Dear specialists,

I really thank everyone here sparing no efforts to help me.

I have a censored dataset and the regression is an inverted U-relationship.


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Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(M control1 control2 control3 control4 control5 control6) int x double(y control7 control8)
1 0 0 0 0 1 0   8 22.949106216430664   17.3120174407959 20.570825576782227
0 1 1 1 0 1 0  18 22.806461334228516 16.588098526000977 18.744325637817383
1 1 0 0 0 4 1   8 17.978151321411133 17.216707229614258  .6931471824645996
0 0 0 1 0 1 0 123 16.005598068237305 16.300416946411133   18.9520320892334
1 0 0 1 0 1 0   5 17.918004989624023 17.034385681152344 15.428786277770996
0 1 0 1 1 1 0 346 24.482324600219727 17.399028778076172 20.355924606323242
1 1 0 1 1 1 0   3 24.223512649536133 18.450239181518555 17.617250442504883
0 0 0 0 0 1 0  96 23.566471099853516 16.811243057250977 16.955846786499023
0 1 0 0 1 2 0 425  23.66777801513672  18.19753646850586 18.775419235229492
0 0 0 1 0 1 0 106 23.174531936645508 17.370859146118164  16.89447021484375
1 1 0 1 0 1 0   4 17.732799530029297 14.457364082336426 19.205141067504883
1 0 0 1 0 1 1   3 15.683768272399902 14.457364082336426  .6931471824645996
0 1 0 1 0 2 0  63 22.443294525146484   17.7275333404541  19.00855827331543
1 0 0 0 0 2 0   8     18.52099609375  16.11809539794922 13.792720794677734
0 0 1 1 0 1 0   9 13.517104148864746  19.04928970336914 18.136098861694336
1 0 0 0 0 1 0   6 15.053014755249023 14.457364082336426 19.068098068237305
0 0 1 0 0 1 0 697 23.785768508911133 17.504390716552734 19.758201599121094
0 1 0 1 1 2 0  46 23.127647399902344 17.822843551635742 18.129066467285156
1 0 0 0 0 2 0 113  17.74494171142578 14.508657455444336 14.646207809448242
0 0 0 1 0 3 1 125  18.34654998779297 14.375125885009766 19.459447860717773
0 1 1 1 1 1 0 107   26.6013240814209 19.806974411010742 21.029006958007813
0 1 1 1 1 1 0 107 26.275054931640625   19.5192928314209 19.718734741210938
1 0 0 0 0 1 0  11 21.269575119018555 13.815510749816895 18.251508712768555
0 1 0 0 1 1 0   4  22.68794822692871 17.281246185302734 16.724597930908203
0 0 0 1 0 1 0 105 14.990157127380371 13.815510749816895 15.718832015991211
0 0 0 1 0 1 0 663 19.204071044921875 17.034385681152344 13.799907684326172
1 0 0 1 0 1 0   4  16.33489990234375 14.508657455444336  17.44756507873535
0 0 0 0 0 1 0   8  18.20665740966797 16.380460739135742  18.75150489807129
1 0 0 1 0 1 0   3 13.334243774414063 14.457364082336426  19.18917465209961
0 0 0 1 0 1 0 284  23.10700035095215 16.523561477661133 19.209861755371094
0 1 0 1 1 2 0 284  22.16788673400879 15.424948692321777 14.695516586303711
1 1 1 1 0 1 0   8 24.212265014648438 19.035865783691406 19.782316207885742
0 0 0 1 0 1 0 455   17.6008358001709 16.811243057250977    9.2648286819458
1 1 0 0 1 1 0   2 23.746435165405273  16.86003303527832 18.270660400390625
1 0 0 1 0 2 0   5 19.581193923950195 17.281246185302734   18.4521541595459
0 0 0 1 0 1 0   5  19.14850425720215  16.21340560913086 15.823354721069336
0 0 1 0 0 1 0 106 22.485082626342773  16.11809539794922 17.567533493041992
1 1 0 0 0 1 0   7 22.783226013183594 15.761420249938965 18.496137619018555
0 0 0 1 0 1 0  11 16.819210052490234 13.527828216552734  16.94902992248535
0 0 0 1 0 1 0 151  18.90007781982422  16.11809539794922 18.147491455078125
1 0 0 0 0 1 0   8  21.86478614807129 17.034385681152344 18.850242614746094
1 1 1 1 1 1 0   7 22.845659255981445  17.68671226501465 19.689342498779297
1 1 0 1 1 1 0   6  23.92540740966797 18.132999420166016  20.34378433227539
0 0 0 1 0 1 0  13 23.123905181884766 17.909854888916016  18.01799774169922
0 0 0 1 0 3 0   3 18.898025512695313 15.761420249938965 18.995527267456055
0 0 0 1 0 1 0   5 19.191537857055664  16.01273536682129 19.141666412353516
0 1 1 0 1 1 0  42  24.29490089416504   17.7275333404541 19.795740127563477
1 0 0 1 0 1 0   8 18.446407318115234 16.883563995361328 20.610980987548828
0 0 0 0 0 1 0   5 17.829132080078125  16.01273536682129 19.647912979125977
1 1 1 1 1 1 0   6 23.370441436767578 17.622173309326172 19.004106521606445
0 1 0 1 1 1 0 175 23.807193756103516 18.980297088623047 20.575855255126953
0 0 0 0 0 1 0 284 21.572847366333008 15.424948692321777 19.118602752685547
0 0 0 1 0 1 0 109 18.238428115844727 15.068273544311523  18.99883270263672
0 1 0 0 1 1 0   5 22.323518753051758 18.035018920898438 18.701475143432617
1 1 1 1 1 1 0   4 24.551706314086914 18.951309204101563   18.1805477142334
0 0 0 1 0 5 0  46  17.35716438293457 13.815510749816895  17.30387306213379
0 0 0 0 0 1 1 279  20.36696434020996 15.894951820373535  .6931471824645996
0 1 1 1 1 2 0  18     24.85302734375 19.113828659057617  19.06422233581543
1 1 0 1 1 1 0   6  23.23371124267578 15.761420249938965  18.10226821899414
1 1 1 1 1 1 0   8 24.375486373901367 17.822843551635742 19.248655319213867
1 1 0 1 1 1 0   8 23.496232986450195 17.909854888916016 18.418119430541992
0 0 0 0 0 1 0  58 18.395998001098633 15.068273544311523  17.63210678100586
0 1 0 1 1 1 0   5  24.17245864868164 18.396387100219727 18.096141815185547
0 0 0 0 0 1 0  40  15.38599967956543 14.508657455444336  9.139273643493652
0 1 0 0 0 1 0  21 23.556793212890625 17.426427841186523 19.268308639526367
0 1 1 0 1 1 0   6 24.420333862304688 16.811243057250977 19.697742462158203
1 0 0 1 0 2 0   4 18.793746948242188 14.457364082336426 13.836281776428223
1 1 0 0 1 1 0   2 23.786779403686523 16.811243057250977 17.747819900512695
1 1 0 1 0 1 0   8  19.58517074584961 16.951004028320313 18.983076095581055
1 0 0 0 0 1 0   5  21.72478675842285 16.811243057250977  18.40504264831543
1 1 1 0 1 1 0   6 25.056909561157227  16.11809539794922  19.21035385131836
0 1 1 0 1 1 0 284  23.98286247253418 15.424948692321777 19.062726974487305
0 1 1 0 1 2 0 284  22.97431182861328  16.01273536682129 18.093008041381836
1 0 0 0 0 1 0   8 18.126632690429688 14.508657455444336  19.24971580505371
0 0 1 1 0 1 0 697  23.21192741394043   17.7275333404541  16.86979103088379
1 0 0 0 0 1 0 135  23.33150863647461  16.11809539794922   19.8182315826416
0 0 0 0 0 5 0 490   18.4140682220459 15.894951820373535 13.995794296264648
0 . . . . . .   .                  .                  .                  .
0 0 0 1 0 1 0 697 22.618450164794922 17.504390716552734  .6931471824645996
0 0 0 1 0 1 0 161  24.22525405883789 16.845643997192383 19.180622100830078
0 0 0 0 0 1 0  63  23.49774169921875 17.281246185302734 17.358951568603516
0 0 0 1 0 1 0 663 19.229278564453125 16.300416946411133 16.765993118286133
0 0 0 0 0 1 0  58 15.912301063537598 14.747674942016602  .6931471824645996
0 1 1 0 1 1 0 284 24.110410690307617  16.11809539794922 12.027538299560547
0 0 0 0 0 1 0  40 21.205873489379883 17.370859146118164 16.865760803222656
1 0 0 1 0 1 0  56 17.881935119628906 13.815510749816895 17.762706756591797
1 0 1 1 0 1 0   4  18.35368537902832  16.11809539794922  19.24974250793457
0 0 1 0 0 1 0 777  23.53510284423828  16.11809539794922 17.638704299926758
0 1 1 1 1 1 0 109 25.289718627929688 17.822843551635742 14.394180297851563
1 0 0 1 0 2 0   8 17.924514770507813 17.676240921020508 19.183582305908203
1 1 1 0 1 1 0  11 24.805057525634766 18.643823623657227 19.623146057128906
1 1 1 0 1 1 0  11 24.311477661132813  18.31532096862793 18.698169708251953
1 1 1 1 1 1 0  12 25.720643997192383 18.951309204101563 20.346450805664063
0 1 0 1 1 1 0   6 18.496326446533203 17.034385681152344 17.243867874145508
1 1 1 1 1 1 0   6 25.264881134033203   19.5192928314209  19.64662742614746
0 0 0 1 0 1 0  40  17.51824378967285 14.457364082336426 18.340465545654297
0 1 0 0 0 1 0 151 23.607288360595703 17.216707229614258 18.674285888671875
0 1 0 0 1 1 0   9  23.45798683166504 16.811243057250977 17.900461196899414
1 0 0 1 1 1 0   5 20.373798370361328 17.553180694580078  18.21268653869629
1 0 0 0 0 1 0   2 22.999513626098633 17.504390716552734 16.867582321166992
end
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M is the moderator and my model is like: tobit y x x2 M Mx Mx2 control1 control2 control3 control4 control5 control6 control7 control8

I want to study the effect of M on the turning point of the quadratic model(shifts to left or right). According to some literature, to test the moderating effect of M on X2, full specification means to include both term M × X and term M × X2 in the regression model. When testing the turning point shift, one should first use the full specification, but if the coefficient of M × X2 is not statistically different from zero, it is appropriate to drop the term M × X2. I dropped the three-way interaction and the X*M is significant. However, I don't know how to graph the original inverted U-relationship and the moderating effect of the turning point(M being 0 or 1) in STATA. I've read and tried some suggestions provided in similar posts about graphing the interaction term in the quadratic model; however, none of them seemed to work in my case.

It would be great if somebody can help me. Thank you very much in advance!

Julie
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