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  • #1

    Problem with the user generated command qregsel

    I am trying to use the user-generated command qregsel, but I am encountering two separate issues. The first issue is the error message: "Dependent variable never censored because of selection or selection indicator is not binary," even though the selection variable is binary. The second issue is: "Factor-variable and time-series operators not allowed." I need help using indicator variables in the list of control variables.



    qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)


    Code:
    * Example generated by -dataex-. For more info, type help dataex
clear
input float(log_avrg_cost inc_d endentulism race age_cat) byte(male education veteran) float mothered byte wealth float(smoke_now chronicdisease) byte r11dentst
 5.860786 0 0 1 3 0 5 0 1 4 0 3 1
        0 0 0 1 1 0 5 0 1 4 0 3 1
 6.478509 0 0 1 2 0 3 0 0 4 0 0 1
 7.824446 0 0 1 2 0 4 0 1 4 1 0 1
 6.398595 0 0 1 3 1 5 1 1 4 0 2 1
 8.699764 0 0 1 2 0 5 0 0 4 0 0 1
 5.525453 0 0 4 3 0 5 0 1 4 0 2 1
        0 0 0 3 3 0 1 0 . 2 0 2 0
 6.685861 0 0 3 3 0 1 0 0 1 0 2 1
 6.398595 0 0 1 2 0 1 0 1 4 0 1 1
 7.378384 0 0 1 4 1 5 1 0 4 0 2 1
        0 0 0 3 3 0 1 0 0 1 0 2 1
        0 0 1 4 3 1 1 0 0 1 0 3 0
        0 0 1 2 3 0 3 0 0 1 0 1 1
        0 0 0 2 3 1 1 0 . 1 0 0 0
 7.151485 0 0 1 3 0 4 0 1 1 0 1 1
        0 1 1 2 3 0 1 0 . 1 0 1 0
 6.216606 0 0 1 3 0 4 0 1 4 0 0 1
 5.993961 0 0 1 3 1 5 1 1 4 0 0 1
  8.03948 0 0 1 3 0 1 0 1 4 0 1 1
        0 0 0 1 3 0 3 0 1 1 0 2 0
 5.303305 0 1 4 3 0 5 0 0 4 0 2 1
 5.303305 0 0 4 3 1 5 1 0 4 0 3 0
 4.620059 0 0 1 3 0 3 0 0 1 0 0 1
        0 0 1 1 3 1 4 1 . 2 0 1 0
 7.313887 0 0 1 2 0 5 0 1 2 0 0 1
 8.881975 0 0 1 4 0 5 0 1 4 0 2 1
 5.993961 0 0 1 3 1 5 1 1 4 0 2 1
 7.824446 0 0 1 3 0 3 0 1 4 0 0 1
 7.523481 0 0 1 3 1 5 0 0 4 0 3 1
 6.803505 0 0 1 3 0 5 0 1 4 0 1 1
        0 0 0 2 3 0 1 0 0 3 0 2 1
        0 0 0 2 3 0 5 0 1 1 0 2 0
        0 0 1 2 3 0 1 0 0 4 0 1 0
        0 0 1 2 2 0 3 0 0 1 0 0 0
 6.398595 0 0 2 3 1 3 1 1 3 0 3 1
        0 0 0 2 3 0 3 0 1 3 0 0 1
 6.908755 0 0 2 3 1 3 0 0 2 . 2 0
 5.786897 0 0 3 1 0 3 0 1 4 0 0 1
 4.836282 0 0 4 3 0 1 0 0 4 0 0 1
 5.303305 0 0 4 4 1 5 0 0 4 0 0 1
 6.908755 0 0 3 3 1 4 1 1 4 0 1 1
 3.314186 0 0 1 2 0 4 0 1 4 0 0 1
  7.31422 0 0 1 2 0 4 0 0 4 0 1 1
 8.987322 0 0 2 3 0 5 0 0 1 0 4 1
        0 0 1 4 3 0 4 0 0 2 0 0 0
 4.912655 1 0 1 3 1 1 0 1 2 0 1 1
  5.86221 0 0 1 3 0 5 0 1 2 0 1 1
 5.239098 0 0 1 3 1 3 1 0 4 0 2 1
        0 0 0 2 3 0 4 0 0 3 0 3 0
 6.803505 0 0 1 4 1 5 1 1 4 0 2 1
 7.438972 0 0 3 3 0 3 0 0 3 0 1 1
        0 0 1 3 3 1 2 1 0 3 0 1 0
4.6151204 0 0 4 4 1 5 0 0 2 0 1 0
3.9318256 0 0 4 3 0 5 0 0 2 0 2 1
        0 0 0 2 3 0 1 0 0 1 1 2 0
 5.351858 0 0 2 3 0 3 0 0 1 0 1 1
        0 0 0 2 3 0 1 0 . 1 0 2 0
  6.53814 0 0 1 3 1 4 1 0 1 0 3 1
 7.313887 1 0 4 2 0 1 0 1 2 0 6 1
 8.101981 0 0 1 4 1 3 1 1 4 0 4 1
  6.29803 0 0 1 3 0 1 0 1 4 0 1 1
 6.055613 0 0 1 3 0 5 0 1 3 0 1 1
        0 0 0 1 3 0 3 0 1 2 0 2 0
  7.91972 0 0 1 4 0 4 0 0 4 0 3 1
 6.908755 0 0 1 4 1 5 1 . 4 0 1 1
 7.003974 0 0 1 4 1 4 1 1 4 0 1 1
 6.763885 0 0 1 3 0 3 0 0 4 0 4 1
        0 0 0 1 2 0 3 0 1 2 0 1 1
        0 0 0 1 3 0 2 0 0 3 0 0 1
1.7917595 0 0 3 3 0 3 0 . 1 0 3 1
        0 1 0 2 1 0 1 0 0 1 1 1 0
        0 0 0 2 1 0 4 0 0 1 1 2 0
        0 0 1 2 3 0 4 0 1 2 0 3 0
        0 0 0 1 3 0 1 0 0 1 0 2 0
 6.685861 0 0 1 3 1 4 1 1 2 0 1 1
 5.463832 0 0 1 3 1 3 0 . 3 0 2 1
        0 0 0 1 3 0 3 0 1 4 0 1 0
 4.620059 0 0 1 4 1 1 1 0 3 0 0 1
 6.634634 0 0 1 3 1 5 1 1 4 0 2 1
 5.525453 0 0 1 3 1 5 0 0 4 0 2 0
 5.860786 0 0 1 1 0 5 0 1 4 0 0 1
        0 0 1 1 3 1 2 0 0 3 0 3 0
 6.246107 0 0 3 2 0 1 0 0 3 . 2 1
        0 0 0 1 4 1 1 0 0 2 0 3 0
 5.673323 0 0 1 4 0 4 0 0 3 0 0 1
        0 0 1 2 3 0 3 0 0 1 0 3 0
        0 0 0 2 3 1 2 0 1 1 1 0 0
        0 1 1 2 3 1 3 0 1 2 1 0 0
        0 0 0 2 3 0 4 0 0 2 0 2 0
 3.433987 0 0 2 3 1 4 1 0 3 0 2 1
3.9318256 1 0 2 1 0 4 0 0 3 1 2 1
 5.170484 0 0 2 3 1 1 0 1 2 0 3 1
        0 0 0 2 2 1 3 1 0 1 . 4 1
        0 0 1 1 3 1 1 1 0 3 0 4 0
 7.601402 0 0 1 3 0 1 0 0 4 0 0 1
        0 0 1 2 3 1 1 1 . 2 0 3 0
        0 0 0 2 2 0 3 0 1 3 0 1 0
        0 0 1 1 2 0 3 0 . 2 1 2 0
        0 1 1 1 3 1 4 0 1 4 0 4 0
end
label values inc_d inc_d
label def inc_d 0 "No", modify
label def inc_d 1 "Yes", modify
label values endentulism endentulism
label def endentulism 0 "No", modify
label def endentulism 1 "Yes", modify
label values race race
label def race 1 "White", modify
label def race 2 "Black", modify
label def race 3 "Hispanic", modify
label def race 4 "Other", modify
label values age_cat age_cat
label def age_cat 1 "50-59", modify
label def age_cat 2 "60-69", modify
label def age_cat 3 "70-79", modify
label def age_cat 4 "80+", modify
label values male male
label def male 0 "Female", modify
label def male 1 "Male", modify
label values education EDUC
label def EDUC 1 "1.lt high-school", modify
label def EDUC 2 "2.ged", modify
label def EDUC 3 "3.high-school graduate", modify
label def EDUC 4 "4.some college", modify
label def EDUC 5 "5.college and above", modify
label values veteran veteran
label def veteran 0 "No", modify
label def veteran 1 "Yes", modify
label values mothered mothered
label def mothered 0 "Less than High School", modify
label def mothered 1 "High School or Higher", modify
label values smoke_now smoke_now
label def smoke_now 0 "Non-Smoker", modify
label def smoke_now 1 "Currently Smokes", modify
label values r11dentst YESNO
label def YESNO 0 "0.no", modify
label def YESNO 1 "1.yes", modify
    Tags: None
  • #2
    Can you run the following code successfully:
    Code:
    webuse womenwk, clear
qregsel wage educ age, select(married children educ age) quantile(.1 .5 .9)
    And qregsel need function mm_cond, you can install it using
    Code:
    ssc install moremata, replace

    Comment

    • #3
      When I run qregsel with the womenwk dataset I get an output, but when I try using the function on my data set, I get still get the error Dependent variable never censored because of selection or selection indicator is not binary.
      r(198); I updated the moremata package and I still get the error above.


      qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)

      Comment

      • #4
        Try to detach value number from variables.

        Code:
        label values education EDUC
label def EDUC 1 "lt high-school", modify
label def EDUC 2 "ged", modify
label def EDUC 3 "high-school graduate", modify
label def EDUC 4 "some college", modify
label def EDUC 5 "college and above", modify

label values r11dentst YESNO
label def YESNO 0 "no", modify
label def YESNO 1 "yes", modify
        https://www.statalist.org/forums/for...implementation

        Comment

        • #5
          Code:
           
label val log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease r11dentst

 qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)

Dependent variable never censored because of selection or selection indicator is not binary.

          My apologies, i don't know why this post copied so many times, I tried dropping the value labels and I still get the error "Dependent variable never censored because of selection or selection indicator is not binary."
          Last edited by Luis Mijares Castaneda; 03 Feb 2025, 19:46.

          Comment

          • #6
            Code:
             
label val log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease r11dentst

 qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)

Dependent variable never censored because of selection or selection indicator is not binary.


            Comment

            • #7
              Sorry for #2 and #4. The basic grammar of -qregsel- is qregsel depvar varlist [if] [in] , select([depvar_s =] varlist_s) quantile(#)
              Like heckman model, if depvar_s is specified, it should be coded as 0 or 1, with 0 indicating an observation not selected and 1 indicating a selected observation. If depvar_s is not specified, observations for which depvar is not missing are assumed selected, and those for which depvar is missing are assumed not selected.

              And that is obvious when set trace on:

              Code:
              - tokenize `select', parse("=")
= tokenize r11dentst=race age_cat, parse("=")
- if "`2'" != "=" {
= if "=" != "=" {
  local x_s `select'
  tempvar y_s
  qui gen byte `y_s' = (`depvar'!=.) `if' `in'
  }
- else {
- local y_s `1'
= local y_s r11dentst
- local x_s `3'
= local x_s race age_cat
- }
- capture unab x_s : `x_s'
= capture unab x_s : race age_cat

..........

- qui tab `y_s'
= qui tab r11dentst
- if r(r) != 2 {
  dis as error "Dependent variable never censored because of selection or selection indicator is not binary."
  exit 198
  }
              So I think the solution is replacing your dependent variable to missing if its value is 0.

              Code:
              replace log_avrg_cost=. if log_avrg_cost==0
qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)

Grid for the copula parameter (100)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................
..................................................
 
Quantile selection model                         Number of obs      =       87
                                                 Selected           =       56
                                                 Nonselected        =       31
Copula parameter (gaussian):    -0.28
--------------------------------------------------------------------------------
 log_avrg_cost |      Coef.
---------------+----------------------------------------------------------------
q10            |
         inc_d |  -.5917213
   endentulism |     1.0741
          race |  -.1868762
       age_cat |   .9016545
          male |  -.2918677
     education |   .0164915
       veteran |   -.711922
      mothered |   1.296248
        wealth |     .14869
     smoke_now |   1.889079
chronicdisease |  -.0735079
         _cons |   1.741545
---------------+----------------------------------------------------------------
q25            |
         inc_d |  -.1185757
   endentulism |   .3394867
          race |  -.4275782
       age_cat |    .190285
          male |     -.7211
     education |   .0247499
       veteran |  -.0931269
      mothered |   .5616347
        wealth |   .3367208
     smoke_now |  -1.026533
chronicdisease |   .2834159
         _cons |   4.065812
---------------+----------------------------------------------------------------
q50            |
         inc_d |  -.7411483
   endentulism |   -.307586
          race |  -.3676573
       age_cat |     .34587
          male |   -1.18841
     education |     .01476
       veteran |    .629845
      mothered |   .3406209
        wealth |  -.0488742
     smoke_now |  -1.015606
chronicdisease |   .2509791
         _cons |   5.663649
---------------+----------------------------------------------------------------
q75            |
         inc_d |   -.477296
   endentulism |   3.650261
          race |  -.3350918
       age_cat |    .502547
          male |  -1.797146
     education |   .0621423
       veteran |   .3633428
      mothered |   .1116142
        wealth |   .2105526
     smoke_now |   .7805313
chronicdisease |   .3878358
         _cons |   5.169081
---------------+----------------------------------------------------------------
q90            |
         inc_d |  -.9127351
   endentulism |  -1.409566
          race |   -.310367
       age_cat |  -.0760186
          male |  -1.806011
     education |   .1288075
       veteran |   .4739201
      mothered |   .0328543
        wealth |   .2035147
     smoke_now |  -.2715086
chronicdisease |   .4045722
         _cons |    7.19649
--------------------------------------------------------------------------------

              Comment

              • #8
                Thank you this worked, however, I was wondering if you knew how to introduce factor variables to the qregsel model? I want to see the coefficient on each level of the categorical variables

                Code:
                
. qregsel log_avrg_cost i.inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)
factor-variable and time-series operators not allowed
r(101);

                Comment

                • #9
                  Yes, use xi: before qregsel.

                  Code:
                  xi: qregsel log_avrg_cost i.inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11dentst) quantile(0.10 0.25 0.5 0.75 0.90)

                  Comment

                  • #10
                    Sorry, but two follow-up questions, I tried implementing the xi: prefix and I don't get coefficients for the levels of the variables, also I was wondering if it was possible to implement survey weights? I know the heckman model states that "Weights are not allowed with the bootstrap prefix twostep, vce(), first, lrmodel, and weights are not allowed with the svy prefix pweights, fweights, and iweights are allowed with maximum likelihood estimation; see weight. No weights are allowed if twostep is specified." But I was wondering if you knew of a possible work around?


                    Code:
                    . xi: qregsel log_avrg_cost inc_d endentulism race age_cat male education veteran mothered wealth smoke_now chronicdisease, select(r11d
> entst) quantile(0.10 0.25 0.5 0.75 0.90)
Grid for the copula parameter (100)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................
..................................................
 
Quantile selection model                         Number of obs      =    12364
                                                 Selected           =     8156
                                                 Nonselected        =     4208
Copula parameter (gaussian):     0.32
--------------------------------------------------------------------------------
 log_avrg_cost | Coefficient
---------------+----------------------------------------------------------------
q10            |
         inc_d |   .0221442
   endentulism |   .0514224
          race |  -.0769367
       age_cat |   .1770904
          male |  -.0648551
     education |   .0225106
       veteran |   .0289118
      mothered |  -.0756595
        wealth |   .1519029
     smoke_now |   .0843225
chronicdisease |   .0252361
         _cons |   3.650929
---------------+----------------------------------------------------------------
q25            |
         inc_d |  -.0101356
   endentulism |   .0881108
          race |  -.0570073
       age_cat |   .1234644
          male |  -.0749183
     education |   .0444297
       veteran |   .0568609
      mothered |   -.046574
        wealth |   .1554628
     smoke_now |   .1718711
chronicdisease |   .0180574
         _cons |   4.491532
---------------+----------------------------------------------------------------
q50            |
         inc_d |  -.0188251
   endentulism |   .3251516
          race |  -.0109526
       age_cat |   .0870298
          male |  -.0658264
     education |   .0444925
       veteran |   .0432081
      mothered |   .0175573
        wealth |   .1209759
     smoke_now |   .1178917
chronicdisease |   .0097562
         _cons |   5.444097
---------------+----------------------------------------------------------------
q75            |
         inc_d |  -.0707277
   endentulism |   .2264196
          race |   .0322966
       age_cat |      .0885
          male |  -.0558291
     education |    .036868
       veteran |   .0564231
      mothered |   .0546447
        wealth |   .0987129
     smoke_now |   .0158849
chronicdisease |   .0102929
         _cons |   6.339289
---------------+----------------------------------------------------------------
q90            |
         inc_d |  -.0221718
   endentulism |   .2190943
          race |  -.0105137
       age_cat |   .0728167
          male |  -.0603088
     education |   .0420561
       veteran |   .1064943
      mothered |    .016353
        wealth |    .107214
     smoke_now |   .1300904
chronicdisease |  -.0007818
         _cons |   7.125686
--------------------------------------------------------------------------------

. 
end of do-file

                    Comment

                    • #11
                      First, you must add an i. prefix before category variables. As you can see in #9, I add i. before inc_d, it's i.inc_d now.
                      Second, I don't find weight option in grammar of qregsel command, so it seems that it does not support weight. You can try to use -arhomme- command as an alternative.

                      Comment

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