Dear stata users,

I have been trying to replicate Stewart, M.B., 2004. Semi-nonparametric estimation of extended ordered probit models. The Stata Journal, 4(1), pp.27-39 in the matter of high school truancy.

I would like to plot the density graph for the sneop estimation but I wasn't able to do it.

This is my code:
Code:
 status - primary dataset

* (1) stopped to go to school before each school year end ("abandoned"); Chronic Absenteeism
* (2) attend school until the school year end, but did not complete the grade due to poor performance "repeated); MEDIUM truancy
* (3) advanced in studies ("approved"). Regular student

* Generate truancy_status
* generate truancy_status = word("low med high", status)

* Identify quantity of pupils per status and its mean nottendence by cohort - I did it in separate dataset.
* egen mean = mean(noatte), by (status c)
* sort c
* by c: tab mean status

* geberate dummy stu_staff_ratio
sort school t
by school t: egen total_stu=count(i)
gen stu_staff_ratio = total_stu/staff

oprobit status boy age govaid night urb lib sci comp sports tage tagesd twom stu_staff_ratio noatte sroom chil element 


********************** Semi Nonparametric Models ********************************************************************************8

sneop status boy age govaid lib sci comp sports tage tagesd twom stu_staff_ratio noatte sroom chil element, order(3)

sneop status boy age govaid lib sci comp sports tage tagesd twom stu_staff_ratio noatte sroom chil element, dplot(gr)
A sample of my data set.

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(status boy) float age byte govaid double(lib sci comp sports tage tagesd twom) float(stu_staff_ratio noatte) double(sroom child element)
3 1 15.3 0 1 1 1 0 43.54 10.11 81.08 .8372093  36 18 1 1
2 1 16.1 0 1 1 1 0 43.54 10.11 81.08 .8372093  77 25 1 1
3 1 16.1 0 1 1 1 0 43.54 10.11 81.08 .8372093  39 18 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  30 25 1 1
2 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093 142 25 1 1
2 0 16.1 0 1 1 1 0 43.54 10.11 81.08 .8372093 164 18 1 1
3 1 15.5 0 1 1 1 0 43.54 10.11 81.08 .8372093  88 25 1 1
3 1 15.1 0 1 1 1 0 43.54 10.11 81.08 .8372093  17 25 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  35 18 1 1
3 1 16.3 0 1 1 1 0 43.54 10.11 81.08 .8372093  85 25 1 1
3 1 15.3 0 1 1 1 0 43.54 10.11 81.08 .8372093  30 18 1 1
2 1 16.3 0 1 1 1 0 43.54 10.11 81.08 .8372093  93 18 1 1
3 1 15.8 0 1 1 1 0 43.54 10.11 81.08 .8372093  15 25 1 1
3 0 14.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  23 25 1 1
2 0 16.7 0 1 1 1 0 43.54 10.11 81.08 .8372093  58 25 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  57 25 1 1
3 1 15.3 0 1 1 1 0 43.54 10.11 81.08 .8372093  65 18 1 1
2 1   16 0 1 1 1 0 43.54 10.11 81.08 .8372093 177 25 1 1
3 0 15.2 0 1 1 1 0 43.54 10.11 81.08 .8372093  20 25 1 1
2 0 17.5 0 1 1 1 0 43.54 10.11 81.08 .8372093 203 25 1 1
2 1 15.6 0 1 1 1 0 43.54 10.11 81.08 .8372093  92 25 1 1
3 0 15.2 0 1 1 1 0 43.54 10.11 81.08 .8372093  60 18 1 1
3 0 15.8 0 1 1 1 0 43.54 10.11 81.08 .8372093  16 18 1 1
2 0 17.6 0 1 1 1 0 43.54 10.11 81.08 .8372093  72 25 1 1
2 0 15.8 0 1 1 1 0 43.54 10.11 81.08 .8372093  63 25 1 1
2 1   17 0 1 1 1 0 43.54 10.11 81.08 .8372093 185 25 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  52 18 1 1
3 0 15.7 0 1 1 1 0 43.54 10.11 81.08 .8372093  44 18 1 1
2 1 15.4 0 1 1 1 0 43.54 10.11 81.08 .8372093  87 18 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 .8372093  54 25 1 1
1 1 16.4 0 1 1 1 0 43.54 10.11 81.08 .8372093   . 18 1 1
3 0 15.5 0 1 1 1 0 43.54 10.11 81.08 .8372093  55 25 1 1
3 0 15.8 0 1 1 1 0 43.54 10.11 81.08 .8372093  76 18 1 1
3 0 16.7 0 1 1 1 0 43.54 10.11 81.08 .8372093  65 25 1 1
2 1 16.6 0 1 1 1 0 43.54 10.11 81.08 .8372093  51 18 1 1
2 1 17.1 0 1 1 1 0 43.54 10.11 81.08 .8372093 146 25 1 1
3 0 16.4 0 1 1 1 0 43.54 10.11 81.08 1.767442  85 14 1 1
3 0 16.7 0 1 1 1 0 43.54 10.11 81.08 1.767442 128 31 1 1
3 1 16.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  27 17 1 1
3 1 15.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  32 14 1 1
3 1 16.6 0 1 1 1 0 43.54 10.11 81.08 1.767442 104 31 1 1
3 0 14.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  32  3 1 1
3 1 17.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  36 14 1 1
3 0 15.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  38 31 1 1
3 1 15.4 0 1 1 1 0 43.54 10.11 81.08 1.767442  89 17 1 1
3 1 16.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  36 31 1 1
3 1 16.3 0 1 1 1 0 43.54 10.11 81.08 1.767442  66 31 1 1
3 0 17.7 0 1 1 1 0 43.54 10.11 81.08 1.767442  63 31 1 1
3 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  82 19 1 1
3 0 16.1 0 1 1 1 0 43.54 10.11 81.08 1.767442  57 31 1 1
3 1 16.1 0 1 1 1 0 43.54 10.11 81.08 1.767442  15 19 1 1
3 0 15.7 0 1 1 1 0 43.54 10.11 81.08 1.767442  61 31 1 1
3 1 15.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  56 31 1 1
3 0 15.2 0 1 1 1 0 43.54 10.11 81.08 1.767442  40 14 1 1
3 1 16.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  25 17 1 1
2 1 18.1 0 1 1 1 0 43.54 10.11 81.08 1.767442  78 31 1 1
3 0 15.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  68 31 1 1
3 1 20.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  45 14 1 1
3 0 16.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  34 19 1 1
2 0 16.6 0 1 1 1 0 43.54 10.11 81.08 1.767442 101 31 1 1
3 1   15 0 1 1 1 0 43.54 10.11 81.08 1.767442  65 31 1 1
3 0 17.7 0 1 1 1 0 43.54 10.11 81.08 1.767442  57 19 1 1
3 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  78 19 1 1
2 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442 168 31 1 1
3 1 15.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  24 14 1 1
3 0 15.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  69 14 1 1
3 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  27 19 1 1
3 1 16.3 0 1 1 1 0 43.54 10.11 81.08 1.767442  86 19 1 1
3 1 15.7 0 1 1 1 0 43.54 10.11 81.08 1.767442  65 31 1 1
3 0 16.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  22 17 1 1
3 0 20.1 0 1 1 1 0 43.54 10.11 81.08 1.767442  60 17 1 1
3 1 17.3 0 1 1 1 0 43.54 10.11 81.08 1.767442  24 17 1 1
3 1 15.7 0 1 1 1 0 43.54 10.11 81.08 1.767442  90 31 1 1
2 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  67 31 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  84 31 1 1
3 1 16.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  68 19 1 1
3 0 14.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  36 14 1 1
3 1 16.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  44 31 1 1
3 0 18.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  79 31 1 1
3 1 16.4 0 1 1 1 0 43.54 10.11 81.08 1.767442  62  3 1 1
3 1 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442   8  3 1 1
2 0 18.4 0 1 1 1 0 43.54 10.11 81.08 1.767442 118 31 1 1
2 1   18 0 1 1 1 0 43.54 10.11 81.08 1.767442 137 31 1 1
3 0 15.4 0 1 1 1 0 43.54 10.11 81.08 1.767442  26 17 1 1
3 0 17.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  63 31 1 1
3 0 16.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  40 17 1 1
3 0 16.3 0 1 1 1 0 43.54 10.11 81.08 1.767442  45 31 1 1
3 0 16.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  46 19 1 1
3 1   16 0 1 1 1 0 43.54 10.11 81.08 1.767442  97 17 1 1
3 0 15.2 0 1 1 1 0 43.54 10.11 81.08 1.767442  21 14 1 1
2 1 16.4 0 1 1 1 0 43.54 10.11 81.08 1.767442 175 31 1 1
3 0 14.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  81 31 1 1
3 0 15.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  18 19 1 1
3 0 16.2 0 1 1 1 0 43.54 10.11 81.08 1.767442  20 19 1 1
2 0 16.3 0 1 1 1 0 43.54 10.11 81.08 1.767442 138 31 1 1
3 0 16.8 0 1 1 1 0 43.54 10.11 81.08 1.767442  48  3 1 1
2 1 15.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  81 31 1 1
3 0 16.5 0 1 1 1 0 43.54 10.11 81.08 1.767442  69 19 1 1
3 1 15.9 0 1 1 1 0 43.54 10.11 81.08 1.767442  53 31 1 1
3 1 19.6 0 1 1 1 0 43.54 10.11 81.08 1.767442  52 14 1 1
end
Thank you all,

Max