018 1 2014 1 3.5 41 4 1 1 2.356055974960327 7 2018 0 2017 5 3.43 41 4 2 1 2.378074884414673 21 2018 1 2014 6 3.34 28 2 2 1 2.378074884414673 8 2018 1 2018 2 2.26 35 3 2 1 2.378074884414673 1 2018 0 2017 12 3.5 19 1 1 1 2.356055974960327 5 2018 1 2018 5 2.99 23 1 1 1 2.356055974960327 12 2018 0 2017 10 3.68 22 1 1 1 2.356055974960327 12 2018 1 2017 3 3.5 26 5 1 1 2.356055974960327 18 2018 1 2016 7 4 22 1 1 1 2.356055974960327 10 2018 1 2015 1 3.5 35 5 1 1 2.356055974960327 36 2018 0 2014 6 3.06 41 5 1 1 2.356055974960327 18 2018 0 2014 8 3.6 37 5 1 1 2.356055974960327 4 2018 1 2018 5 3.46 33 5 1 1 2.356055974960327 3 2018 1 2018 7 3.2 35 5 1 1 2.356055974960327 10 2018 1 2018 1 3.27 21 4 1 1 2.356055974960327 5 2018 0 2018 3 3.16 21 4 1 1 2.356055974960327 24 2018 0 2013 12 3.26 45 4 1 1 2.356055974960327 46 2018 1 2015 1 3 33 3 1 1 2.356055974960327 2 2018 0 2014 12 3.4 25 4 1 1 2.356055974960327 24 2018 1 2015 5 2.9 36 5 1 1 2.356055974960327 20 2018 0 2016 6 3.7 31 2 1 1 2.356055974960327 22 2018 0 2016 12 3.25 33 5 1 1 2.356055974960327 3 2018 0 2018 8 2.5 33 5 1 1 2.356055974960327 6 2018 0 2015 11 3.7 29 5 1 1 2.356055974960327 4 2018 1 2015 11 2.68 25 4 1 1 2.356055974960327 24 2018 1 2013 11 3.3 38 3 1 1 2.356055974960327 18 2018 0 2015 10 4.2 29 4 1 1 2.356055974960327 5 2018 1 2018 4 3.5 25 4 1 1 2.356055974960327 5 2018 0 2017 9 3.75 24 4 1 1 2.356055974960327 1 2018 1 2018 10 3.7 30 3 1 1 2.356055974960327 12 2018 0 2015 1 . 28 2 1 1 2.356055974960327 24 2018 0 2015 5 3.6 35 5 1 1 2.356055974960327 11 2018 1 2015 11 3.8 29 3 1 1 2.356055974960327 8 2018 1 2018 2 3.65 31 3 1 1 2.356055974960327 20 2018 0 2017 2 . 24 1 1 1 2.356055974960327 24 2018 1 2013 12 2.9 30 5 1 1 2.356055974960327 6 2018 1 2018 5 3.3 23 4 1 1 2.356055974960327 18 2018 0 2014 10 2.7 32 3 1 1 2.356055974960327 24 2018 1 2015 9 2.9 28 5 1 1 2.356055974960327 26 2018 1 2016 8 3.5 26 2 1 1 2.356055974960327 18 2018 0 2015 11 2.45 23 3 1 1 2.356055974960327 8 2018 1 2018 2 3.5 28 3 1 1 2.356055974960327 18 2018 0 2015 7 3.2 38 3 1 1 2.356055974960327 2 2018 1 2016 8 2.85 21 2 1 1 2.356055974960327 10 2018 1 2017 11 2.5 28 3 1 1 2.356055974960327 6 2018 0 2015 12 2.3 34 3 1 1 2.356055974960327 2 2018 1 2018 7 3.35 40 5 1 1 2.356055974960327 18 2018 0 2017 4 2.9 35 5 1 1 2.356055974960327 24 2018 0 2014 4 2.65 33 5 1 1 2.356055974960327 7 2018 0 2017 1 3 37 5 1 1 2.356055974960327 4 2018 0 2018 6 3.9 34 5 1 1 2.356055974960327 20 2018 1 2016 6 2.5 32 2 1 1 2.356055974960327 26 2018 0 2016 8 3.1 37 3 1 1 2.356055974960327 24 2018 0 2014 3 3.15 38 4 1 1 2.356055974960327 6 2018 0 2016 11 2.45 23 3 1 1 2.356055974960327 2 2018 0 2018 7 2.65 37 4 1 1 2.356055974960327 24 2018 1 2016 4 4.5 41 5 1 1 2.356055974960327 1 2018 0 2018 9 3.2 37 4 1 1 2.356055974960327 7 2018 0 2018 3 3.1 28 4 1 1 2.356055974960327 29 2018 0 2016 5 2.98 26 3 1 1 2.356055974960327 12 2018 0 2016 5 . 25 3 1 1 2.356055974960327 27 2018 1 2014 1 3.2 35 5 1 1 2.356055974960327 24 2018 1 2014 4 3.2 41 3 1 1 2.356055974960327 24 2018 1 2016 1 3.09 25 3 1 1 2.356055974960327 30 2018 0 2015 6 3.9 31 5 1 1 2.356055974960327 6 2018 1 2014 8 3.5 40 5 1 1 2.356055974960327 1 2018 0 2018 10 2.4 18 2 1 1 2.356055974960327 end label values wealth_index wealth_index label def wealth_index 1 "Poorest", modify label def wealth_index 2 "Poorer", modify label def wealth_index 3 "Middle", modify label def wealth_index 4 "Rich", modify label def wealth_index 5 "Richest", modify label values rural_urban rural_urban label def rural_urban 1 "Urban", modify label def rural_urban 2 "Rural", modify label values region5 region5 label def region5 1 " West", modify
0 Response to asdoc sum - does not allow analytical weights
Post a Comment