Non proportional odds model in GSEM

Neeraj Kumar

Join Date: Jul 2017
Posts: 98

#16

27 Jul 2022, 16:57

The remaining variable results are as followed:

Code:

 Predictive margins                              Number of obs     =      9,458
Model VCE    : OIM

Expression   : Pr(A170==6), predict(outcome (6))

1._at        : quantX0472      =           0

2._at        : quantX0472      =           1

3._at        : quantX0473      =           0

4._at        : quantX0473      =           1

5._at        : quantX0474      =           0

6._at        : quantX0474      =           1

7._at        : relative2       =           0

8._at        : relative2       =           1

9._at        : relative3       =           0

10._at       : relative3       =           1

11._at       : relative4       =           0

12._at       : relative4       =           1

13._at       : marriage        =           0

14._at       : marriage        =           1

15._at       : IU0             =        .747

16._at       : X011            =         2.6

17._at       : X003            =       39.17

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0659772    .003817    17.28   0.000      .058496    .0734585
          2  |   .1133882    .006058    18.72   0.000     .1015147    .1252617
          3  |   .0672797   .0029593    22.74   0.000     .0614796    .0730798
          4  |   .1776509   .0123581    14.38   0.000     .1534295    .2018723
          5  |   .0492205   .0035433    13.89   0.000     .0422757    .0561653
          6  |   .1647399    .012964    12.71   0.000     .1393308     .190149
          7  |    .098202   .0037686    26.06   0.000     .0908157    .1055883
          8  |    .050772    .006651     7.63   0.000     .0377363    .0638077
          9  |   .0979507   .0035934    27.26   0.000     .0909078    .1049936
         10  |   .0207947    .008947     2.32   0.020     .0032589    .0383305
         11  |    .115669   .0050211    23.04   0.000     .1058279    .1255101
         12  |  -.0421235   .0107233    -3.93   0.000    -.0631409   -.0211062
         13  |   .0702149   .0076901     9.13   0.000     .0551427    .0852871
         14  |   .0885738   .0030359    29.18   0.000     .0826236    .0945241
         15  |    .087774   .0028923    30.35   0.000     .0821052    .0934428
         16  |   .0871265   .0028713    30.34   0.000     .0814988    .0927543
         17  |   .0870935   .0028949    30.08   0.000     .0814195    .0927674
------------------------------------------------------------------------------

Predictive margins                              Number of obs     =      9,458
Model VCE    : OIM

Expression   : Pr(A170==7), predict(outcome (7))

1._at        : quantX0472      =           0

2._at        : quantX0472      =           1

3._at        : quantX0473      =           0

4._at        : quantX0473      =           1

5._at        : quantX0474      =           0

6._at        : quantX0474      =           1

7._at        : relative2       =           0

8._at        : relative2       =           1

9._at        : relative3       =           0

10._at       : relative3       =           1

11._at       : relative4       =           0

12._at       : relative4       =           1

13._at       : marriage        =           0

14._at       : marriage        =           1

15._at       : IU0             =        .747

16._at       : X011            =         2.6

17._at       : X003            =       39.17

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1633802    .005715    28.59   0.000     .1521791    .1745814
          2  |   .1801634   .0085627    21.04   0.000     .1633808    .1969459
          3  |   .1649903    .004526    36.45   0.000     .1561194    .1738612
          4  |   .1933135   .0172953    11.18   0.000     .1594153    .2272117
          5  |   .1599886   .0059154    27.05   0.000     .1483947    .1715825
          6  |   .1948554   .0189071    10.31   0.000     .1577982    .2319127
          7  |   .1625858   .0047734    34.06   0.000     .1532302    .1719415
          8  |   .2013719   .0107094    18.80   0.000     .1803818    .2223621
          9  |    .168024    .004607    36.47   0.000     .1589944    .1770536
         10  |   .1891139   .0139307    13.58   0.000     .1618103    .2164175
         11  |   .1613192   .0063993    25.21   0.000     .1487768    .1738616
         12  |   .1903685   .0158969    11.98   0.000     .1592111    .2215258
         13  |   .1510309   .0114899    13.14   0.000      .128511    .1735507
         14  |   .1742829   .0041136    42.37   0.000     .1662205    .1823454
         15  |   .1756855   .0039588    44.38   0.000     .1679264    .1834446
         16  |   .1724044   .0038844    44.38   0.000     .1647911    .1800177
         17  |    .171953   .0038933    44.17   0.000     .1643222    .1795839
------------------------------------------------------------------------------

Predictive margins                              Number of obs     =      9,458
Model VCE    : OIM

Expression   : Pr(A170==8), predict(outcome (8))

1._at        : quantX0472      =           0

2._at        : quantX0472      =           1

3._at        : quantX0473      =           0

4._at        : quantX0473      =           1

5._at        : quantX0474      =           0

6._at        : quantX0474      =           1

7._at        : relative2       =           0

8._at        : relative2       =           1

9._at        : relative3       =           0

10._at       : relative3       =           1

11._at       : relative4       =           0

12._at       : relative4       =           1

13._at       : marriage        =           0

14._at       : marriage        =           1

15._at       : IU0             =        .747

16._at       : X011            =         2.6

17._at       : X003            =       39.17

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0772218   .0039081    19.76   0.000     .0695621    .0848816
          2  |   .1352447   .0073282    18.46   0.000     .1208816    .1496077
          3  |   .0852352   .0031768    26.83   0.000     .0790089    .0914615
          4  |   .1823037   .0143415    12.71   0.000     .1541949    .2104125
          5  |   .0678116   .0038742    17.50   0.000     .0602184    .0754048
          6  |   .2194487   .0162704    13.49   0.000     .1875592    .2513381
          7  |    .103373   .0038623    26.76   0.000      .095803    .1109431
          8  |   .0739286   .0078286     9.44   0.000     .0585848    .0892723
          9  |   .1034279   .0038076    27.16   0.000     .0959652    .1108906
         10  |    .065084   .0104739     6.21   0.000     .0445555    .0856125
         11  |   .1160519   .0054597    21.26   0.000     .1053511    .1267527
         12  |    .049786   .0099898     4.98   0.000     .0302064    .0693655
         13  |   .0811256   .0082655     9.81   0.000     .0649256    .0973257
         14  |   .0987434   .0031904    30.95   0.000     .0924903    .1049964
         15  |   .0951648   .0030102    31.61   0.000     .0892649    .1010648
         16  |   .0963744    .002998    32.15   0.000     .0904983    .1022504
         17  |   .0976228   .0030429    32.08   0.000     .0916589    .1035867
------------------------------------------------------------------------------

Predictive margins                              Number of obs     =      9,458
Model VCE    : OIM

Expression   : Pr(A170==9), predict(outcome (9))

1._at        : quantX0472      =           0

2._at        : quantX0472      =           1

3._at        : quantX0473      =           0

4._at        : quantX0473      =           1

5._at        : quantX0474      =           0

6._at        : quantX0474      =           1

7._at        : relative2       =           0

8._at        : relative2       =           1

9._at        : relative3       =           0

10._at       : relative3       =           1

11._at       : relative4       =           0

12._at       : relative4       =           1

13._at       : marriage        =           0

14._at       : marriage        =           1

15._at       : IU0             =        .747

16._at       : X011            =         2.6

17._at       : X003            =       39.17

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0531288   .0030728    17.29   0.000     .0471063    .0591513
          2  |   .0747666   .0053458    13.99   0.000      .064289    .0852442
          3  |   .0542097   .0026107    20.76   0.000     .0490928    .0593265
          4  |   .1063096   .0111136     9.57   0.000     .0845275    .1280918
          5  |   .0353079   .0031324    11.27   0.000     .0291685    .0414472
          6  |   .1528295   .0137302    11.13   0.000     .1259188    .1797402
          7  |   .0631237   .0029141    21.66   0.000     .0574122    .0688352
          8  |   .0542482   .0057572     9.42   0.000     .0429644     .065532
          9  |   .0668948   .0030564    21.89   0.000     .0609045    .0728852
         10  |   .0302351   .0081632     3.70   0.000     .0142355    .0462347
         11  |   .0694173   .0044273    15.68   0.000     .0607399    .0780947
         12  |   .0382753   .0086517     4.42   0.000     .0213182    .0552324
         13  |   .0451989   .0067103     6.74   0.000      .032047    .0583507
         14  |   .0636268   .0026317    24.18   0.000     .0584688    .0687847
         15  |   .0623231   .0025263    24.67   0.000     .0573716    .0672746
         16  |   .0615617   .0024555    25.07   0.000      .056749    .0663744
         17  |    .062173   .0024908    24.96   0.000     .0572911    .0670549
------------------------------------------------------------------------------

Predictive margins                              Number of obs     =      9,458
Model VCE    : OIM

Expression   : Pr(A170==10), predict(outcome (10))

1._at        : quantX0472      =           0

2._at        : quantX0472      =           1

3._at        : quantX0473      =           0

4._at        : quantX0473      =           1

5._at        : quantX0474      =           0

6._at        : quantX0474      =           1

7._at        : relative2       =           0

8._at        : relative2       =           1

9._at        : relative3       =           0

10._at       : relative3       =           1

11._at       : relative4       =           0

12._at       : relative4       =           1

13._at       : marriage        =           0

14._at       : marriage        =           1

15._at       : IU0             =        .747

16._at       : X011            =         2.6

17._at       : X003            =       39.17

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1364811   .0056093    24.33   0.000      .125487    .1474751
          2  |   .0975743   .0067594    14.44   0.000     .0843261    .1108225
          3  |   .1259752   .0042673    29.52   0.000     .1176114     .134339
          4  |   .1007457   .0122556     8.22   0.000     .0767252    .1247663
          5  |   .1198535   .0059102    20.28   0.000     .1082697    .1314372
          6  |   .1276852   .0138983     9.19   0.000      .100445    .1549253
          7  |   .1180815   .0039366    30.00   0.000      .110366    .1257971
          8  |   .1389333   .0103927    13.37   0.000     .1185641    .1593026
          9  |   .1183194   .0038803    30.49   0.000      .110714    .1259247
         10  |   .1452532   .0142847    10.17   0.000     .1172557    .1732508
         11  |   .1132043   .0052972    21.37   0.000      .102822    .1235866
         12  |   .1480418   .0149514     9.90   0.000     .1187377     .177346
         13  |   .1337301   .0110354    12.12   0.000     .1121012     .155359
         14  |   .1206428   .0035626    33.86   0.000     .1136603    .1276254
         15  |     .11949   .0034464    34.67   0.000     .1127352    .1262448
         16  |   .1219906   .0034034    35.84   0.000     .1153201    .1286611
         17  |   .1220141   .0034341    35.53   0.000     .1152835    .1287448
------------------------------------------------------------------------------

Comment

Richard Williams

Join Date: Apr 2014

Posts: 5043
#17

27 Jul 2022, 19:39

I don't know what you changed but you have it running. As to whether to set the contnuus variables at their means or some other value, see

https://www3.nd.edu/~rwilliam/xsoc73994/Margins01.pdf

I still don't see much point in running your model as is if you aren't going to impose any constraints on the variables. Consider adding the autofit option.

-------------------------------------------
Richard Williams
Professor Emeritus of Sociology
University of Notre Dame
StataNow Version: 19.5 MP (2 processor)
EMAIL: [email protected]
WWW: https://academicweb.nd.edu/~rwilliam/
Comment
Neeraj Kumar

Join Date: Jul 2017

Posts: 98
#18

28 Jul 2022, 14:04

Sir, Thank you so much for the guidance. I will incorporate the autofit option. Once again thank you so much.
Comment
Neeraj Kumar

Join Date: Jul 2017

Posts: 98
#19

25 Apr 2023, 15:54

Sir Richard Williams, I am using a xtoprobit command and after that I am calculating margins, it is showing error "discontinuous region with missing value". I am using the following command:

Code:

xtoprobit A170 LnNSDPpcC X003 X011 marriage employ gender dharm1 dharm2 dharm3 health2 health3 edu2 edu3 edu4 class1 class2 So2 No2 BOD formalscindex2 formalscindex3 informalSc2 informalSc3

Code:

margins, dydx(*)

I have run the model with different permutation and combination and I have found this particular error is coming when I am including No2 variable. No2 is a continuous variable and one of the important variable for my study. I have deleted all the missing value but if I am including this No2 variable the error is persisting. Is there any way I can sort out this issue or dropping this variable is the only choice left for me. Kindly help me.
Comment
Richard Williams

Join Date: Apr 2014

Posts: 5043
#20

25 Apr 2023, 16:57

Hi Neeraz. This appears to be a new topic so you may want to start a new thread. My guess is you'd have to look more carefully at the problematic variable, e.g. maybe it has very little variability or is miscoded or is highly correlated with other variables.

My generic advice for dealing with weird problems is on pp. 2-3 of

https://www3.nd.edu/~rwilliam/xsoc73994/L02.pdf

-------------------------------------------
Richard Williams
Professor Emeritus of Sociology
University of Notre Dame
StataNow Version: 19.5 MP (2 processor)
EMAIL: [email protected]
WWW: https://academicweb.nd.edu/~rwilliam/
Comment
Neeraj Kumar

Join Date: Jul 2017

Posts: 98
#21

25 Apr 2023, 19:58

Sir, Thank you so much for reply. I have used the difficult command and it's work. I had posted the new thread but I did not got the response. Once again thank you so much for your help.
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