Hi All

I imputed (25 imputations) my longitudinal dataset (in the wide format) which seems to have worked well. I'm now trying to reshape the dataset to the long format for my analysis but I seem to be having some issue reshaping the data. Stata doesn't recognise the 'stubs' or variables that need to be reshaped which I guess is because the imputed variables have a numerical prefix such as _5_ (?)

Do I need to rename all the imputed variables or is there another solution. Or maybe I'm misunderstanding the problem altogether:

The imputation syntax:

Code:
mi set wide

mi register imputed rutter2all rutter3all rutter4all mal5all mal7all mal9all bmi3 bmi4 bmi5 bmi7 bmi9 ///
childses adultses education34 maritalstatus42 birthwt femployment smokepreg ///
health42 illness42 lifesatis42 smoke42 employ42 partner42 home42 

mi impute chained (mlogit) childses adultses education34 maritalstatus42  ///
(logit) femployment smokepreg smoke42 health42 illness42 employ42 partner42 home42 ///
(regress) birthwt rutter2all rutter3all rutter4all mal5all mal7all mal9all bmi3 bmi4 bmi5 bmi7 bmi9 lifesatis42 ///
= sexatbirth, add(25) rseed (53421) force augment dots
The reshape command:

Code:
mi reshape long rutter2all rutter3all rutter4all mal5all mal7all mal9all bmi3 bmi4 bmi5 bmi7 bmi9 childses education34 maritalstatus42, i(bcsid) j(j)
A snapshot of the dataset:

id cohort rutter21 rutter22 rutter23 rutter24 rutter25 rutter2all rutter31 rutter32 rutter33 rutter34 rutter35 rutter3all rutter41 rutter42 rutter43 rutter44 rutter45 rutter4all mal51 mal52 mal53 mal54 mal55 mal56 mal57 mal58 mal59 mal5all mal71 mal72 mal73 mal74 mal75 mal76 mal77 mal78 mal79 mal7all mal91 mal92 mal93 mal94 mal95 mal96 mal97 mal98 mal99 mal9all sex3 age3 bmi3 sex4 age4 bmi4 sex5 age5 bmi5 sex7 age7 bmi7 sex9 age9 bmi9 childses adultses education34 maritalstatus42 sexatbirth fscbirth _mi_miss _1_rutter2all _2_rutter2all _3_rutter2all _4_rutter2all _5_rutter2all _6_rutter2all _7_rutter2all _8_rutter2all _9_rutter2all _10_rutter2all _11_rutter2all _12_rutter2all _13_rutter2all _14_rutter2all _15_rutter2all _16_rutter2all _17_rutter2all _18_rutter2all _19_rutter2all _20_rutter2all _21_rutter2all _22_rutter2all _23_rutter2all _24_rutter2all _25_rutter2all _1_rutter3all _2_rutter3all _3_rutter3all _4_rutter3all _5_rutter3all _6_rutter3all _7_rutter3all _8_rutter3all _9_rutter3all _10_rutter3all _11_rutter3all _12_rutter3all _13_rutter3all _14_rutter3all _15_rutter3all _16_rutter3all _17_rutter3all _18_rutter3all _19_rutter3all _20_rutter3all _21_rutter3all _22_rutter3all _23_rutter3all _24_rutter3all _25_rutter3all _1_rutter4all _2_rutter4all _3_rutter4all _4_rutter4all _5_rutter4all _6_rutter4all _7_rutter4all _8_rutter4all _9_rutter4all _10_rutter4all _11_rutter4all _12_rutter4all _13_rutter4all _14_rutter4all _15_rutter4all _16_rutter4all _17_rutter4all _18_rutter4all _19_rutter4all _20_rutter4all _21_rutter4all _22_rutter4all _23_rutter4all _24_rutter4all _25_rutter4all _1_mal5all _2_mal5all _3_mal5all _4_mal5all _5_mal5all _6_mal5all _7_mal5all _8_mal5all _9_mal5all _10_mal5all _11_mal5all _12_mal5all _13_mal5all _14_mal5all _15_mal5all _16_mal5all _17_mal5all _18_mal5all _19_mal5all _20_mal5all _21_mal5all _22_mal5all _23_mal5all _24_mal5all _25_mal5all _1_mal7all _2_mal7all _3_mal7all _4_mal7all _5_mal7all _6_mal7all _7_mal7all _8_mal7all _9_mal7all _10_mal7all _11_mal7all _12_mal7all _13_mal7all _14_mal7all _15_mal7all _16_mal7all _17_mal7all _18_mal7all _19_mal7all _20_mal7all _21_mal7all _22_mal7all _23_mal7all _24_mal7all _25_mal7all _1_mal9all _2_mal9all _3_mal9all _4_mal9all _5_mal9all _6_mal9all _7_mal9all _8_mal9all _9_mal9all _10_mal9all _11_mal9all _12_mal9all _13_mal9all _14_mal9all _15_mal9all _16_mal9all _17_mal9all _18_mal9all _19_mal9all _20_mal9all _21_mal9all _22_mal9all _23_mal9all _24_mal9all _25_mal9all _1_bmi3 _2_bmi3 _3_bmi3 _4_bmi3 _5_bmi3 _6_bmi3 _7_bmi3 _8_bmi3 _9_bmi3 _10_bmi3 _11_bmi3 _12_bmi3 _13_bmi3 _14_bmi3 _15_bmi3 _16_bmi3 _17_bmi3 _18_bmi3 _19_bmi3 _20_bmi3 _21_bmi3 _22_bmi3 _23_bmi3 _24_bmi3 _25_bmi3 _1_bmi4 _2_bmi4 _3_bmi4 _4_bmi4 _5_bmi4 _6_bmi4 _7_bmi4 _8_bmi4 _9_bmi4 _10_bmi4 _11_bmi4 _12_bmi4 _13_bmi4 _14_bmi4 _15_bmi4 _16_bmi4 _17_bmi4 _18_bmi4 _19_bmi4 _20_bmi4 _21_bmi4 _22_bmi4 _23_bmi4 _24_bmi4 _25_bmi4 _1_bmi5 _2_bmi5 _3_bmi5 _4_bmi5 _5_bmi5 _6_bmi5 _7_bmi5 _8_bmi5 _9_bmi5 _10_bmi5 _11_bmi5 _12_bmi5 _13_bmi5 _14_bmi5 _15_bmi5 _16_bmi5 _17_bmi5 _18_bmi5 _19_bmi5 _20_bmi5 _21_bmi5 _22_bmi5 _23_bmi5 _24_bmi5 _25_bmi5
1 2 0 2 0 0 0 2 2 0 0 1 0 3 No No Yes No No No No No No 1 No No Yes No No No No No No 1 No No No No No No No No No 0 Female 10.22 16.25 Female 16.52 23.24 Female 26 21.58 Female 34.44 22.67 Female 42.61 23.24 Managerial/Technical nvq4 lev married Female 3.1 1 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 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2.072433 .8163094 5.273491 4.545129 5.783971 .1306898 .9832175 1.814043 1.515714 1.403755 4.709615 -.6260981 3.703815 2.011278 1.798827 7.470809 2.485234 2.469866 1.935715 -.2343003 3.266917 3.046252 -.0290871 3.955323 3.437697 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 16.25 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 23.24 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58 21.58
2 2 0 1 0 1 0 2 0 1 0 0 0 1 Male 10.34 15.89 Male Male Male Male 3.2 Male Partly skilled/Unskilled 1 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2.153539 1.245701 4.849857 -1.273342 .6035499 2.405425 -.3142972 3.021267 3.243126 1.420298 .4188714 .492611 2.322208 1.495101 3.902206 1.411599 2.296456 2.340232 -.8609622 2.753034 1.330311 .0409159 -.1798425 4.862536 4.717918 4.091299 .3138668 .8088912 2.977851 1.467018 2.075816 1.614034 1.860016 4.702971 2.053424 1.189264 -.972762 1.261846 -.4229388 2.474241 4.602363 2.074609 5.248667 -1.5724 1.957378 2.782016 1.494212 4.3235 .9378836 5.259079 5.342359 1.577507 1.449277 3.896977 2.031081 4.185738 .0263815 .0111119 1.261825 3.970456 4.654943 -.8793136 1.734049 1.049268 2.08578 2.063489 3.451377 3.795507 -1.844642 2.240778 .13521 2.611025 1.013698 1.072508 2.811083 3.639225 1.638335 .9664008 1.521721 1.004483 2.761574 .1242575 .6184756 1.630188 1.877767 3.969803 -.2759738 .7066768 2.785779 .4112329 3.205324 1.648427 4.030689 .2071863 1.653786 -.6097738 2.293722 2.833877 .3376981 3.850429 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 15.89 24.006582 18.03845 20.318122 20.894677 23.84487 17.627483 19.994651 18.232087 19.324769 23.108349 21.493701 21.505528 18.215168 17.889558 19.662741 22.999492 17.17509 21.883783 21.716616 20.26136 21.373189 21.945217 22.630743 20.422002 22.378265 23.331376 23.053748 21.768432 19.080222 29.347021 26.090552 21.652967 23.837322 17.475772 24.81869 27.999145 21.764405 21.309584 24.135544 23.077336 29.110069 24.010468 27.04915 29.656375 29.029405 21.330121 23.586867 25.28558 24.416991 24.145665
3 2 0 1 2 1 0 4 0 0 2 0 0 2 0 1 0 0 1 2 Yes No No No No No No No No 1 Yes No No No No No No No No 1 Yes No No No No Yes No No No 2 Male 9.99 17.07 Male Male 26 Male 34.27 23.09 Male 42.19 24.01 3.2 3.2 nvq2 lev married Male 3.2 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 17.07 18.584944 20.314734 19.074343 17.689871 18.969511 24.9365 22.645439 18.835999 23.075619 20.097796 18.890739 21.716477 22.549255 18.855319 20.270808 21.906391 18.875012 21.007545 22.55765 18.231376 20.636669 18.069695 21.820067 19.545134 24.008081 22.209444 19.491367 21.496576 25.411514 21.194573 24.001867 20.815928 19.741698 25.54198 24.231039 22.567579 22.37027 20.014333 21.592882 22.506563 25.067076 26.001577 20.534612 22.731633 21.657021 22.220693 18.682335 20.945533 22.455062 24.058596
4 2 0 1 1 0 1 3 0 2 1 0 0 3 2 1 2 0 2 7 No No Yes No No No No No No 1 No No Yes No Yes Yes No Yes Yes 5 Female 10.43 15.47 Female 16.3 18.77 Female 26 20.6 Female 34.61 22.67 Female 42.36 22.32 Partly skilled/Unskilled nvq4 lev married Female Managerial/Technical 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1.836787 4.031497 3.419526 .265069 6.41191 2.347236 2.339737 3.069198 3.190099 4.492092 2.770283 3.425983 4.092963 2.800628 4.68963 3.39283 3.783581 3.627538 2.113401 3.158496 .133341 2.535626 5.73802 .9533454 4.124063 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 15.47 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 18.77 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6
5 2 1 1 0 0 0 2 2 2 2 0 0 6 1 0 1 0 0 2 No No Yes No No No No No No 1 No No No No No No No No No 0 Male 10.26 15.01 Male Male Male 34.03 21.8 Male 43.03 20.77 3.1 3.1 nvq2 lev married Male Managerial/Technical 1 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 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 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 1.078648 -.7509429 .4147037 1.663399 1.863124 -.3136207 .811648 2.07656 2.179804 2.548537 1.506171 -.2922465 -.6022886 .7769023 -.0055012 -.8815057 .3652946 1.345548 -1.369497 .094575 .560407 1.557777 -1.144994 .3373731 1.750165 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 15.01 20.756018 16.550529 18.485043 19.993011 17.642679 19.833878 17.832939 17.802739 18.601316 16.084242 17.775667 16.077027 15.979541 18.915844 18.481137 16.227312 21.244811 17.983683 20.624551 16.104158 17.600846 19.667399 17.593515 21.502933 18.746199 22.369411 21.38929 22.959844 21.644269 24.210994 23.702372 24.664978 23.079244 22.08847 19.40981 21.131286 17.285259 20.310576 24.945052 21.416091 19.562742 14.96406 21.551755 23.293054 18.770077 18.671076 21.064539 19.949017 21.285538 21.097869



The other problem could be that the dataset also includes non-imputed variables, which I haven't included in the syntax for reshape (I assume it's only the imputed variables that need to be mentioned in the reshape syntax).

Any help would be much appreciated!

Thanks
/Amal