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  • #1

    Combination of dummy variables and return mean of the dummies

    Dear all,

    I have posted a similar topic but information I provided was not sufficient to get the answer I wanted (which is my fault).

    Code:
    clear
input symptomA symptomB symptomC symptomD symptomE symptomF symptomG QOL
0 0 0 0 0 1 0 62
0 0 0 0 0 0 1 93
0 0 0 0 0 0 1 87
0 0 1 0 0 0 1 82
0 0 0 0 0 0 1 88
0 0 0 0 0 0 0 99
0 0 0 1 1 0 0 64
0 0 0 0 0 0 1 78
0 0 0 0 1 1 0 69
0 0 0 0 0 0 1 77
0 0 0 0 0 1 1 93
0 0 0 0 0 0 1 81
0 0 0 0 0 0 1 61
0 0 0 0 0 1 1 100
0 0 0 0 0 0 0 79
0 0 0 0 0 0 1 41
0 0 0 0 0 1 1 80
1 0 0 0 0 1 1 74
0 0 0 0 0 0 1 52
0 0 0 0 0 0 1 89
0 0 0 0 0 0 0 81
0 0 0 0 0 0 0 90
0 0 0 0 0 0 0 95
0 1 0 0 0 1 1 28
0 0 0 0 0 0 0 85
0 0 0 0 0 0 0 96
0 0 0 0 0 0 0 48
0 0 0 0 0 0 1 61
0 0 0 0 0 0 1 96
0 0 0 0 0 0 1 90
0 0 0 0 0 1 1 62
0 0 0 0 0 1 0 72
0 0 0 0 0 0 0 95
0 0 0 0 0 0 1 59
0 0 0 0 1 1 0 79
0 0 0 0 0 1 0 93
0 0 0 0 0 0 1 92
0 0 0 0 0 0 0 54
0 0 0 0 0 0 1 96
0 0 0 0 0 0 0 81
end
    I want to return mean of QOL (quality of life) for someone with dual symptoms. For example, If someone has symptomA and symptomB;that is, symtptomA=1 and symptomB=1, I want to get the mean of QOL for the observations all having symptomA and symptomB. Likewise, I need to return all the possible combinations of 7C2 of symptoms and their mean, showing what symptoms they have (because I need to check what kinds of symptoms generate lower QOL).

    so the result will be like

    symptomA & symptomB mean QOL: xx
    ...

    symptomF& symptomG mean QOL: xx



    afterwards, I want to get mean QOL of 7C3 as well.

    Any help will be appreciated. Thank you!

    BW
    Kim




    Tags: None
  • #2
    Sungwook:
    do you mean something along the following lines?
    Code:
    . egen wanted=group( symptomA- symptomG)

. bysort wanted: egen mean_QoL=mean(QOL)

. list

     +------------------------------------------------------------------------------------------------------+
     | symptomA   symptomB   symptomC   symptomD   symptomE   symptomF   symptomG   QOL   wanted   mean_QoL |
     |------------------------------------------------------------------------------------------------------|
  1. |        0          0          0          0          0          0          0    54        1   82.09091 |
  2. |        0          0          0          0          0          0          0    81        1   82.09091 |
  3. |        0          0          0          0          0          0          0    96        1   82.09091 |
  4. |        0          0          0          0          0          0          0    99        1   82.09091 |
  5. |        0          0          0          0          0          0          0    81        1   82.09091 |
     |------------------------------------------------------------------------------------------------------|
  6. |        0          0          0          0          0          0          0    85        1   82.09091 |
  7. |        0          0          0          0          0          0          0    90        1   82.09091 |
  8. |        0          0          0          0          0          0          0    95        1   82.09091 |
  9. |        0          0          0          0          0          0          0    95        1   82.09091 |
 10. |        0          0          0          0          0          0          0    79        1   82.09091 |
     |------------------------------------------------------------------------------------------------------|
 11. |        0          0          0          0          0          0          0    48        1   82.09091 |
 12. |        0          0          0          0          0          0          1    61        2    77.5625 |
 13. |        0          0          0          0          0          0          1    89        2    77.5625 |
 14. |        0          0          0          0          0          0          1    96        2    77.5625 |
 15. |        0          0          0          0          0          0          1    41        2    77.5625 |
     |------------------------------------------------------------------------------------------------------|
 16. |        0          0          0          0          0          0          1    87        2    77.5625 |
 17. |        0          0          0          0          0          0          1    77        2    77.5625 |
 18. |        0          0          0          0          0          0          1    88        2    77.5625 |
 19. |        0          0          0          0          0          0          1    93        2    77.5625 |
 20. |        0          0          0          0          0          0          1    61        2    77.5625 |
     |------------------------------------------------------------------------------------------------------|
 21. |        0          0          0          0          0          0          1    81        2    77.5625 |
 22. |        0          0          0          0          0          0          1    78        2    77.5625 |
 23. |        0          0          0          0          0          0          1    59        2    77.5625 |
 24. |        0          0          0          0          0          0          1    96        2    77.5625 |
 25. |        0          0          0          0          0          0          1    52        2    77.5625 |
     |------------------------------------------------------------------------------------------------------|
 26. |        0          0          0          0          0          0          1    90        2    77.5625 |
 27. |        0          0          0          0          0          0          1    92        2    77.5625 |
 28. |        0          0          0          0          0          1          0    62        3   75.66666 |
 29. |        0          0          0          0          0          1          0    93        3   75.66666 |
 30. |        0          0          0          0          0          1          0    72        3   75.66666 |
     |------------------------------------------------------------------------------------------------------|
 31. |        0          0          0          0          0          1          1    93        4      83.75 |
 32. |        0          0          0          0          0          1          1    80        4      83.75 |
 33. |        0          0          0          0          0          1          1    62        4      83.75 |
 34. |        0          0          0          0          0          1          1   100        4      83.75 |
 35. |        0          0          0          0          1          1          0    79        5         74 |
     |------------------------------------------------------------------------------------------------------|
 36. |        0          0          0          0          1          1          0    69        5         74 |
 37. |        0          0          0          1          1          0          0    64        6         64 |
 38. |        0          0          1          0          0          0          1    82        7         82 |
 39. |        0          1          0          0          0          1          1    28        8         28 |
 40. |        1          0          0          0          0          1          1    74        9         74 |
     +------------------------------------------------------------------------------------------------------+

.
    Kind regards,
    Carlo
    (StataNow 18.5)

    Comment

    • #3
      Hello Carlo,

      Thanks for your reply. It is similar but I want to display which combination shows which QoL - so for mean QoL of 83.75 from line 31 to line 34, I want to display "symptom F & G" together. Likewise, from line 35 to 36, I want to display "symptom E & F".

      Anyway, thank you very much for your help!
      BW
      Kim

      Comment

      • #4
        Please consider this token code and result:

        Code:
        clear
input symptomA symptomB symptomC symptomD symptomE symptomF symptomG QOL
0 0 0 0 0 1 0 62
0 0 0 0 0 0 1 93
0 0 0 0 0 0 1 87
0 0 1 0 0 0 1 82
0 0 0 0 0 0 1 88
0 0 0 0 0 0 0 99
0 0 0 1 1 0 0 64
0 0 0 0 0 0 1 78
0 0 0 0 1 1 0 69
0 0 0 0 0 0 1 77
0 0 0 0 0 1 1 93
0 0 0 0 0 0 1 81
0 0 0 0 0 0 1 61
0 0 0 0 0 1 1 100
0 0 0 0 0 0 0 79
0 0 0 0 0 0 1 41
0 0 0 0 0 1 1 80
1 0 0 0 0 1 1 74
0 0 0 0 0 0 1 52
0 0 0 0 0 0 1 89
0 0 0 0 0 0 0 81
0 0 0 0 0 0 0 90
0 0 0 0 0 0 0 95
0 1 0 0 0 1 1 28
0 0 0 0 0 0 0 85
0 0 0 0 0 0 0 96
0 0 0 0 0 0 0 48
0 0 0 0 0 0 1 61
0 0 0 0 0 0 1 96
0 0 0 0 0 0 1 90
0 0 0 0 0 1 1 62
0 0 0 0 0 1 0 72
0 0 0 0 0 0 0 95
0 0 0 0 0 0 1 59
0 0 0 0 1 1 0 79
0 0 0 0 0 1 0 93
0 0 0 0 0 0 1 92
0 0 0 0 0 0 0 54
0 0 0 0 0 0 1 96
0 0 0 0 0 0 0 81
end

gen which = ""

foreach s in A B C D E F G {
    replace which = which + "`s'" if symptom`s' == 1
}

replace which = "none" if which == ""

tab which, su(QOL)


            |           Summary of QOL
      which |        Mean   Std. dev.       Freq.
------------+------------------------------------
        AFG |          74           0           1
        BFG |          28           0           1
         CG |          82           0           1
         DE |          64           0           1
         EF |          74   7.0710678           2
          F |   75.666667   15.821926           3
         FG |       83.75   16.700798           4
          G |     77.5625   17.331931          16
       none |   82.090909   16.872786          11
------------+------------------------------------
      Total |       77.55   17.414921          40
        The number of symptoms is the row total of your indicators.

        See also upsetplot and vennbar as discussed in

        SJ-24-2 gr0095 . . . The joy of sets: Graphical alt. to Euler & Venn diagrams
        . . . . . . . . . . . . . . . . . . . . . . N. J. Cox and T. P. Morris
        (help upsetplot, vennbar, sortmean if installed)
        Q2/24 SJ 24(2):329--361
        introduces graphical alternatives for Euler or Venn diagrams
        mapped to bar or dot charts



        Last edited by Nick Cox; 10 Oct 2024, 07:54.

        Comment

        • #5
          Code:
          clear
input symptomA symptomB symptomC symptomD symptomE symptomF symptomG QOL
0 0 0 0 0 1 0 62
0 0 0 0 0 0 1 93
0 0 0 0 0 0 1 87
0 0 1 0 0 0 1 82
0 0 0 0 0 0 1 88
0 0 0 0 0 0 0 99
0 0 0 1 1 0 0 64
0 0 0 0 0 0 1 78
0 0 0 0 1 1 0 69
0 0 0 0 0 0 1 77
0 0 0 0 0 1 1 93
0 0 0 0 0 0 1 81
0 0 0 0 0 0 1 61
0 0 0 0 0 1 1 100
0 0 0 0 0 0 0 79
0 0 0 0 0 0 1 41
0 0 0 0 0 1 1 80
1 0 0 0 0 1 1 74
0 0 0 0 0 0 1 52
0 0 0 0 0 0 1 89
0 0 0 0 0 0 0 81
0 0 0 0 0 0 0 90
0 0 0 0 0 0 0 95
0 1 0 0 0 1 1 28
0 0 0 0 0 0 0 85
0 0 0 0 0 0 0 96
0 0 0 0 0 0 0 48
0 0 0 0 0 0 1 61
0 0 0 0 0 0 1 96
0 0 0 0 0 0 1 90
0 0 0 0 0 1 1 62
0 0 0 0 0 1 0 72
0 0 0 0 0 0 0 95
0 0 0 0 0 0 1 59
0 0 0 0 1 1 0 79
0 0 0 0 0 1 0 93
0 0 0 0 0 0 1 92
0 0 0 0 0 0 0 54
0 0 0 0 0 0 1 96
0 0 0 0 0 0 0 81
end

// who has two symptoms?
egen nsympt = rowtotal(symptom?)
gen byte touse = (nsympt == 2)

// combinations (with labels)
local sympt "A B C D E F G"
foreach s of local sympt {
    label define `s' 1 "`s'"
    label value symptom`s' `s'
}

egen gr = group(symptom?) if touse, label

// display the means
table (gr) , stat(freq) stat(mean QOL) nototal
          ---------------------------------
          Maarten L. Buis
          University of Konstanz
          Department of history and sociology
          box 40
          78457 Konstanz
          Germany
          http://www.maartenbuis.nl
          ---------------------------------
          • 1 like

          Comment

          • #6
            Dear Nick
            Thanks for your reply. It works perfectly! RE combination, this is a subset of my original data. The data contains 3 or 4 symptoms with over 1000 obs.

            Thank you again!

            BW
            Kim

            Comment

            • #7
              Dear Maarten,

              thanks for your reply. Your solution is great and works perfectly!

              BW
              kim

              Comment

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