. do "C:\Users\hstryhn\AppData\Local\Temp\STD1c28_000000.tmp"

. * do-file for lecture 3a of VHM 802/812, Winter 2020
. version 16 /* works also with versions 14-15 */

. set more off

. cd "r:\"
r:\

.
. use micegrp.dta, clear

. regress dead dose /* to illustrate extrapolation below zero in regression */

      Source |       SS           df       MS      Number of obs   =        12
-------------+----------------------------------   F(1, 10)        =     29.83
       Model |   49.616367         1   49.616367   Prob > F        =    0.0003
    Residual |   16.633633        10   1.6633633   R-squared       =    0.7489
-------------+----------------------------------   Adj R-squared   =    0.7238
       Total |       66.25        11  6.02272727   Root MSE        =    1.2897

-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     29.1441    5.336195      5.46    0.000      17.25431     41.03388
       _cons |   -2.107171    1.049163     -2.01    0.072     -4.444851     .2305092
-------------+----------------------------------------------------------------

. use mice.dta, clear

. regress dead dose /* same estimates of regression coefficients */

      Source |       SS           df       MS      Number of obs   =       120
-------------+----------------------------------   F(1, 118)       =     27.41
       Model |   4.9616367         1   4.9616367   Prob > F        =    0.0000
    Residual |  21.3633633       118  .181045452   R-squared       =    0.1885
-------------+----------------------------------   Adj R-squared   =    0.1816
       Total |      26.325       119  .221218487   Root MSE        =    .42549

-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     2.91441    .5567137      5.24    0.000      1.811965     4.016855
       _cons |   -.2107171    .1094569     -1.93    0.057     -.4274715     .0060373
-------------+----------------------------------------------------------------

.
. * logistic regression using logit command
. logit dead dose

Iteration 0:   log likelihood = -75.669723
Iteration 1:   log likelihood = -64.201632
Iteration 2:   log likelihood = -63.944944
Iteration 3:   log likelihood = -63.944713
Iteration 4:   log likelihood = -63.944713

Logistic regression                             Number of obs     =        120
                                                LR chi2(1)        =      23.45
                                                Prob > chi2       =     0.0000
Log likelihood = -63.944713                     Pseudo R2         =     0.1549

-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     14.6369    3.332533      4.39    0.000       8.10526     21.16855
       _cons |   -3.569738    .7053432     -5.06    0.000     -4.952185    -2.187291
-------------+----------------------------------------------------------------

. predict linpred, xb /* predictions on logit scale */

. twoway (scatter linpred dose) (line linpred dose)

. lincom _cons+dose*0.1


 ( 1)  .1*[dead]dose + [dead]_cons = 0


-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |   -2.106048    .4015256     -5.25    0.000     -2.893023    -1.319072
-------------+----------------------------------------------------------------

. predict phat, p     /* predicted probabilities */

. twoway (scatter phat dose) (line phat dose)

. estat gof           /* Pearson goodness-of-fit test */


Logistic model for dead, goodness-of-fit test


       number of observations =       120
 number of covariate patterns =        12
             Pearson chi2(10) =         8.74
                  Prob > chi2 =         0.5567

.
. * analysis with dichotomous dose
. prtesti 60 32 60 7, count /* 2 independent proportions */

Two-sample test of proportions                     x: Number of obs =       60
                                                   y: Number of obs =       60
-------------+----------------------------------------------------------------
             |       Mean   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           x |   .5333333   .0644061                      .4070997     .659567
           y |   .1166667   .0414438                      .0354382    .1978951
-------------+----------------------------------------------------------------
        diff |   .4166667   .0765881                      .2665567    .5667766
             |  under Ho:   .0855132     4.87   0.000
-------------+----------------------------------------------------------------
        diff = prop(x) - prop(y)                                  z =   4.8725
    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = 1.0000         Pr(|Z| > |z|) = 0.0000          Pr(Z > z) = 0.0000

. csi 32 7 28 53, or        /* relative risk and odds-ratio */

                 |   Exposed   Unexposed  |      Total
-----------------+------------------------+------------
           Cases |        32           7  |         39
        Noncases |        28          53  |         81
-----------------+------------------------+------------
           Total |        60          60  |        120
                 |                        |
            Risk |  .5333333    .1166667  |       .325
                 |                        |
                 |      Point estimate    |    [95% Conf. Interval]
                 +------------------------+------------------------
 Risk difference |         .4166667       |    .2665567    .5667766
      Risk ratio |         4.571429       |    2.191203     9.53721
 Attr. frac. ex. |           .78125       |    .5436296    .8951475
 Attr. frac. pop |         .6410256       |
      Odds ratio |         8.653061       |    3.441938    21.63764 (Cornfield)
                 +------------------------+------------------------
                               chi2(1) =    23.74  Pr>chi2 = 0.0000

. generate dose2=(dose>0.16) if dose~=.

. tabulate dead dose2, chi2 column exact lrchi2 /* 2x2 table analysis */

+-------------------+
| Key               |
+-------------------+
|     frequency     |
| column percentage |
+-------------------+

           |         dose2
      dead |         0          1 |     Total
-----------+----------------------+----------
         0 |        53         28 |        81
           |     88.33      46.67 |     67.50
-----------+----------------------+----------
         1 |         7         32 |        39
           |     11.67      53.33 |     32.50
-----------+----------------------+----------
     Total |        60         60 |       120
           |    100.00     100.00 |    100.00

          Pearson chi2(1) =  23.7417   Pr = 0.000
 likelihood-ratio chi2(1) =  25.2010   Pr = 0.000
           Fisher's exact =                 0.000
   1-sided Fisher's exact =                 0.000

. logit dead dose2    /* logistic regression */

Iteration 0:   log likelihood = -75.669723
Iteration 1:   log likelihood = -63.522292
Iteration 2:   log likelihood = -63.070143
Iteration 3:   log likelihood =  -63.06923
Iteration 4:   log likelihood =  -63.06923

Logistic regression                             Number of obs     =        120
                                                LR chi2(1)        =      25.20
                                                Prob > chi2       =     0.0000
Log likelihood =  -63.06923                     Pseudo R2         =     0.1665

-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dose2 |    2.157913    .4782148      4.51    0.000      1.220629     3.095197
       _cons |   -2.024382    .4021506     -5.03    0.000     -2.812582    -1.236181
-------------+----------------------------------------------------------------

. logistic dead dose2 /* same as logit with option or */

Logistic regression                             Number of obs     =        120
                                                LR chi2(1)        =      25.20
                                                Prob > chi2       =     0.0000
Log likelihood =  -63.06923                     Pseudo R2         =     0.1665

-------------+----------------------------------------------------------------
         dead | Odds Ratio    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       dose2 |    8.653061    4.138022      4.51    0.000       3.38932     22.09159
       _cons |    .1320755    .0531142     -5.03    0.000      .0600497     .2904914
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


. display 1/(1+exp(2.024382)) /* baseline prob */
.11666664

. logit dead dose

Iteration 0:   log likelihood = -75.669723
Iteration 1:   log likelihood = -64.201632
Iteration 2:   log likelihood = -63.944944
Iteration 3:   log likelihood = -63.944713
Iteration 4:   log likelihood = -63.944713

Logistic regression                             Number of obs     =        120
                                                LR chi2(1)        =      23.45
                                                Prob > chi2       =     0.0000
Log likelihood = -63.944713                     Pseudo R2         =     0.1549

-------------+----------------------------------------------------------------
         dead |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     14.6369    3.332533      4.39    0.000       8.10526     21.16855
       _cons |   -3.569738    .7053432     -5.06    0.000     -4.952185    -2.187291
-------------+----------------------------------------------------------------

. logistic dead dose  /* same as logit with option or */

Logistic regression                             Number of obs     =        120
                                                LR chi2(1)        =      23.45
                                                Prob > chi2       =     0.0000
Log likelihood = -63.944713                     Pseudo R2         =     0.1549

-------------+----------------------------------------------------------------
         dead | Odds Ratio    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     2273668     7577074      4.39    0.000      3311.843     1.56e+09
       _cons |    .0281632    .0198647     -5.06    0.000      .0070679     .1122204
-------------+----------------------------------------------------------------


Note: _cons estimates baseline odds.


. display 1/(1+exp(3.569738)) /* intercept prob */
.02739179

.
. * demonstration of analysis using grouped data
. use micegrp.dta, clear

. blogit dead total dose

Logistic regression for grouped data            Number of obs     =        120
                                                LR chi2(1)        =      23.45
                                                Prob > chi2       =     0.0000
Log likelihood = -63.944713                     Pseudo R2         =     0.1549

-------------+----------------------------------------------------------------
     _outcome |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dose |     14.6369    3.332533      4.39    0.000       8.10526     21.16855
       _cons |   -3.569738    .7053432     -5.06    0.000     -4.952185    -2.187291
-------------+----------------------------------------------------------------

. predict linpred, xb

. predict phat, p

. predict deadhat, n

. * illustration of calculation of X^2
. generate x2=(dead-deadhat)^2/(total*phat*(1-phat))

. total x2

Total estimation                  Number of obs   =         12

-------------+------------------------------------------------
             |      Total    Std. Err.      [95% Con f. Interval]
-------------+------------------------------------------------
          x2 |    8.742145    3.243557       1.603125     15.88116
-------------+------------------------------------------------

. display chi2tail(12-1-1,8.742145)
.55673739

.
. * detailed do-file for prediction part of lecture
. use daisy2red, clear

.
. * 1a)
. regress wpc i.herd

      Source |       SS           df       MS      Number of obs   =     1,574
-------------+----------------------------------   F(6, 1567)      =     15.11
       Model |  228992.526         6   38165.421   Prob > F        =    0.0000
    Residual |  3959098.03     1,567  2526.54629   R-squared       =    0.0547
-------------+----------------------------------   Adj R-squared   =    0.0511
       Total |  4188090.56     1,573  2662.48605   Root MSE        =    50.265

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        herd |
          2  |   -8.637779    4.518731     -1.91    0.056     -17.50118     .2256182
          3  |    11.08273    4.139467      2.68    0.007      2.963254     19.20221
          4  |    -21.3346    4.662061     -4.58    0.000     -30.47913    -12.19006
          5  |   -22.04426    5.373491     -4.10    0.000     -32.58425    -11.50427
        106  |   -16.29361    4.390344     -3.71    0.000     -24.90518    -7.682041
        119  |   -17.54149    4.932316     -3.56    0.000     -27.21613    -7.866858
             |
       _cons |    76.82721    3.047749     25.21    0.000      70.84911      82.8053
-------------+----------------------------------------------------------------

. margins herd

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        herd |
          1  |    76.82721    3.047749     25.21    0.000      70.84911      82.8053
          2  |    68.18943    3.336189     20.44    0.000      61.64556     74.73329
          3  |    87.90994    2.801146     31.38    0.000      82.41555     93.40433
          4  |    55.49261    3.527895     15.73    0.000      48.57272      62.4125
          5  |    54.78295    4.425566     12.38    0.000      46.10229      63.4636
        106  |     60.5336    3.160119     19.16    0.000      54.33509      66.7321
        119  |    59.28571    3.878011     15.29    0.000      51.67908     66.89235
-------------+----------------------------------------------------------------

. lincom _cons+2.herd


 ( 1)  2.herd + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    68.18943    3.336189     20.44    0.000      61.64556     74.73329
-------------+----------------------------------------------------------------

. tabstat wpc, statistics(mean sd semean count) by(herd)

Summary for variables: wpc
     by categories of: herd (Herd Number)

herd   |      mean        sd  se(mean)         N
---------+----------------------------------------
1   |  76.82721  54.25425  3.289647       272
2   |  68.18943  51.42065  3.412909       227
3   |  87.90994  56.03043  3.122454       322
4   |  55.49261  46.33257  3.251909       203
5   |  54.78295  43.38904  3.820192       129
106   |   60.5336  43.14395  2.712437       253
119   |  59.28571  49.69417  3.833989       168
---------+----------------------------------------
Total   |  68.79924  51.59928  1.300593      1574
---------+----------------------------------------

.
. * 1b)
. regress wpc milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(1, 1534)      =      1.50
       Model |  3998.19335         1  3998.19335   Prob > F        =    0.2215
    Residual |  4100366.46     1,534  2672.98987   R-squared       =    0.0010
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.701

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     milk120 |   -.0023117    .0018902     -1.22    0.222     -.0060194     .0013959
       _cons |    76.41752    6.218695     12.29    0.000      64.21947     88.61556
-------------+----------------------------------------------------------------

. margins , at( milk120=(1200(1000)5600) )

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4200


5._at  : milk120         =        5200


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    73.64342    4.030901     18.27    0.000      65.73676     81.55008
          2  |    71.33167    2.328467     30.63    0.000      66.76436     75.89898
          3  |    69.01992    1.319485     52.31    0.000      66.43174     71.60811
          4  |    66.70818     2.28167     29.24    0.000      62.23265      71.1837
          5  |    64.39643     3.97702     16.19    0.000      56.59546      72.1974
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: milk120


. lincom _cons+milk120*3200


 ( 1)  3200*milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    69.01992    1.319485     52.31    0.000      66.43174     71.60811
-------------+----------------------------------------------------------------

. sum milk120, d

       Milk volume (l) in first 120 days of lactation
-------------------------------------------------------------
      Percentiles      Smallest
 1%       1698.5         1110.2
 5%       2077.2         1397.7
10%       2298.2         1461.5       Obs               1,536
25%      2731.95           1467       Sum of Wgt.       1,536

50%      3215.25                      Mean           3215.096
                        Largest       Std. Dev.      698.1316
75%       3682.1         5181.8
90%       4080.7           5278       Variance       487387.7
95%       4403.3         5399.9       Skewness       .1101838
99%       4904.4         5630.3       Kurtosis       2.845637

.
. * 1c)
. regress wpc c.milk120##c.milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(2, 1533)      =      2.84
       Model |   15168.437         2  7584.21849   Prob > F        =    0.0585
    Residual |  4089196.22     1,533  2667.44698   R-squared       =    0.0037
-------------+----------------------------------   Adj R-squared   =    0.0024
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.647

--------------------+----------------------------------------------------------------
                 wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
--------------------+----------------------------------------------------------------
            milk120 |   -.0289184    .0131383     -2.20    0.028     -.0546894    -.0031475
                    |
c.milk120#c.milk120 |    4.09e-06    2.00e-06      2.05    0.041      1.70e-07     8.01e-06
                    |
              _cons |    117.7029    21.10975      5.58    0.000      76.29586     159.1099
--------------------+----------------------------------------------------------------

. margins , at( milk120=(1200(1000)5600) )

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4200


5._at  : milk120         =        5200


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    88.88877    8.468555     10.50    0.000       72.2776        105.5
          2  |    73.87253    2.636701     28.02    0.000      68.70061     79.04445
          3  |    67.03403    1.636826     40.95    0.000      63.82338     70.24469
          4  |    68.37329     2.42019     28.25    0.000      63.62605     73.12052
          5  |    77.89029    7.698409     10.12    0.000      62.78976     92.99082
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: milk120


. lincom _cons+milk120*3200+c.milk120#c.milk120*10240000


 ( 1)  3200*milk120 + 1.02e+07*c.milk120#c.milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    67.03403    1.636826     40.95    0.000      63.82338     70.24469
-------------+----------------------------------------------------------------

.
. * 1d)
. generate lnwpc=ln(wpc)

. regress lnwpc milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(1, 1534)      =      2.51
       Model |  1.45963987         1  1.45963987   Prob > F        =    0.1130
    Residual |  890.377121     1,534  .580428371   R-squared       =    0.0016
-------------+----------------------------------   Adj R-squared   =    0.0010
       Total |  891.836761     1,535  .581001147   Root MSE        =    .76186

-------------+----------------------------------------------------------------
        lnwpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
     milk120 |   -.0000442    .0000279     -1.59    0.113     -.0000988     .0000105
       _cons |    4.103887    .0916379     44.78    0.000      3.924139     4.283636
-------------+----------------------------------------------------------------

. margins , at( milk120=(1200(1000)5600))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4200


5._at  : milk120         =        5200


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    4.050883    .0593988     68.20    0.000      3.934371     4.167394
          2  |    4.006712     .034312    116.77    0.000      3.939409     4.074016
          3  |    3.962542    .0194438    203.80    0.000      3.924403     4.000681
          4  |    3.918372    .0336224    116.54    0.000      3.852421     3.984322
          5  |    3.874201    .0586048     66.11    0.000      3.759247     3.989155
-------------+----------------------------------------------------------------

. margins , at( milk120=(1200(1000)5600)) expression(exp(predict(xb)))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : exp(predict(xb))


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4200


5._at  : milk120         =        5200


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    57.44816    3.412352     16.84    0.000      50.76007     64.13624
          2  |    54.96587    1.885987     29.14    0.000       51.2694     58.66234
          3  |    52.59085    1.022564     51.43    0.000      50.58666     54.59503
          4  |    50.31844    1.691826     29.74    0.000      47.00252     53.63436
          5  |    48.14423    2.821485     17.06    0.000      42.61422     53.67424
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: milk120


. di exp(3.934371) "  ,  " exp(4.167394) /* correct CI for 1200 */
51.129979  ,  64.547023

.
. * 2a)
. regress wpc i.rp i.vag_disch

      Source |       SS           df       MS      Number of obs   =     1,574
-------------+----------------------------------   F(2, 1571)      =      4.65
       Model |  24663.1482         2  12331.5741   Prob > F        =    0.0097
    Residual |  4163427.41     1,571  2650.17658   R-squared       =    0.0059
-------------+----------------------------------   Adj R-squared   =    0.0046
       Total |  4188090.56     1,573  2662.48605   Root MSE        =     51.48

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
        yes  |     10.2397    4.540774      2.26    0.024      1.333087     19.14632
             |
   vag_disch |
        yes  |    9.066942      5.9819      1.52    0.130     -2.666406     20.80029
       _cons |    67.35756    1.381095     48.77    0.000      64.64857     70.06654
-------------+----------------------------------------------------------------

. margins rp vag_disch

Predictive margins                              Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
         no  |    67.82991    1.366926     49.62    0.000      65.14872     70.51111
        yes  |    78.06962    4.310854     18.11    0.000      69.61398     86.52525
             |
   vag_disch |
         no  |    68.32688     1.33448     51.20    0.000      65.70933     70.94443
        yes  |    77.39382    5.816838     13.31    0.000      65.98424     88.80341
-------------+----------------------------------------------------------------

. table rp vag_disch, row col

----------+--------------------
Retained  |
placenta  |  Vaginal discharge
at        |      observed
calving   |    no    yes  Total
----------+--------------------
       no | 1,373     52  1,425
      yes |   119     30    149
          |
    Total | 1,492     82  1,574
----------+--------------------

. lincom _cons+1.rp+1.vag_disch*82/1574 /* rp=1 */


 ( 1)  1.rp + .0520966*1.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    78.06962    4.310854     18.11    0.000      69.61398     86.52525
-------------+----------------------------------------------------------------

. margins rp vag_disch, asbalanced

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


at  : rp                 (asbalanced)


     vag_disch          (asbalanced)


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
         no  |    71.89103    3.089892     23.27    0.000      65.83028     77.95177
        yes  |    82.13073    4.580191     17.93    0.000       73.1468     91.11466
             |
   vag_disch |
         no  |    72.47741    2.327566     31.14    0.000      67.91195     77.04287
        yes  |    81.54435     5.71754     14.26    0.000      70.32954     92.75916
-------------+----------------------------------------------------------------

. lincom _cons+1.rp+1.vag_disch*0.5 /* rp=1 */


 ( 1)  1.rp + .5*1.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    82.13073    4.580191     17.93    0.000       73.1468     91.11466
-------------+----------------------------------------------------------------

. margins rp, over(vag_disch) /* same as: at(vag_disch=(0 1)) */

Predictive margins                              Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


over  : vag_disch


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
vag_disch#rp |
      no#no  |    67.35756    1.381095     48.77    0.000      64.64857     70.06654
     no#yes  |    77.59726    4.386003     17.69    0.000      68.99422      86.2003
     yes#no  |     76.4245    5.922753     12.90    0.000      64.80717     88.04183
    yes#yes  |     86.6642    6.372663     13.60    0.000      74.16438     99.16402
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: rp vag_disch


. lincom _cons+1.rp+1.vag_disch /* rp=1, vag_disch=1 */


 ( 1)  1.rp + 1.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |     86.6642    6.372663     13.60    0.000      74.16438     99.16402
-------------+----------------------------------------------------------------

.
. * 2b)
. regress wpc rp##vag_disch

      Source |       SS           df       MS      Number of obs   =     1,574
-------------+----------------------------------   F(3, 1570)      =      4.53
       Model |  35915.9774         3  11971.9925   Prob > F        =    0.0036
    Residual |  4152174.58     1,570  2644.69719   R-squared       =    0.0086
-------------+----------------------------------   Adj R-squared   =    0.0067
       Total |  4188090.56     1,573  2662.48605   Root MSE        =    51.427

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
        yes  |    6.339794    4.914322      1.29    0.197     -3.299531     15.97912
             |
   vag_disch |
        yes  |    .5429296    7.265382      0.07    0.940     -13.70794      14.7938
             |
rp#vag_disch |
    yes#yes  |    26.34867    12.77367      2.06    0.039      1.293414     51.40392
             |
       _cons |    67.66861    1.387883     48.76    0.000      64.94631     70.39091
-------------+----------------------------------------------------------------

. margins rp#vag_disch

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
rp#vag_disch |
      no#no  |    67.66861    1.387883     48.76    0.000      64.94631     70.39091
     no#yes  |    68.21154    7.131589      9.56    0.000       54.2231     82.19998
     yes#no  |     74.0084     4.71427     15.70    0.000      64.76147     83.25533
    yes#yes  |       100.9    9.389173     10.75    0.000      82.48336     119.3166
-------------+----------------------------------------------------------------

. marginsplot, noci /* this is the interaction plot! */


Variables that uniquely identify margins: rp vag_disch


. marginsplot, noci x(vag_disch) /* controlling variable on x */


Variables that uniquely identify margins: rp vag_disch


. marginsplot, noci x(rp) /* same as default */


Variables that uniquely identify margins: rp vag_disch


. table rp vag_disch, contents(mean wpc) row col

----------+-----------------------------
Retained  |
placenta  |
at        |  Vaginal discharge observed
calving   |       no       yes     Total
----------+-----------------------------
       no | 67.66861  68.21154  67.68842
      yes |  74.0084     100.9  79.42282
          |
    Total | 68.17426  80.17073  68.79924
----------+-----------------------------

. margins rp

Predictive margins                              Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
         no  |    67.69689    1.367034     49.52    0.000      65.01549      70.3783
        yes  |    75.40936    4.495364     16.77    0.000      66.59181     84.22691
-------------+----------------------------------------------------------------

. lincom _cons+1.rp+(1.vag_disch+1.rp#1.vag_disch)*82/1574  /* rp=1 */


 ( 1)  1.rp + .0520966*1.vag_disch + .0520966*1.rp#1.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    75.40936    4.495364     16.77    0.000      66.59181     84.22691
-------------+----------------------------------------------------------------

. margins rp, asbalanced

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


at  : rp                 (asbalanced)


     vag_disch          (asbalanced)


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
          rp |
         no  |    67.94007    3.632691     18.70    0.000      60.81464     75.06551
        yes  |     87.4542    5.253116     16.65    0.000      77.15034     97.75806
-------------+----------------------------------------------------------------

. lincom _cons+1.rp+(1.vag_disch+1.rp#1.vag_disch)*0.5  /* rp=1 */


 ( 1)  1.rp + .5*1.vag_disch + .5*1.rp#1.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |     87.4542    5.253116     16.65    0.000      77.15034     97.75806
-------------+----------------------------------------------------------------

.
. * 2c)
. regress wpc i.dyst milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(2, 1533)      =      1.01
       Model |   5380.7883         2  2690.39415   Prob > F        =    0.3658
    Residual |  4098983.87     1,533  2673.83162   R-squared       =    0.0013
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.709

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dyst |
        yes  |    4.004176    5.568429      0.72    0.472     -6.918368     14.92672
     milk120 |   -.0022375    .0018933     -1.18    0.237     -.0059512     .0014763
       _cons |    75.93882    6.255198     12.14    0.000      63.66917     88.20847
-------------+----------------------------------------------------------------

. margins dyst, at( milk120=(1200 2200 3200 4300 5500))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4300


5._at  : milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
    _at#dyst |
       1#no  |    73.25388    4.067768     18.01    0.000       65.2749     81.23286
      1#yes  |    77.25805    6.443687     11.99    0.000      64.61868     89.89743
       2#no  |    71.01642    2.369738     29.97    0.000      66.36815     75.66469
      2#yes  |     75.0206    5.633884     13.32    0.000      63.96966     86.07153
       3#no  |    68.77897     1.36157     50.51    0.000      66.10823      71.4497
      3#yes  |    72.78314    5.397173     13.49    0.000      62.19652     83.36977
       4#no  |    66.31777    2.448762     27.08    0.000      61.51449     71.12105
      4#yes  |    70.32194    5.876881     11.97    0.000      58.79437     81.84952
       5#no  |    63.63282    4.517657     14.09    0.000      54.77138     72.49426
      5#yes  |      67.637    7.094453      9.53    0.000      53.72114     81.55286
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: milk120 dyst


. lincom _cons+1.dyst+milk120*3200 /* dyst=1, milk120=3200 */


 ( 1)  1.dyst + 3200*milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    72.78314    5.397173     13.49    0.000      62.19652     83.36977
-------------+----------------------------------------------------------------

. margin dyst, atmeans

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


at  : 0.dyst          =    .9401042 (mean)


     1.dyst          =    .0598958 (mean)


     milk120         =    3215.096 (mean)


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dyst |
         no  |    68.74519    1.360888     50.51    0.000      66.07579     71.41459
        yes  |    72.74937     5.39861     13.48    0.000      62.15993     83.33881
-------------+----------------------------------------------------------------

. lincom _cons+1.dyst+milk120*3215.096


 ( 1)  1.dyst + 3215.096*milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    72.74937     5.39861     13.48    0.000      62.15993     83.33881
-------------+----------------------------------------------------------------

. * interaction model
. regress wpc dyst##c.milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(3, 1532)      =      3.83
       Model |  30572.8757         3  10190.9586   Prob > F        =    0.0095
    Residual |  4073791.78     1,532  2659.13302   R-squared       =    0.0074
-------------+----------------------------------   Adj R-squared   =    0.0055
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.567

---------------+----------------------------------------------------------------
            wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
---------------+----------------------------------------------------------------
          dyst |
          yes  |   -85.48838    29.60089     -2.89    0.004     -143.5509    -27.42583
       milk120 |   -.0034465    .0019285     -1.79    0.074     -.0072294     .0003363
               |
dyst#c.milk120 |
          yes  |    .0291424    .0094681      3.08    0.002      .0105706     .0477142
               |
         _cons |    79.83774    6.365297     12.54    0.000      67.35213     92.32336
---------------+----------------------------------------------------------------

. margins dyst, at( milk120=(1200 2200 3200 4300 5500))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : milk120         =        1200


2._at  : milk120         =        2200


3._at  : milk120         =        3200


4._at  : milk120         =        4300


5._at  : milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
    _at#dyst |
       1#no  |    75.70191    4.133806     18.31    0.000      67.59339     83.81042
      1#yes  |    25.18437    18.09754      1.39    0.164      -10.3142     60.68295
       2#no  |    72.25537    2.397252     30.14    0.000      67.55313     76.95762
      2#yes  |    50.88022    9.647734      5.27    0.000      31.95606     69.80438
       3#no  |    68.80884    1.357857     50.67    0.000      66.14539      71.4723
      3#yes  |    76.57606    5.521583     13.87    0.000       65.7454     87.40672
       4#no  |    65.01766    2.478284     26.23    0.000      60.15647     69.87885
      4#yes  |    104.8415     12.6541      8.29    0.000       80.0203     129.6627
       5#no  |    60.88182    4.593023     13.26    0.000      51.87254      69.8911
      5#yes  |    135.6765    23.21002      5.85    0.000      90.14974     181.2033
-------------+----------------------------------------------------------------

. marginsplot, noci /* this is the interaction plot! */


Variables that uniquely identify margins: milk120 dyst


. lincom _cons+1.dyst+(c.milk120+1.dyst#c.milk120)*3200 /* dyst=1, milk120=3200 */


 ( 1)  1.dyst + 3200*milk120 + 3200*1.dyst#c.milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    76.57606    5.521583     13.87    0.000       65.7454     87.40672
-------------+----------------------------------------------------------------

.
. * 2d)
. regress wpc parity milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(2, 1533)      =      3.18
       Model |  16982.1525         2  8491.07626   Prob > F        =    0.0417
    Residual |   4087382.5     1,533  2666.26386   R-squared       =    0.0041
-------------+----------------------------------   Adj R-squared   =    0.0028
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.636

-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
      parity |    2.104084    .9534787      2.21    0.027      .2338231     3.974344
     milk120 |   -.0040343    .0020428     -1.97    0.048     -.0080413    -.0000273
       _cons |    76.19823    6.211661     12.27    0.000      64.01398     88.38248
-------------+----------------------------------------------------------------

. tabulate parity

  Lactation |
     number |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        417       26.49       26.49
          2 |        374       23.76       50.25
          3 |        319       20.27       70.52
          4 |        222       14.10       84.63
          5 |        169       10.74       95.36
          6 |         69        4.38       99.75
          7 |          4        0.25      100.00
------------+-----------------------------------
      Total |      1,574      100.00

. margins , at( parity=(1(1)6) milk120=(1200 2200 3200 4300 5500))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : parity          =           1


     milk120         =        1200


2._at  : parity          =           1


     milk120         =        2200


3._at  : parity          =           1


     milk120         =        3200


4._at  : parity          =           1


     milk120         =        4300


5._at  : parity          =           1


     milk120         =        5500


6._at  : parity          =           2


     milk120         =        1200


7._at  : parity          =           2


     milk120         =        2200


8._at  : parity          =           2


     milk120         =        3200


9._at  : parity          =           2


     milk120         =        4300


10._at  : parity          =           2


     milk120         =        5500


11._at  : parity          =           3


     milk120         =        1200


12._at  : parity          =           3


     milk120         =        2200


13._at  : parity          =           3


     milk120         =        3200


14._at  : parity          =           3


     milk120         =        4300


15._at  : parity          =           3


     milk120         =        5500


16._at  : parity          =           4


     milk120         =        1200


17._at  : parity          =           4


     milk120         =        2200


18._at  : parity          =           4


     milk120         =        3200


19._at  : parity          =           4


     milk120         =        4300


20._at  : parity          =           4


     milk120         =        5500


21._at  : parity          =           5


     milk120         =        1200


22._at  : parity          =           5


     milk120         =        2200


23._at  : parity          =           5


     milk120         =        3200


24._at  : parity          =           5


     milk120         =        4300


25._at  : parity          =           5


     milk120         =        5500


26._at  : parity          =           6


     milk120         =        1200


27._at  : parity          =           6


     milk120         =        2200


28._at  : parity          =           6


     milk120         =        3200


29._at  : parity          =           6


     milk120         =        4300


30._at  : parity          =           6


     milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    73.46115    4.026674     18.24    0.000      65.56278     81.35952
          2  |    69.42685    2.480564     27.99    0.000      64.56119     74.29251
          3  |    65.39255    2.106807     31.04    0.000      61.26002     69.52508
          4  |    60.95481    3.491797     17.46    0.000      54.10561     67.80402
          5  |    56.11365    5.671817      9.89    0.000      44.98831     67.23899
          6  |    75.56523    4.118946     18.35    0.000      67.48587      83.6446
          7  |    71.53093    2.327288     30.74    0.000      66.96593     76.09594
          8  |    67.49663     1.48767     45.37    0.000      64.57855     70.41471
          9  |     63.0589    2.886134     21.85    0.000      57.39771     68.72009
         10  |    58.21774    5.149795     11.30    0.000      48.11635     68.31912
         11  |    77.66932    4.419907     17.57    0.000      68.99962     86.33902
         12  |    73.63502    2.549035     28.89    0.000      68.63505     78.63498
         13  |    69.60071    1.343849     51.79    0.000      66.96474     72.23669
         14  |    65.16298    2.507018     25.99    0.000      60.24543     70.08053
         15  |    60.32182     4.76335     12.66    0.000      50.97845     69.66519
         16  |     79.7734    4.891183     16.31    0.000      70.17928     89.36752
         17  |     75.7391    3.065475     24.71    0.000      69.72613     81.75207
         18  |     71.7048    1.793583     39.98    0.000      68.18666     75.22293
         19  |    67.26707    2.461453     27.33    0.000      62.43889     72.09524
         20  |     62.4259    4.547181     13.73    0.000      53.50655     71.34526
         21  |    81.87749    5.489082     14.92    0.000      71.11058     92.64439
         22  |    77.84318    3.756985     20.72    0.000      70.47381     85.21256
         23  |    73.80888    2.538936     29.07    0.000      68.82873     78.78904
         24  |    69.37115    2.765973     25.08    0.000      63.94566     74.79664
         25  |    64.52999    4.525755     14.26    0.000      55.65266     73.40731
         26  |    83.98157    6.176942     13.60    0.000      71.86542     96.09772
         27  |    79.94727    4.544335     17.59    0.000       71.0335     88.86104
         28  |    75.91296    3.390235     22.39    0.000      69.26298     82.56295
         29  |    71.47523    3.325764     21.49    0.000       64.9517     77.99876
         30  |    66.63407    4.701734     14.17    0.000      57.41156     75.85658
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: parity milk120


. margins , at( milk120=(1200 2200 3200 4300 5500) parity=(1(1)6) )

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : parity          =           1


     milk120         =        1200


2._at  : parity          =           1


     milk120         =        2200


3._at  : parity          =           1


     milk120         =        3200


4._at  : parity          =           1


     milk120         =        4300


5._at  : parity          =           1


     milk120         =        5500


6._at  : parity          =           2


     milk120         =        1200


7._at  : parity          =           2


     milk120         =        2200


8._at  : parity          =           2


     milk120         =        3200


9._at  : parity          =           2


     milk120         =        4300


10._at  : parity          =           2


     milk120         =        5500


11._at  : parity          =           3


     milk120         =        1200


12._at  : parity          =           3


     milk120         =        2200


13._at  : parity          =           3


     milk120         =        3200


14._at  : parity          =           3


     milk120         =        4300


15._at  : parity          =           3


     milk120         =        5500


16._at  : parity          =           4


     milk120         =        1200


17._at  : parity          =           4


     milk120         =        2200


18._at  : parity          =           4


     milk120         =        3200


19._at  : parity          =           4


     milk120         =        4300


20._at  : parity          =           4


     milk120         =        5500


21._at  : parity          =           5


     milk120         =        1200


22._at  : parity          =           5


     milk120         =        2200


23._at  : parity          =           5


     milk120         =        3200


24._at  : parity          =           5


     milk120         =        4300


25._at  : parity          =           5


     milk120         =        5500


26._at  : parity          =           6


     milk120         =        1200


27._at  : parity          =           6


     milk120         =        2200


28._at  : parity          =           6


     milk120         =        3200


29._at  : parity          =           6


     milk120         =        4300


30._at  : parity          =           6


     milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    73.46115    4.026674     18.24    0.000      65.56278     81.35952
          2  |    69.42685    2.480564     27.99    0.000      64.56119     74.29251
          3  |    65.39255    2.106807     31.04    0.000      61.26002     69.52508
          4  |    60.95481    3.491797     17.46    0.000      54.10561     67.80402
          5  |    56.11365    5.671817      9.89    0.000      44.98831     67.23899
          6  |    75.56523    4.118946     18.35    0.000      67.48587      83.6446
          7  |    71.53093    2.327288     30.74    0.000      66.96593     76.09594
          8  |    67.49663     1.48767     45.37    0.000      64.57855     70.41471
          9  |     63.0589    2.886134     21.85    0.000      57.39771     68.72009
         10  |    58.21774    5.149795     11.30    0.000      48.11635     68.31912
         11  |    77.66932    4.419907     17.57    0.000      68.99962     86.33902
         12  |    73.63502    2.549035     28.89    0.000      68.63505     78.63498
         13  |    69.60071    1.343849     51.79    0.000      66.96474     72.23669
         14  |    65.16298    2.507018     25.99    0.000      60.24543     70.08053
         15  |    60.32182     4.76335     12.66    0.000      50.97845     69.66519
         16  |     79.7734    4.891183     16.31    0.000      70.17928     89.36752
         17  |     75.7391    3.065475     24.71    0.000      69.72613     81.75207
         18  |     71.7048    1.793583     39.98    0.000      68.18666     75.22293
         19  |    67.26707    2.461453     27.33    0.000      62.43889     72.09524
         20  |     62.4259    4.547181     13.73    0.000      53.50655     71.34526
         21  |    81.87749    5.489082     14.92    0.000      71.11058     92.64439
         22  |    77.84318    3.756985     20.72    0.000      70.47381     85.21256
         23  |    73.80888    2.538936     29.07    0.000      68.82873     78.78904
         24  |    69.37115    2.765973     25.08    0.000      63.94566     74.79664
         25  |    64.52999    4.525755     14.26    0.000      55.65266     73.40731
         26  |    83.98157    6.176942     13.60    0.000      71.86542     96.09772
         27  |    79.94727    4.544335     17.59    0.000       71.0335     88.86104
         28  |    75.91296    3.390235     22.39    0.000      69.26298     82.56295
         29  |    71.47523    3.325764     21.49    0.000       64.9517     77.99876
         30  |    66.63407    4.701734     14.17    0.000      57.41156     75.85658
-------------+----------------------------------------------------------------

. marginsplot, noci /* changing roles in plot */


Variables that uniquely identify margins: milk120 parity


. margins , at( milk120=(1200 2200 3200 4300 5500) (median)parity)

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : parity          =           2 (median)


     milk120         =        1200


2._at  : parity          =           2 (median)


     milk120         =        2200


3._at  : parity          =           2 (median)


     milk120         =        3200


4._at  : parity          =           2 (median)


     milk120         =        4300


5._at  : parity          =           2 (median)


     milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    75.56523    4.118946     18.35    0.000      67.48587      83.6446
          2  |    71.53093    2.327288     30.74    0.000      66.96593     76.09594
          3  |    67.49663     1.48767     45.37    0.000      64.57855     70.41471
          4  |     63.0589    2.886134     21.85    0.000      57.39771     68.72009
          5  |    58.21774    5.149795     11.30    0.000      48.11635     68.31912
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: milk120


. lincom _cons+milk120*1200+parity*2 /* milk120=1200, parity=2 */


 ( 1)  2*parity + 1200*milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    75.56523    4.118946     18.35    0.000      67.48587      83.6446
-------------+----------------------------------------------------------------

. margins, atmeans

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


at  : parity          =    2.736328 (mean)


     milk120         =    3215.096 (mean)


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
       _cons |    68.98503    1.317516     52.36    0.000       66.4007     71.56935
-------------+----------------------------------------------------------------

. lincom _cons+milk120*3215.096+parity*2.736328 /* both at means */


 ( 1)  2.736328*parity + 3215.096*milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    68.98502    1.317516     52.36    0.000       66.4007     71.56935
-------------+----------------------------------------------------------------

. * interaction model
. regress wpc c.parity##c.milk120

      Source |       SS           df       MS      Number of obs   =     1,536
-------------+----------------------------------   F(3, 1532)      =      2.26
       Model |  18084.1915         3  6028.06382   Prob > F        =    0.0797
    Residual |  4086280.46     1,532   2667.2849   R-squared       =    0.0044
-------------+----------------------------------   Adj R-squared   =    0.0025
       Total |  4104364.66     1,535   2673.8532   Root MSE        =    51.646

-------------------+----------------------------------------------------------------
                wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------------+----------------------------------------------------------------
            parity |     4.88999    4.437817      1.10    0.271     -3.814849     13.59483
           milk120 |   -.0016664    .0042125     -0.40    0.692     -.0099293     .0065965
                   |
c.parity#c.milk120 |   -.0008764    .0013635     -0.64    0.520      -.003551     .0017981
                   |
             _cons |    69.02201    12.77659      5.40    0.000      43.96056     94.08347
-------------------+----------------------------------------------------------------

. margins , at( parity=(1(1)6) milk120=(1200 2200 3200 4300 5500))

Adjusted predictions                            Number of obs     =      1,536
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : parity          =           1


     milk120         =        1200


2._at  : parity          =           1


     milk120         =        2200


3._at  : parity          =           1


     milk120         =        3200


4._at  : parity          =           1


     milk120         =        4300


5._at  : parity          =           1


     milk120         =        5500


6._at  : parity          =           2


     milk120         =        1200


7._at  : parity          =           2


     milk120         =        2200


8._at  : parity          =           2


     milk120         =        3200


9._at  : parity          =           2


     milk120         =        4300


10._at  : parity          =           2


     milk120         =        5500


11._at  : parity          =           3


     milk120         =        1200


12._at  : parity          =           3


     milk120         =        2200


13._at  : parity          =           3


     milk120         =        3200


14._at  : parity          =           3


     milk120         =        4300


15._at  : parity          =           3


     milk120         =        5500


16._at  : parity          =           4


     milk120         =        1200


17._at  : parity          =           4


     milk120         =        2200


18._at  : parity          =           4


     milk120         =        3200


19._at  : parity          =           4


     milk120         =        4300


20._at  : parity          =           4


     milk120         =        5500


21._at  : parity          =           5


     milk120         =        1200


22._at  : parity          =           5


     milk120         =        2200


23._at  : parity          =           5


     milk120         =        3200


24._at  : parity          =           5


     milk120         =        4300


25._at  : parity          =           5


     milk120         =        5500


26._at  : parity          =           6


     milk120         =        1200


27._at  : parity          =           6


     milk120         =        2200


28._at  : parity          =           6


     milk120         =        3200


29._at  : parity          =           6


     milk120         =        4300


30._at  : parity          =           6


     milk120         =        5500


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     70.8606    5.708639     12.41    0.000      59.66303     82.05818
          2  |    68.31777    3.022034     22.61    0.000      62.39001     74.24553
          3  |    65.77493    2.189574     30.04    0.000      61.48005     70.06981
          4  |    62.97782    4.701336     13.40    0.000      53.75608     72.19955
          5  |    59.92642    8.207704      7.30    0.000      43.82689     76.02594
          6  |    74.69887    4.334614     17.23    0.000      66.19647     83.20127
          7  |     71.2796    2.360345     30.20    0.000      66.64975     75.90945
          8  |    67.86033    1.591905     42.63    0.000      64.73779     70.98287
          9  |    64.09913    3.309373     19.37    0.000      57.60775     70.59051
         10  |    59.99601    5.846725     10.26    0.000      48.52758     71.46444
         11  |    78.53714    4.622317     16.99    0.000       69.4704     87.60387
         12  |    74.24143    2.718476     27.31    0.000       68.9091     79.57376
         13  |    69.94572    1.447313     48.33    0.000       67.1068     72.78465
         14  |    65.22045    2.509091     25.99    0.000      60.29883     70.14206
         15  |     60.0656    4.780908     12.56    0.000      50.68778     69.44341
         16  |     82.3754    6.349754     12.97    0.000      69.92027     94.83053
         17  |    77.20326    3.819598     20.21    0.000      69.71107     84.69545
         18  |    72.03112    1.864377     38.64    0.000      68.37412     75.68812
         19  |    66.34176    2.851897     23.26    0.000      60.74773      71.9358
         20  |    60.13519    5.777981     10.41    0.000       48.8016     71.46878
         21  |    86.21367    8.697676      9.91    0.000      69.15306     103.2743
         22  |    80.16509    5.212379     15.38    0.000      69.94094     90.38924
         23  |    74.11651    2.584128     28.68    0.000      69.04771     79.18531
         24  |    67.46308    4.057744     16.63    0.000      59.50376      75.4224
         25  |    60.20478    8.109756      7.42    0.000      44.29739     76.11218
         26  |    90.05194    11.28523      7.98    0.000      67.91581     112.1881
         27  |    83.12692    6.717797     12.37    0.000      69.94987     96.30397
         28  |    76.20191     3.42055     22.28    0.000      69.49245     82.91136
         29  |    68.58439    5.593876     12.26    0.000      57.61193     79.55686
         30  |    60.27437    10.95474      5.50    0.000       38.7865     81.76224
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: parity milk120


. lincom _cons+milk120*1200+parity*2+milk120#c.parity*2400 /* milk120=1200, parity=2 */


 ( 1)  2*parity + 1200*milk120 + 2400*c.parity#c.milk120 + _cons = 0


-------------+----------------------------------------------------------------
          wpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    74.69887    4.334614     17.23    0.000      66.19647     83.20127
-------------+----------------------------------------------------------------

.
. * VER Ex. 14.16: estimates on sqrt-scale and backtransformed
. gen calv_mth=month(calv_dt)

. gen aut_calv=(calv_mth>=2 & calv_mth<=7) if !missing(calv_mth)

. gen rootwpc=sqrt(wpc)

. regress rootwpc aut_calv c.herd_size##c.herd_size parity twin dyst rp##vag_disch

      Source |       SS           df       MS      Number of obs   =     1,574
-------------+----------------------------------   F(9, 1564)      =     16.19
       Model |  1133.93223         9   125.99247   Prob > F        =    0.0000
    Residual |  12172.3346     1,564  7.78282264   R-squared       =    0.0852
-------------+----------------------------------   Adj R-squared   =    0.0800
       Total |  13306.2668     1,573  8.45916519   Root MSE        =    2.7898

------------------------+----------------------------------------------------------------
                 rootwpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
------------------------+----------------------------------------------------------------
               aut_calv |   -.5136529    .1419214     -3.62    0.000     -.7920293    -.2352766
              herd_size |   -.0232678    .0084168     -2.76    0.006     -.0397771    -.0067585
                        |
c.herd_size#c.herd_size |    .0000709    .0000174      4.07    0.000      .0000367      .000105
                        |
                 parity |     .058304    .0480002      1.21    0.225     -.0358476     .1524556
                   twin |    1.385453    .5505819      2.52    0.012      .3054964     2.465409
                   dyst |    .5422828    .3054896      1.78    0.076     -.0569296     1.141495
                        |
                     rp |
                   yes  |    .3894596    .2691054      1.45    0.148     -.1383858      .917305
                        |
              vag_disch |
                   yes  |   -.0132839    .4004945     -0.03    0.974     -.7988467     .7722788
                        |
           rp#vag_disch |
               yes#yes  |    1.491019    .6999486      2.13    0.033      .1180826     2.863956
                        |
                  _cons |    8.834599    .9921073      8.90    0.000      6.888598      10.7806
------------------------+----------------------------------------------------------------

. *1) correct estimate and CI for rp=1, vag_disch=0, dyst=1
. lincom _cons+aut_calv*0+herd_size*251+c.herd_size#c.herd_size*63001+parity*1+twin*0+dyst*1+1.rp+0.vag_disch+1.rp#0.vag_disch


 ( 1)  251*herd_size + 63001*c.herd_size#c.herd_size + parity + dyst + 1.rp + 0b.vag_disch + 1o.rp#0b.vag_disch + _cons = 0


-------------+----------------------------------------------------------------
      rootwpc |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |    8.448395    .3770661     22.41    0.000      7.708787     9.188003
-------------+----------------------------------------------------------------

. *1) correct estimate and SE using margins command
. margins , over(rp vag_disch dyst) at(parity=1 aut_calv=0 herd_size=251 twin=0)

Predictive margins                              Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


over  : rp vag_disch dyst


at  : 0.rp#0.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#0.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#1.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#1.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#0.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#0.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#1.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#1.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


------------------+----------------------------------------------------------------
                  |            Delta-method
                  |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
------------------+----------------------------------------------------------------
rp#vag_disch#dyst |
        no#no#no  |    7.516653    .1473104     51.03    0.000      7.227706     7.805599
       no#no#yes  |    8.058936     .310164     25.98    0.000      7.450554     8.667317
       no#yes#no  |    7.503369    .4023829     18.65    0.000      6.714102     8.292636
      no#yes#yes  |    8.045652    .4553635     17.67    0.000      7.152464     8.938839
       yes#no#no  |    7.906112    .2801134     28.22    0.000      7.356675      8.45555
      yes#no#yes  |    8.448395    .3770661     22.41    0.000      7.708787     9.188003
      yes#yes#no  |    9.383847    .5287705     17.75    0.000      8.346674     10.42102
     yes#yes#yes  |     9.92613    .5925179     16.75    0.000      8.763917     11.08834
------------------+----------------------------------------------------------------

. *1) correct backtransformed estimate, but inappropriate CI
. margins , over(rp vag_disch dyst) at(parity=1 aut_calv=0 herd_size=251 twin=0) expr(predict(xb)^2)

Predictive margins                              Number of obs     =      1,574
 Model VCE   : OLS


Expression  : predict(xb)^2


over  : rp vag_disch dyst


at  : 0.rp#0.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#0.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#1.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     0.rp#1.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#0.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#0.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#1.vag_disch#0.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


     1.rp#1.vag_disch#1.dyst


         aut_calv        =           0


         herd_size       =         251


         parity          =           1


         twin            =           0


------------------+----------------------------------------------------------------
                  |            Delta-method
                  |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
------------------+----------------------------------------------------------------
rp#vag_disch#dyst |
        no#no#no  |    56.50007    2.214563     25.51    0.000       52.1596     60.84053
       no#no#yes  |    64.94644    4.999184     12.99    0.000      55.14822     74.74466
       no#yes#no  |    56.30054    6.038455      9.32    0.000      44.46539      68.1357
      no#yes#yes  |    64.73251    7.327391      8.83    0.000      50.37109     79.09393
       yes#no#no  |    62.50661    4.429216     14.11    0.000      53.82551     71.18771
      yes#no#yes  |    71.37538    6.371206     11.20    0.000      58.88805     83.86271
      yes#yes#no  |    88.05659    9.923803      8.87    0.000       68.6063     107.5069
     yes#yes#yes  |    98.52806    11.76282      8.38    0.000      75.47336     121.5828
------------------+----------------------------------------------------------------

. table rp dyst vag_disch, contents(count wpc )

----------+-----------------------------
Retained  |  Vaginal discharge observed
placenta  |   and Dystocia at calving
at        | ---- no ----    ---- yes ---
calving   |    no    yes       no    yes
----------+-----------------------------
       no | 1,307     66       40     12
      yes |   105     14       28      2
----------+-----------------------------

. table parity

----------+-----------
Lactation |
number    |      Freq.
----------+-----------
        1 |        417
        2 |        374
        3 |        319
        4 |        222
        5 |        169
        6 |         69
        7 |          4
----------+-----------

. table twin

----------+-----------
Twins     |
born      |      Freq.
----------+-----------
       no |      1,547
      yes |         27
----------+-----------

. sum herd_size

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   herd_size |      1,574    251.0076    62.01692        125        333

. table aut_calv

----------+-----------
 aut_calv |      Freq.
----------+-----------
        0 |        831
        1 |        743
----------+-----------

. *3) correct estimate and SE using margins command
. margins , at(parity=1 aut_calv=0 herd_size=(125 185 201 235 263 294 333) twin=0 rp=0 dyst=0 vag_disch=0)

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : Linear prediction, predict()


1._at  : aut_calv        =           0


     herd_size       =         125


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


2._at  : aut_calv        =           0


     herd_size       =         185


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


3._at  : aut_calv        =           0


     herd_size       =         201


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


4._at  : aut_calv        =           0


     herd_size       =         235


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


5._at  : aut_calv        =           0


     herd_size       =         263


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


6._at  : aut_calv        =           0


     herd_size       =         294


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


7._at  : aut_calv        =           0


     herd_size       =         333


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    7.091544    .2559118     27.71    0.000      6.589578      7.59351
          2  |    7.013389    .1550613     45.23    0.000      6.709239     7.317539
          3  |    7.078708    .1514358     46.74    0.000      6.781669     7.375746
          4  |    7.337965    .1497933     48.99    0.000      7.044148     7.631782
          5  |    7.674476    .1445034     53.11    0.000      7.391035     7.957917
          6  |    8.176637    .1404317     58.23    0.000      7.901183     8.452091
          7  |    9.001823    .1805099     49.87    0.000      8.647756      9.35589
-------------+----------------------------------------------------------------

. table herd_size

----------+-----------
Herd size |      Freq.
----------+-----------
      125 |        129
      185 |        168
      201 |        203
      235 |        227
      263 |        253
      294 |        272
      333 |        322
----------+-----------

. *3) correct backtransformed estimate, but inappropriate CI
. margins , at(parity=1 aut_calv=0 herd_size=(125 185 201 235 263 294 333) twin=0 rp=0 dyst=0 vag_disch=0) expr(predict(xb)^2)

Adjusted predictions                            Number of obs     =      1,574
 Model VCE   : OLS


Expression  : predict(xb)^2


1._at  : aut_calv        =           0


     herd_size       =         125


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


2._at  : aut_calv        =           0


     herd_size       =         185


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


3._at  : aut_calv        =           0


     herd_size       =         201


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


4._at  : aut_calv        =           0


     herd_size       =         235


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


5._at  : aut_calv        =           0


     herd_size       =         263


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


6._at  : aut_calv        =           0


     herd_size       =         294


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


7._at  : aut_calv        =           0


     herd_size       =         333


     parity          =           1


     twin            =           0


     dyst            =           0


     rp              =           0


     vag_disch       =           0


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |       50.29    3.629619     13.86    0.000      43.17607     57.40392
          2  |    49.18762     2.17501     22.61    0.000      44.92468     53.45056
          3  |     50.1081    2.143939     23.37    0.000      45.90606     54.31015
          4  |    53.84573    2.198356     24.49    0.000      49.53703     58.15443
          5  |    58.89758    2.217976     26.55    0.000      54.55043     63.24473
          6  |    66.85739    2.296517     29.11    0.000       62.3563     71.35848
          7  |    81.03282    3.249837     24.93    0.000      74.66326     87.40238
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: herd_size


.
. * logistic regression for Nocardia data
. use nocardia.dta, clear

. * effects on probability scale
. logit casecont i.dneo i.dclox dcpct

Iteration 0:   log likelihood = -74.859896
Iteration 1:   log likelihood = -54.156164
Iteration 2:   log likelihood = -53.993934
Iteration 3:   log likelihood = -53.993656
Iteration 4:   log likelihood = -53.993656

Logistic regression                             Number of obs     =        108
                                                LR chi2(3)        =      41.73
                                                Prob > chi2       =     0.0000
Log likelihood = -53.993656                     Pseudo R2         =     0.2787

-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
        dneo |
        yes  |    2.212564    .5780423      3.83    0.000      1.079622     3.345506
             |
       dclox |
        yes  |   -1.412516    .5572076     -2.53    0.011     -2.504623    -.3204093
       dcpct |    .0226682    .0071871      3.15    0.002      .0085817     .0367547
       _cons |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
-------------+----------------------------------------------------------------

. predict phat
(option pr assumed; Pr(casecont))

. predict logitphat, xb

. * plots of predicted values - for demonstration only (not valid in case-control study)
. twoway (connected logitphat dcpct if dneo==1 & dclox==0, sort) (connected logitphat dcpct if dneo==0 & dclox==0, sort)

. twoway (connected phat dcpct if dneo==1 & dclox==0, sort) (connected phat dcpct if dneo==0 & dclox==0, sort)

. * incorrect plots (next 6 lines)
. twoway (connected logitphat dcpct, sort)

. twoway (lfit logitphat dcpct)

. twoway (lowess logitphat dcpct)

. twoway (connected phat dcpct, sort)

. twoway (lfit phat dcpct)

. twoway (lowess phat dcpct)

. * manually created prediction values and plots
. set obs 119


number of observations (_N)  was 108,
now 119


. replace dneo=0 in 109/119
(11 real changes made)

. replace dclox=0 in 109/119
(11 real changes made)

. replace dcpct=0+(_n-109)*10 in 109/119
(11 real changes made)

. capture drop phat logitphat

. predict phat
(option pr assumed; Pr(casecont))

. predict logitphat, xb

. twoway (connected logitphat dcpct in 109/119, sort)

. twoway (connected phat dcpct in 109/119, sort)

.
. * estimation and plots by margins and marginsplot commands
. margins, at(dcpct=(0(10)100) dneo=1 dclox=0) at(dcpct=(0(10)100) dneo=0 dclox=0) predict(xb)

Adjusted predictions                            Number of obs     =        108
 Model VCE   : OIM


Expression  : Linear prediction (log odds), predict(xb)


1._at  : dneo            =           1


     dclox           =           0


     dcpct           =           0


2._at  : dneo            =           1


     dclox           =           0


     dcpct           =          10


3._at  : dneo            =           1


     dclox           =           0


     dcpct           =          20


4._at  : dneo            =           1


     dclox           =           0


     dcpct           =          30


5._at  : dneo            =           1


     dclox           =           0


     dcpct           =          40


6._at  : dneo            =           1


     dclox           =           0


     dcpct           =          50


7._at  : dneo            =           1


     dclox           =           0


     dcpct           =          60


8._at  : dneo            =           1


     dclox           =           0


     dcpct           =          70


9._at  : dneo            =           1


     dclox           =           0


     dcpct           =          80


10._at  : dneo            =           1


     dclox           =           0


     dcpct           =          90


11._at  : dneo            =           1


     dclox           =           0


     dcpct           =         100


12._at  : dneo            =           0


     dclox           =           0


     dcpct           =           0


13._at  : dneo            =           0


     dclox           =           0


     dcpct           =          10


14._at  : dneo            =           0


     dclox           =           0


     dcpct           =          20


15._at  : dneo            =           0


     dclox           =           0


     dcpct           =          30


16._at  : dneo            =           0


     dclox           =           0


     dcpct           =          40


17._at  : dneo            =           0


     dclox           =           0


     dcpct           =          50


18._at  : dneo            =           0


     dclox           =           0


     dcpct           =          60


19._at  : dneo            =           0


     dclox           =           0


     dcpct           =          70


20._at  : dneo            =           0


     dclox           =           0


     dcpct           =          80


21._at  : dneo            =           0


     dclox           =           0


     dcpct           =          90


22._at  : dneo            =           0


     dclox           =           0


     dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   -.7717085    .5987154     -1.29    0.197     -1.945169     .4017521
          2  |    -.545026    .5378343     -1.01    0.311     -1.599162     .5091099
          3  |   -.3183436    .4800024     -0.66    0.507     -1.259131     .6224439
          4  |   -.0916611     .426462     -0.21    0.830     -.9275113      .744189
          5  |    .1350213     .379036      0.36    0.722     -.6078756     .8779183
          6  |    .3617038    .3402906      1.06    0.288     -.3052536     1.028661
          7  |    .5883862    .3134615      1.88    0.061     -.0259869     1.202759
          8  |    .8150687    .3017439      2.70    0.007      .2236615     1.406476
          9  |    1.041751     .306874      3.39    0.001      .4402891     1.643213
         10  |    1.268434    .3280623      3.87    0.000      .6254433     1.911424
         11  |    1.495116    .3625038      4.12    0.000      .7846216     2.205611
         12  |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
         13  |    -2.75759    .7219825     -3.82    0.000      -4.17265     -1.34253
         14  |   -2.530907    .6756357     -3.75    0.000     -3.855129    -1.206686
         15  |   -2.304225    .6340656     -3.63    0.000     -3.546971    -1.061479
         16  |   -2.077543    .5982691     -3.47    0.001     -3.250128    -.9049568
         17  |    -1.85086     .569336     -3.25    0.001     -2.966738    -.7349821
         18  |   -1.624178    .5483539     -2.96    0.003     -2.698932    -.5494237
         19  |   -1.397495    .5362569     -2.61    0.009     -2.448539    -.3464509
         20  |   -1.170813    .5336496     -2.19    0.028     -2.216747    -.1248787
         21  |   -.9441303    .5406693     -1.75    0.081     -2.003823     .1155621
         22  |   -.7174478    .5569521     -1.29    0.198     -1.809054     .3741583
-------------+----------------------------------------------------------------

. marginsplot


Variables that uniquely identify margins: dcpct _atopt


  Multiple at() options specified:


_atoption=1: dcpct=(0(10)100) dneo=1 dclox=0


_atoption=2: dcpct=(0(10)100) dneo=0 dclox=0


. margins, at(dcpct=(0(10)100) dneo=1 dclox=0) at(dcpct=(0(10)100) dneo=0 dclox=0) expression(1/(1+exp(-predict(xb))))

Adjusted predictions                            Number of obs     =        108
 Model VCE   : OIM


Expression  : 1/(1+exp(-predict(xb)))


1._at  : dneo            =           1


     dclox           =           0


     dcpct           =           0


2._at  : dneo            =           1


     dclox           =           0


     dcpct           =          10


3._at  : dneo            =           1


     dclox           =           0


     dcpct           =          20


4._at  : dneo            =           1


     dclox           =           0


     dcpct           =          30


5._at  : dneo            =           1


     dclox           =           0


     dcpct           =          40


6._at  : dneo            =           1


     dclox           =           0


     dcpct           =          50


7._at  : dneo            =           1


     dclox           =           0


     dcpct           =          60


8._at  : dneo            =           1


     dclox           =           0


     dcpct           =          70


9._at  : dneo            =           1


     dclox           =           0


     dcpct           =          80


10._at  : dneo            =           1


     dclox           =           0


     dcpct           =          90


11._at  : dneo            =           1


     dclox           =           0


     dcpct           =         100


12._at  : dneo            =           0


     dclox           =           0


     dcpct           =           0


13._at  : dneo            =           0


     dclox           =           0


     dcpct           =          10


14._at  : dneo            =           0


     dclox           =           0


     dcpct           =          20


15._at  : dneo            =           0


     dclox           =           0


     dcpct           =          30


16._at  : dneo            =           0


     dclox           =           0


     dcpct           =          40


17._at  : dneo            =           0


     dclox           =           0


     dcpct           =          50


18._at  : dneo            =           0


     dclox           =           0


     dcpct           =          60


19._at  : dneo            =           0


     dclox           =           0


     dcpct           =          70


20._at  : dneo            =           0


     dclox           =           0


     dcpct           =          80


21._at  : dneo            =           0


     dclox           =           0


     dcpct           =          90


22._at  : dneo            =           0


     dclox           =           0


     dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .3161096    .1294329      2.44    0.015      .0624258     .5697934
          2  |    .3670192    .1249476      2.94    0.003      .1221264     .6119119
          3  |    .4210795    .1170109      3.60    0.000      .1917423     .6504167
          4  |    .4771007    .1063919      4.48    0.000      .2685765      .685625
          5  |    .5337041    .0943284      5.66    0.000      .3488238     .7185845
          6  |    .5894528    .0823497      7.16    0.000      .4280503     .7508553
          7  |    .6429948    .0719559      8.94    0.000      .5019639     .7840257
          8  |    .6931886    .0641743     10.80    0.000      .5674091      .818968
          9  |    .7391878     .059162     12.49    0.000      .6232324     .8551431
         10  |    .7804745    .0562082     13.89    0.000      .6703084     .8906406
         11  |    .8168449    .0542339     15.06    0.000      .7105483     .9231415
         12  |    .0481415    .0353873      1.36    0.174     -.0212164     .1174994
         13  |    .0596594    .0405034      1.47    0.141     -.0197257     .1390445
         14  |    .0737197    .0461358      1.60    0.110     -.0167049     .1641442
         15  |    .0907736    .0523318      1.73    0.083     -.0117949     .1933422
         16  |    .1112988    .0591756      1.88    0.060     -.0046833     .2272809
         17  |    .1357719    .0668047      2.03    0.042      .0048371     .2667067
         18  |    .1646295    .0754133      2.18    0.029      .0168222     .3124368
         19  |    .1982139    .0852247      2.33    0.020      .0311765     .3652512
         20  |    .2367081    .0964184      2.46    0.014      .0477315     .4256847
         21  |    .2800668    .1090148      2.57    0.010      .0664017     .4937319
         22  |    .3279552    .1227526      2.67    0.008      .0873646     .5685459
-------------+----------------------------------------------------------------

. marginsplot, noci /* CIs incorrect */


Variables that uniquely identify margins: dcpct _atopt


  Multiple at() options specified:


_atoption=1: dcpct=(0(10)100) dneo=1 dclox=0


_atoption=2: dcpct=(0(10)100) dneo=0 dclox=0


. margins, over(dcpct) at(dneo=1 dclox=0) at(dneo=0 dclox=0) expression(1/(1+exp(-predict(xb))))

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : 1/(1+exp(-predict(xb)))


over  : dcpct


1._at  : 0.dcpct


         dneo            =           1


         dclox           =           0


     1.dcpct


         dneo            =           1


         dclox           =           0


     3.dcpct


         dneo            =           1


         dclox           =           0


     5.dcpct


         dneo            =           1


         dclox           =           0


     7.dcpct


         dneo            =           1


         dclox           =           0


     10.dcpct


         dneo            =           1


         dclox           =           0


     14.dcpct


         dneo            =           1


         dclox           =           0


     20.dcpct


         dneo            =           1


         dclox           =           0


     25.dcpct


         dneo            =           1


         dclox           =           0


     30.dcpct


         dneo            =           1


         dclox           =           0


     40.dcpct


         dneo            =           1


         dclox           =           0


     50.dcpct


         dneo            =           1


         dclox           =           0


     75.dcpct


         dneo            =           1


         dclox           =           0


     80.dcpct


         dneo            =           1


         dclox           =           0


     83.dcpct


         dneo            =           1


         dclox           =           0


     90.dcpct


         dneo            =           1


         dclox           =           0


     95.dcpct


         dneo            =           1


         dclox           =           0


     99.dcpct


         dneo            =           1


         dclox           =           0


     100.dcpct


         dneo            =           1


         dclox           =           0


2._at  : 0.dcpct


         dneo            =           0


         dclox           =           0


     1.dcpct


         dneo            =           0


         dclox           =           0


     3.dcpct


         dneo            =           0


         dclox           =           0


     5.dcpct


         dneo            =           0


         dclox           =           0


     7.dcpct


         dneo            =           0


         dclox           =           0


     10.dcpct


         dneo            =           0


         dclox           =           0


     14.dcpct


         dneo            =           0


         dclox           =           0


     20.dcpct


         dneo            =           0


         dclox           =           0


     25.dcpct


         dneo            =           0


         dclox           =           0


     30.dcpct


         dneo            =           0


         dclox           =           0


     40.dcpct


         dneo            =           0


         dclox           =           0


     50.dcpct


         dneo            =           0


         dclox           =           0


     75.dcpct


         dneo            =           0


         dclox           =           0


     80.dcpct


         dneo            =           0


         dclox           =           0


     83.dcpct


         dneo            =           0


         dclox           =           0


     90.dcpct


         dneo            =           0


         dclox           =           0


     95.dcpct


         dneo            =           0


         dclox           =           0


     99.dcpct


         dneo            =           0


         dclox           =           0


     100.dcpct


         dneo            =           0


         dclox           =           0


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
   _at#dcpct |
      1   0  |    .3161096    .1294329      2.44    0.015      .0624258     .5697934
      1   1  |    .3210305    .1291508      2.49    0.013      .0678996     .5741613
      1   3  |    .3309916    .1284744      2.58    0.010      .0791864     .5827968
      1   5  |    .3411065    .1276492      2.67    0.008      .0909186     .5912944
      1   7  |    .3513681    .1266768      2.77    0.006      .1030861     .5996502
      1  10  |    .3670192    .1249476      2.94    0.003      .1221264     .6119119
      1  14  |    .3883263    .1221557      3.18    0.001      .1489056      .627747
      1  20  |    .4210795    .1170109      3.60    0.000      .1917423     .6504167
      1  25  |    .4489281    .1119689      4.01    0.000      .2294731     .6683832
      1  30  |    .4771007    .1063919      4.48    0.000      .2685765      .685625
      1  40  |    .5337041    .0943284      5.66    0.000      .3488238     .7185845
      1  50  |    .5894528    .0823497      7.16    0.000      .4280503     .7508553
      1  75  |    .7167526    .0613502     11.68    0.000      .5965083     .8369968
      1  80  |    .7391878     .059162     12.49    0.000      .6232324     .8551431
      1  83  |    .7520836    .0581075     12.94    0.000       .638195     .8659722
      1  90  |    .7804745    .0562082     13.89    0.000      .6703084     .8906406
      1  95  |    .7992759    .0551635     14.49    0.000      .6911574     .9073944
      1  99  |    .8134292    .0544159     14.95    0.000      .7067759     .9200824
      1 100  |    .8168449    .0542339     15.06    0.000      .7105483     .9231415
      2   0  |    .0481415    .0353873      1.36    0.174     -.0212164     .1174994
      2   1  |    .0491909    .0358766      1.37    0.170     -.0211258     .1195077
      2   3  |    .0513552    .0368698      1.39    0.164     -.0209082     .1236186
      2   5  |    .0536094    .0378828      1.42    0.157     -.0206395     .1278583
      2   7  |    .0559567    .0389158      1.44    0.150     -.0203169     .1322302
      2  10  |    .0596594    .0405034      1.47    0.141     -.0197257     .1390445
      2  14  |     .064954    .0426923      1.52    0.128     -.0187215     .1486294
      2  20  |    .0737197    .0461358      1.60    0.110     -.0167049     .1641442
      2  25  |     .081843    .0491592      1.66    0.096     -.0145073     .1781932
      2  30  |    .0907736    .0523318      1.73    0.083     -.0117949     .1933422
      2  40  |    .1112988    .0591756      1.88    0.060     -.0046833     .2272809
      2  50  |    .1357719    .0668047      2.03    0.042      .0048371     .2667067
      2  75  |     .216844    .0906423      2.39    0.017      .0391884     .3944995
      2  80  |    .2367081    .0964184      2.46    0.014      .0477315     .4256847
      2  83  |    .2492141    .1000555      2.49    0.013      .0531089     .4453194
      2  90  |    .2800668    .1090148      2.57    0.010      .0664017     .4937319
      2  95  |    .3034783     .115771      2.62    0.009      .0765712     .5303853
      2  99  |    .3229788    .1213422      2.66    0.008      .0851524     .5608051
      2 100  |    .3279552    .1227526      2.67    0.008      .0873646     .5685459
-------------+----------------------------------------------------------------

. marginsplot, noci /* CIs incorrect */


Variables that uniquely identify margins: dcpct _atopt


  Multiple at() options specified:


_atoption=1: dneo=1 dclox=0


_atoption=2: dneo=0 dclox=0


. * figure in lecture
. margins, over(dcpct) at(dneo=0 dclox=0) at(dneo=1 dclox=0) at(dneo=0 dclox=1) at(dneo=1 dclox=1) expression(1/(1+exp(-predict(xb))))

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : 1/(1+exp(-predict(xb)))


over  : dcpct


1._at  : 0.dcpct


         dneo            =           0


         dclox           =           0


     1.dcpct


         dneo            =           0


         dclox           =           0


     3.dcpct


         dneo            =           0


         dclox           =           0


     5.dcpct


         dneo            =           0


         dclox           =           0


     7.dcpct


         dneo            =           0


         dclox           =           0


     10.dcpct


         dneo            =           0


         dclox           =           0


     14.dcpct


         dneo            =           0


         dclox           =           0


     20.dcpct


         dneo            =           0


         dclox           =           0


     25.dcpct


         dneo            =           0


         dclox           =           0


     30.dcpct


         dneo            =           0


         dclox           =           0


     40.dcpct


         dneo            =           0


         dclox           =           0


     50.dcpct


         dneo            =           0


         dclox           =           0


     75.dcpct


         dneo            =           0


         dclox           =           0


     80.dcpct


         dneo            =           0


         dclox           =           0


     83.dcpct


         dneo            =           0


         dclox           =           0


     90.dcpct


         dneo            =           0


         dclox           =           0


     95.dcpct


         dneo            =           0


         dclox           =           0


     99.dcpct


         dneo            =           0


         dclox           =           0


     100.dcpct


         dneo            =           0


         dclox           =           0


2._at  : 0.dcpct


         dneo            =           1


         dclox           =           0


     1.dcpct


         dneo            =           1


         dclox           =           0


     3.dcpct


         dneo            =           1


         dclox           =           0


     5.dcpct


         dneo            =           1


         dclox           =           0


     7.dcpct


         dneo            =           1


         dclox           =           0


     10.dcpct


         dneo            =           1


         dclox           =           0


     14.dcpct


         dneo            =           1


         dclox           =           0


     20.dcpct


         dneo            =           1


         dclox           =           0


     25.dcpct


         dneo            =           1


         dclox           =           0


     30.dcpct


         dneo            =           1


         dclox           =           0


     40.dcpct


         dneo            =           1


         dclox           =           0


     50.dcpct


         dneo            =           1


         dclox           =           0


     75.dcpct


         dneo            =           1


         dclox           =           0


     80.dcpct


         dneo            =           1


         dclox           =           0


     83.dcpct


         dneo            =           1


         dclox           =           0


     90.dcpct


         dneo            =           1


         dclox           =           0


     95.dcpct


         dneo            =           1


         dclox           =           0


     99.dcpct


         dneo            =           1


         dclox           =           0


     100.dcpct


         dneo            =           1


         dclox           =           0


3._at  : 0.dcpct


         dneo            =           0


         dclox           =           1


     1.dcpct


         dneo            =           0


         dclox           =           1


     3.dcpct


         dneo            =           0


         dclox           =           1


     5.dcpct


         dneo            =           0


         dclox           =           1


     7.dcpct


         dneo            =           0


         dclox           =           1


     10.dcpct


         dneo            =           0


         dclox           =           1


     14.dcpct


         dneo            =           0


         dclox           =           1


     20.dcpct


         dneo            =           0


         dclox           =           1


     25.dcpct


         dneo            =           0


         dclox           =           1


     30.dcpct


         dneo            =           0


         dclox           =           1


     40.dcpct


         dneo            =           0


         dclox           =           1


     50.dcpct


         dneo            =           0


         dclox           =           1


     75.dcpct


         dneo            =           0


         dclox           =           1


     80.dcpct


         dneo            =           0


         dclox           =           1


     83.dcpct


         dneo            =           0


         dclox           =           1


     90.dcpct


         dneo            =           0


         dclox           =           1


     95.dcpct


         dneo            =           0


         dclox           =           1


     99.dcpct


         dneo            =           0


         dclox           =           1


     100.dcpct


         dneo            =           0


         dclox           =           1


4._at  : 0.dcpct


         dneo            =           1


         dclox           =           1


     1.dcpct


         dneo            =           1


         dclox           =           1


     3.dcpct


         dneo            =           1


         dclox           =           1


     5.dcpct


         dneo            =           1


         dclox           =           1


     7.dcpct


         dneo            =           1


         dclox           =           1


     10.dcpct


         dneo            =           1


         dclox           =           1


     14.dcpct


         dneo            =           1


         dclox           =           1


     20.dcpct


         dneo            =           1


         dclox           =           1


     25.dcpct


         dneo            =           1


         dclox           =           1


     30.dcpct


         dneo            =           1


         dclox           =           1


     40.dcpct


         dneo            =           1


         dclox           =           1


     50.dcpct


         dneo            =           1


         dclox           =           1


     75.dcpct


         dneo            =           1


         dclox           =           1


     80.dcpct


         dneo            =           1


         dclox           =           1


     83.dcpct


         dneo            =           1


         dclox           =           1


     90.dcpct


         dneo            =           1


         dclox           =           1


     95.dcpct


         dneo            =           1


         dclox           =           1


     99.dcpct


         dneo            =           1


         dclox           =           1


     100.dcpct


         dneo            =           1


         dclox           =           1


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
   _at#dcpct |
      1   0  |    .0481415    .0353873      1.36    0.174     -.0212164     .1174994
      1   1  |    .0491909    .0358766      1.37    0.170     -.0211258     .1195077
      1   3  |    .0513552    .0368698      1.39    0.164     -.0209082     .1236186
      1   5  |    .0536094    .0378828      1.42    0.157     -.0206395     .1278583
      1   7  |    .0559567    .0389158      1.44    0.150     -.0203169     .1322302
      1  10  |    .0596594    .0405034      1.47    0.141     -.0197257     .1390445
      1  14  |     .064954    .0426923      1.52    0.128     -.0187215     .1486294
      1  20  |    .0737197    .0461358      1.60    0.110     -.0167049     .1641442
      1  25  |     .081843    .0491592      1.66    0.096     -.0145073     .1781932
      1  30  |    .0907736    .0523318      1.73    0.083     -.0117949     .1933422
      1  40  |    .1112988    .0591756      1.88    0.060     -.0046833     .2272809
      1  50  |    .1357719    .0668047      2.03    0.042      .0048371     .2667067
      1  75  |     .216844    .0906423      2.39    0.017      .0391884     .3944995
      1  80  |    .2367081    .0964184      2.46    0.014      .0477315     .4256847
      1  83  |    .2492141    .1000555      2.49    0.013      .0531089     .4453194
      1  90  |    .2800668    .1090148      2.57    0.010      .0664017     .4937319
      1  95  |    .3034783     .115771      2.62    0.009      .0765712     .5303853
      1  99  |    .3229788    .1213422      2.66    0.008      .0851524     .5608051
      1 100  |    .3279552    .1227526      2.67    0.008      .0873646     .5685459
      2   0  |    .3161096    .1294329      2.44    0.015      .0624258     .5697934
      2   1  |    .3210305    .1291508      2.49    0.013      .0678996     .5741613
      2   3  |    .3309916    .1284744      2.58    0.010      .0791864     .5827968
      2   5  |    .3411065    .1276492      2.67    0.008      .0909186     .5912944
      2   7  |    .3513681    .1266768      2.77    0.006      .1030861     .5996502
      2  10  |    .3670192    .1249476      2.94    0.003      .1221264     .6119119
      2  14  |    .3883263    .1221557      3.18    0.001      .1489056      .627747
      2  20  |    .4210795    .1170109      3.60    0.000      .1917423     .6504167
      2  25  |    .4489281    .1119689      4.01    0.000      .2294731     .6683832
      2  30  |    .4771007    .1063919      4.48    0.000      .2685765      .685625
      2  40  |    .5337041    .0943284      5.66    0.000      .3488238     .7185845
      2  50  |    .5894528    .0823497      7.16    0.000      .4280503     .7508553
      2  75  |    .7167526    .0613502     11.68    0.000      .5965083     .8369968
      2  80  |    .7391878     .059162     12.49    0.000      .6232324     .8551431
      2  83  |    .7520836    .0581075     12.94    0.000       .638195     .8659722
      2  90  |    .7804745    .0562082     13.89    0.000      .6703084     .8906406
      2  95  |    .7992759    .0551635     14.49    0.000      .6911574     .9073944
      2  99  |    .8134292    .0544159     14.95    0.000      .7067759     .9200824
      2 100  |    .8168449    .0542339     15.06    0.000      .7105483     .9231415
      3   0  |     .012167    .0113038      1.08    0.282      -.009988     .0343219
      3   1  |    .0124425    .0114918      1.08    0.279     -.0100811      .034966
      3   3  |     .013012    .0118767      1.10    0.273      -.010266       .03629
      3   5  |    .0136073    .0122738      1.11    0.268     -.0104489     .0376635
      3   7  |    .0142294    .0126833      1.12    0.262     -.0106294     .0390883
      3  10  |    .0152155     .013322      1.14    0.253     -.0108951     .0413261
      3  14  |    .0166356    .0142212      1.17    0.242     -.0112374     .0445086
      3  20  |    .0190132    .0156799      1.21    0.225     -.0117188     .0497453
      3  25  |    .0212466    .0170054      1.25    0.212     -.0120834     .0545766
      3  30  |     .023736     .018441      1.29    0.198     -.0124077     .0598796
      3  40  |    .0295964     .021688      1.36    0.172     -.0129112     .0721041
      3  50  |    .0368492    .0255313      1.44    0.149     -.0131913     .0868897
      3  75  |    .0631701    .0388438      1.63    0.104     -.0129622     .1393025
      3  80  |    .0702191    .0423739      1.66    0.097     -.0128323     .1532704
      3  83  |    .0747909    .0446697      1.67    0.094     -.0127601     .1623419
      3  90  |    .0865389    .0506051      1.71    0.087     -.0126453     .1857232
      3  95  |    .0959285    .0553947      1.73    0.083      -.012643     .2045001
      3  99  |    .1040855    .0595911      1.75    0.081      -.012711      .220882
      3 100  |    .1062184    .0606939      1.75    0.080     -.0127395     .2251763
      4   0  |    .1011761    .0737216      1.37    0.170     -.0433155     .2456678
      4   1  |    .1032563    .0745313      1.39    0.166     -.0428223     .2493348
      4   3  |    .1075303    .0761506      1.41    0.158      -.041722     .2567827
      4   5  |    .1119592     .077768      1.44    0.150     -.0404632     .2643816
      4   7  |    .1165467    .0793811      1.47    0.142     -.0390374     .2721307
      4  10  |    .1237333    .0817875      1.51    0.130     -.0365673     .2840339
      4  14  |    .1339044    .0849584      1.58    0.115     -.0326111     .3004199
      4  20  |    .1504777    .0895928      1.68    0.093     -.0251211     .3260764
      4  25  |    .1655474    .0932996      1.77    0.076     -.0173165     .3484113
      4  30  |    .1818033     .096825      1.88    0.060     -.0079701     .3715768
      4  40  |     .217977    .1032015      2.11    0.035      .0157058     .4202482
      4  50  |    .2590692    .1085332      2.39    0.017      .0463479     .4717904
      4  75  |     .381283     .117435      3.25    0.001      .1511146     .6114514
      4  80  |    .4083562    .1186504      3.44    0.001      .1758057     .6409068
      4  83  |    .4248829    .1193208      3.56    0.000      .1910185     .6587473
      4  90  |    .4640416    .1207365      3.84    0.000      .2274024     .7006808
      4  95  |    .4923153    .1216232      4.05    0.000      .2539383     .7306923
      4  99  |    .5149785    .1222476      4.21    0.000      .2753775     .7545794
      4 100  |    .5206383    .1223902      4.25    0.000       .280758     .7605186
-------------+----------------------------------------------------------------

. marginsplot, noci


Variables that uniquely identify margins: dcpct _atopt


  Multiple at() options specified:


_atoption=1: dneo=0 dclox=0


_atoption=2: dneo=1 dclox=0


_atoption=3: dneo=0 dclox=1


_atoption=4: dneo=1 dclox=1


. * illustration of weighting
. margins, at(dcpct=(0(10)100)) predict(xb)

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : Linear prediction (log odds), predict(xb)


1._at  : dcpct           =           0


2._at  : dcpct           =          10


3._at  : dcpct           =          20


4._at  : dcpct           =          30


5._at  : dcpct           =          40


6._at  : dcpct           =          50


7._at  : dcpct           =          60


8._at  : dcpct           =          70


9._at  : dcpct           =          80


10._at  : dcpct           =          90


11._at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   -1.821385    .6139347     -2.97    0.003     -3.024675    -.6180956
          2  |   -1.594703    .5487433     -2.91    0.004      -2.67022    -.5191858
          3  |    -1.36802    .4854417     -2.82    0.005     -2.319469    -.4165722
          4  |   -1.141338    .4248753     -2.69    0.007     -1.974078    -.3085977
          5  |   -.9146556    .3683957     -2.48    0.013     -1.636698    -.1926133
          6  |   -.6879731    .3181865     -2.16    0.031     -1.311607     -.064339
          7  |   -.4612907    .2776705     -1.66    0.097     -1.005515     .0829335
          8  |   -.2346082    .2515752     -0.93    0.351     -.7276865     .2584701
          9  |   -.0079257    .2445612     -0.03    0.974      -.487257     .4714055
         10  |    .2187567    .2581885      0.85    0.397     -.2872834     .7247968
         11  |    .4454392    .2895571      1.54    0.124     -.1220823     1.012961
-------------+----------------------------------------------------------------

. table dneo dclox, row col

----------+--------------------
Neomycin  | Cloxacillin used on
used on   |        farm
farm      |    no    yes  Total
----------+--------------------
       no |    33     12     45
      yes |    59     15     74
          |
    Total |    92     27    119
----------+--------------------

. lincom _cons+0*dcpct+1.dclox*27/108+1.dneo*74/108 /* dcpct=0 */


 ( 1)  .6851852*[casecont]1.dneo + .25*[casecont]1.dclox + [casecont]_cons = 0


-------------+----------------------------------------------------------------
     casecont |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         (1) |   -1.821385    .6139347     -2.97    0.003     -3.024675    -.6180956
-------------+----------------------------------------------------------------

. margins, at(dcpct=(0(10)100)) predict(xb) atmeans /* same as above */

Adjusted predictions                            Number of obs     =        108
 Model VCE   : OIM


Expression  : Linear prediction (log odds), predict(xb)


1._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =           0


2._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          10


3._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          20


4._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          30


5._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          40


6._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          50


7._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          60


8._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          70


9._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          80


10._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          90


11._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   -1.821385    .6139347     -2.97    0.003     -3.024675    -.6180956
          2  |   -1.594703    .5487433     -2.91    0.004      -2.67022    -.5191858
          3  |    -1.36802    .4854417     -2.82    0.005     -2.319469    -.4165722
          4  |   -1.141338    .4248753     -2.69    0.007     -1.974078    -.3085976
          5  |   -.9146556    .3683957     -2.48    0.013     -1.636698    -.1926133
          6  |   -.6879731    .3181865     -2.16    0.031     -1.311607     -.064339
          7  |   -.4612906    .2776705     -1.66    0.097     -1.005515     .0829335
          8  |   -.2346082    .2515752     -0.93    0.351     -.7276865     .2584701
          9  |   -.0079257    .2445612     -0.03    0.974      -.487257     .4714055
         10  |    .2187567    .2581885      0.85    0.397     -.2872834     .7247968
         11  |    .4454392    .2895571      1.54    0.124     -.1220823     1.012961
-------------+----------------------------------------------------------------

. margins, at(dcpct=(0(10)100)) predict(xb) over(dneo dclox)

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : Linear prediction (log odds), predict(xb)


over  : dneo dclox


1._at  : 0.dneo#0.dclox


         dcpct           =           0


     0.dneo#1.dclox


         dcpct           =           0


     1.dneo#0.dclox


         dcpct           =           0


     1.dneo#1.dclox


         dcpct           =           0


2._at  : 0.dneo#0.dclox


         dcpct           =          10


     0.dneo#1.dclox


         dcpct           =          10


     1.dneo#0.dclox


         dcpct           =          10


     1.dneo#1.dclox


         dcpct           =          10


3._at  : 0.dneo#0.dclox


         dcpct           =          20


     0.dneo#1.dclox


         dcpct           =          20


     1.dneo#0.dclox


         dcpct           =          20


     1.dneo#1.dclox


         dcpct           =          20


4._at  : 0.dneo#0.dclox


         dcpct           =          30


     0.dneo#1.dclox


         dcpct           =          30


     1.dneo#0.dclox


         dcpct           =          30


     1.dneo#1.dclox


         dcpct           =          30


5._at  : 0.dneo#0.dclox


         dcpct           =          40


     0.dneo#1.dclox


         dcpct           =          40


     1.dneo#0.dclox


         dcpct           =          40


     1.dneo#1.dclox


         dcpct           =          40


6._at  : 0.dneo#0.dclox


         dcpct           =          50


     0.dneo#1.dclox


         dcpct           =          50


     1.dneo#0.dclox


         dcpct           =          50


     1.dneo#1.dclox


         dcpct           =          50


7._at  : 0.dneo#0.dclox


         dcpct           =          60


     0.dneo#1.dclox


         dcpct           =          60


     1.dneo#0.dclox


         dcpct           =          60


     1.dneo#1.dclox


         dcpct           =          60


8._at  : 0.dneo#0.dclox


         dcpct           =          70


     0.dneo#1.dclox


         dcpct           =          70


     1.dneo#0.dclox


         dcpct           =          70


     1.dneo#1.dclox


         dcpct           =          70


9._at  : 0.dneo#0.dclox


         dcpct           =          80


     0.dneo#1.dclox


         dcpct           =          80


     1.dneo#0.dclox


         dcpct           =          80


     1.dneo#1.dclox


         dcpct           =          80


10._at  : 0.dneo#0.dclox


         dcpct           =          90


     0.dneo#1.dclox


         dcpct           =          90


     1.dneo#0.dclox


         dcpct           =          90


     1.dneo#1.dclox


         dcpct           =          90


11._at  : 0.dneo#0.dclox


         dcpct           =         100


     0.dneo#1.dclox


         dcpct           =         100


     1.dneo#0.dclox


         dcpct           =         100


     1.dneo#1.dclox


         dcpct           =         100


---------------+----------------------------------------------------------------
               |            Delta-method
               |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
---------------+----------------------------------------------------------------
_at#dneo#dclox |
      1#no#no  |   -2.984272    .7722467     -3.86    0.000     -4.497848    -1.470697
     1#no#yes  |   -4.396788    .9404953     -4.67    0.000     -6.240125    -2.553452
     1#yes#no  |   -.7717085    .5987154     -1.29    0.197     -1.945169     .4017521
    1#yes#yes  |   -2.184225    .8106662     -2.69    0.007     -3.773101    -.5953479
      2#no#no  |    -2.75759    .7219825     -3.82    0.000      -4.17265     -1.34253
     2#no#yes  |   -4.170106    .8890788     -4.69    0.000     -5.912668    -2.427544
     2#yes#no  |    -.545026    .5378343     -1.01    0.311     -1.599162     .5091099
    2#yes#yes  |   -1.957542     .754335     -2.60    0.009     -3.436012    -.4790726
      3#no#no  |   -2.530907    .6756357     -3.75    0.000     -3.855129    -1.206686
     3#no#yes  |   -3.943424    .8406675     -4.69    0.000     -5.591102    -2.295745
     3#yes#no  |   -.3183436    .4800024     -0.66    0.507     -1.259131     .6224439
    3#yes#yes  |    -1.73086    .7008523     -2.47    0.014     -3.104505    -.3572144
      4#no#no  |   -2.304225    .6340656     -3.63    0.000     -3.546971    -1.061479
     4#no#yes  |   -3.716741      .79581     -4.67    0.000       -5.2765    -2.156982
     4#yes#no  |   -.0916611     .426462     -0.21    0.830     -.9275113      .744189
    4#yes#yes  |   -1.504177    .6509204     -2.31    0.021     -2.779958    -.2283966
      5#no#no  |   -2.077543    .5982691     -3.47    0.001     -3.250128    -.9049568
     5#no#yes  |   -3.490059    .7551398     -4.62    0.000     -4.970105    -2.010012
     5#yes#no  |    .1350213     .379036      0.36    0.722     -.6078756     .8779183
    5#yes#yes  |   -1.277495    .6054187     -2.11    0.035     -2.464094    -.0908958
      6#no#no  |    -1.85086     .569336     -3.25    0.001     -2.966738    -.7349821
     6#no#yes  |   -3.263376    .7193676     -4.54    0.000     -4.673311    -1.853442
     6#yes#no  |    .3617038    .3402906      1.06    0.288     -.3052536     1.028661
    6#yes#yes  |   -1.050812    .5654177     -1.86    0.063     -2.159011     .0573861
      7#no#no  |   -1.624178    .5483539     -2.96    0.003     -2.698932    -.5494237
     7#no#yes  |   -3.036694    .6892563     -4.41    0.000     -4.387611    -1.685776
     7#yes#no  |    .5883862    .3134615      1.88    0.061     -.0259869     1.202759
    7#yes#yes  |   -.8241298    .5321593     -1.55    0.121     -1.867143     .2188833
      8#no#no  |   -1.397495    .5362569     -2.61    0.009     -2.448539    -.3464509
     8#no#yes  |   -2.810011    .6655747     -4.22    0.000     -4.114514    -1.505509
     8#yes#no  |    .8150687    .3017439      2.70    0.007      .2236615     1.406476
    8#yes#yes  |   -.5974473    .5069722     -1.18    0.239     -1.591095        .3962
      9#no#no  |   -1.170813    .5336496     -2.19    0.028     -2.216747    -.1248787
     9#no#yes  |   -2.583329     .649027     -3.98    0.000     -3.855398    -1.311259
     9#yes#no  |    1.041751     .306874      3.39    0.001      .4402891     1.643213
    9#yes#yes  |   -.3707649    .4910999     -0.75    0.450     -1.333303     .5917732
     10#no#no  |   -.9441303    .5406693     -1.75    0.081     -2.003823     .1155621
    10#no#yes  |   -2.356646    .6401668     -3.68    0.000      -3.61135    -1.101943
    10#yes#no  |    1.268434    .3280623      3.87    0.000      .6254433     1.911424
   10#yes#yes  |   -.1440824    .4854568     -0.30    0.767      -1.09556     .8073953
     11#no#no  |   -.7174478    .5569521     -1.29    0.198     -1.809054     .3741583
    11#no#yes  |   -2.129964    .6393135     -3.33    0.001     -3.382995    -.8769324
    11#yes#no  |    1.495116    .3625038      4.12    0.000      .7846216     2.205611
   11#yes#yes  |       .0826    .4903962      0.17    0.866     -.8785588     1.043759
---------------+----------------------------------------------------------------

. di ((-2.984272)*22+(-4.396788)*12+(-.7717085)*59+(-2.184225)*15)/108 /* dcpct=0 again */
-1.8213853

. margins, at(dcpct=(0(10)100)) atmeans /* backtransformed */

Adjusted predictions                            Number of obs     =        108
 Model VCE   : OIM


Expression  : Pr(casecont), predict()


1._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =           0


2._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          10


3._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          20


4._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          30


5._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          40


6._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          50


7._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          60


8._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          70


9._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          80


10._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =          90


11._at  : 0.dneo          =    .3148148 (mean)


     1.dneo          =    .6851852 (mean)


     0.dclox         =         .75 (mean)


     1.dclox         =         .25 (mean)


     dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .1392677    .0735937      1.89    0.058     -.0049733     .2835087
          2  |    .1687233    .0769644      2.19    0.028      .0178758     .3195707
          3  |    .2029399    .0785228      2.58    0.010      .0490381     .3568416
          4  |    .2420748    .0779538      3.11    0.002      .0892881     .3948615
          5  |    .2860481    .0752355      3.80    0.000      .1385893     .4335069
          6  |    .3344841    .0708297      4.72    0.000      .1956604     .4733079
          7  |    .3866797    .0658519      5.87    0.000      .2576123     .5157471
          8  |    .4416155    .0620362      7.12    0.000      .3200267     .5632043
          9  |    .4980186    .0611394      8.15    0.000      .3781877     .6178495
         10  |    .5544721     .063781      8.69    0.000      .4294636     .6794806
         11  |    .6095543     .068914      8.85    0.000      .4744854     .7446232
-------------+----------------------------------------------------------------

. di 1/(1+exp(1.821385)) /* dcpct=0, transformed to probability scale */
.13926777

. * weighting on probability scale
. margins, at(dcpct=(0(10)100)) /* not the same! */

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : Pr(casecont), predict()


1._at  : dcpct           =           0


2._at  : dcpct           =          10


3._at  : dcpct           =          20


4._at  : dcpct           =          30


5._at  : dcpct           =          40


6._at  : dcpct           =          50


7._at  : dcpct           =          60


8._at  : dcpct           =          70


9._at  : dcpct           =          80


10._at  : dcpct           =          90


11._at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .1979002    .0842364      2.35    0.019         .0328     .3630005
          2  |    .2315299    .0825239      2.81    0.005       .069786     .3932737
          3  |    .2680634    .0785496      3.41    0.001       .114109     .4220178
          4  |    .3070171    .0726128      4.23    0.000      .1646987     .4493355
          5  |    .3477956    .0653188      5.32    0.000      .2197731     .4758182
          6  |    .3897493    .0575728      6.77    0.000      .2769088     .5025898
          7  |    .4322352     .050553      8.55    0.000      .3331532     .5313172
          8  |    .4746683     .045601     10.41    0.000       .385292     .5640446
          9  |    .5165521    .0438292     11.79    0.000      .4306484     .6024558
         10  |    .5574866    .0454685     12.26    0.000      .4683699     .6466033
         11  |     .597158    .0497083     12.01    0.000      .4997316     .6945844
-------------+----------------------------------------------------------------

. margins, at(dcpct=(0(10)100)) expression(1/(1+exp(-predict(xb)))) /* same as preceding line */

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : 1/(1+exp(-predict(xb)))


1._at  : dcpct           =           0


2._at  : dcpct           =          10


3._at  : dcpct           =          20


4._at  : dcpct           =          30


5._at  : dcpct           =          40


6._at  : dcpct           =          50


7._at  : dcpct           =          60


8._at  : dcpct           =          70


9._at  : dcpct           =          80


10._at  : dcpct           =          90


11._at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .1979002    .0842364      2.35    0.019         .0328     .3630005
          2  |    .2315299    .0825239      2.81    0.005       .069786     .3932737
          3  |    .2680634    .0785496      3.41    0.001       .114109     .4220178
          4  |    .3070171    .0726128      4.23    0.000      .1646987     .4493355
          5  |    .3477956    .0653188      5.32    0.000      .2197731     .4758182
          6  |    .3897493    .0575728      6.77    0.000      .2769088     .5025898
          7  |    .4322352     .050553      8.55    0.000      .3331532     .5313172
          8  |    .4746683     .045601     10.41    0.000       .385292     .5640446
          9  |    .5165521    .0438292     11.79    0.000      .4306484     .6024558
         10  |    .5574866    .0454685     12.26    0.000      .4683699     .6466033
         11  |     .597158    .0497083     12.01    0.000      .4997316     .6945844
-------------+----------------------------------------------------------------

. margins, at(dcpct=(0(10)100)) predict(pr) /* same as preceding line */

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : Pr(casecont), predict(pr)


1._at  : dcpct           =           0


2._at  : dcpct           =          10


3._at  : dcpct           =          20


4._at  : dcpct           =          30


5._at  : dcpct           =          40


6._at  : dcpct           =          50


7._at  : dcpct           =          60


8._at  : dcpct           =          70


9._at  : dcpct           =          80


10._at  : dcpct           =          90


11._at  : dcpct           =         100


-------------+----------------------------------------------------------------
             |            Delta-method
             |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .1979002    .0842364      2.35    0.019         .0328     .3630005
          2  |    .2315299    .0825239      2.81    0.005       .069786     .3932737
          3  |    .2680634    .0785496      3.41    0.001       .114109     .4220178
          4  |    .3070171    .0726128      4.23    0.000      .1646987     .4493355
          5  |    .3477956    .0653188      5.32    0.000      .2197731     .4758182
          6  |    .3897493    .0575728      6.77    0.000      .2769088     .5025898
          7  |    .4322352     .050553      8.55    0.000      .3331532     .5313172
          8  |    .4746683     .045601     10.41    0.000       .385292     .5640446
          9  |    .5165521    .0438292     11.79    0.000      .4306484     .6024558
         10  |    .5574866    .0454685     12.26    0.000      .4683699     .6466033
         11  |     .597158    .0497083     12.01    0.000      .4997316     .6945844
-------------+----------------------------------------------------------------

. margins, at(dcpct=(0(10)100)) over(dneo dclox)

Predictive margins                              Number of obs     =        108
 Model VCE   : OIM


Expression  : Pr(casecont), predict()


over  : dneo dclox


1._at  : 0.dneo#0.dclox


         dcpct           =           0


     0.dneo#1.dclox


         dcpct           =           0


     1.dneo#0.dclox


         dcpct           =           0


     1.dneo#1.dclox


         dcpct           =           0


2._at  : 0.dneo#0.dclox


         dcpct           =          10


     0.dneo#1.dclox


         dcpct           =          10


     1.dneo#0.dclox


         dcpct           =          10


     1.dneo#1.dclox


         dcpct           =          10


3._at  : 0.dneo#0.dclox


         dcpct           =          20


     0.dneo#1.dclox


         dcpct           =          20


     1.dneo#0.dclox


         dcpct           =          20


     1.dneo#1.dclox


         dcpct           =          20


4._at  : 0.dneo#0.dclox


         dcpct           =          30


     0.dneo#1.dclox


         dcpct           =          30


     1.dneo#0.dclox


         dcpct           =          30


     1.dneo#1.dclox


         dcpct           =          30


5._at  : 0.dneo#0.dclox


         dcpct           =          40


     0.dneo#1.dclox


         dcpct           =          40


     1.dneo#0.dclox


         dcpct           =          40


     1.dneo#1.dclox


         dcpct           =          40


6._at  : 0.dneo#0.dclox


         dcpct           =          50


     0.dneo#1.dclox


         dcpct           =          50


     1.dneo#0.dclox


         dcpct           =          50


     1.dneo#1.dclox


         dcpct           =          50


7._at  : 0.dneo#0.dclox


         dcpct           =          60


     0.dneo#1.dclox


         dcpct           =          60


     1.dneo#0.dclox


         dcpct           =          60


     1.dneo#1.dclox


         dcpct           =          60


8._at  : 0.dneo#0.dclox


         dcpct           =          70


     0.dneo#1.dclox


         dcpct           =          70


     1.dneo#0.dclox


         dcpct           =          70


     1.dneo#1.dclox


         dcpct           =          70


9._at  : 0.dneo#0.dclox


         dcpct           =          80


     0.dneo#1.dclox


         dcpct           =          80


     1.dneo#0.dclox


         dcpct           =          80


     1.dneo#1.dclox


         dcpct           =          80


10._at  : 0.dneo#0.dclox


         dcpct           =          90


     0.dneo#1.dclox


         dcpct           =          90


     1.dneo#0.dclox


         dcpct           =          90


     1.dneo#1.dclox


         dcpct           =          90


11._at  : 0.dneo#0.dclox


         dcpct           =         100


     0.dneo#1.dclox


         dcpct           =         100


     1.dneo#0.dclox


         dcpct           =         100


     1.dneo#1.dclox


         dcpct           =         100


---------------+----------------------------------------------------------------
               |            Delta-method
               |     Margin    Std. Err.       z    P>|z|      [95% Con f. Interval]
---------------+----------------------------------------------------------------
_at#dneo#dclox |
      1#no#no  |    .0481415    .0353873      1.36    0.174     -.0212164     .1174994
     1#no#yes  |     .012167    .0113038      1.08    0.282      -.009988     .0343219
     1#yes#no  |    .3161096    .1294329      2.44    0.015      .0624258     .5697934
    1#yes#yes  |    .1011761    .0737216      1.37    0.170     -.0433155     .2456678
      2#no#no  |    .0596594    .0405034      1.47    0.141     -.0197257     .1390445
     2#no#yes  |    .0152155     .013322      1.14    0.253     -.0108951     .0413261
     2#yes#no  |    .3670192    .1249476      2.94    0.003      .1221264     .6119119
    2#yes#yes  |    .1237333    .0817875      1.51    0.130     -.0365673     .2840339
      3#no#no  |    .0737197    .0461358      1.60    0.110     -.0167049     .1641442
     3#no#yes  |    .0190132    .0156799      1.21    0.225     -.0117188     .0497453
     3#yes#no  |    .4210795    .1170109      3.60    0.000      .1917423     .6504167
    3#yes#yes  |    .1504777    .0895928      1.68    0.093     -.0251211     .3260764
      4#no#no  |    .0907736    .0523318      1.73    0.083     -.0117949     .1933422
     4#no#yes  |     .023736     .018441      1.29    0.198     -.0124077     .0598796
     4#yes#no  |    .4771007    .1063919      4.48    0.000      .2685765      .685625
    4#yes#yes  |    .1818033     .096825      1.88    0.060     -.0079701     .3715768
      5#no#no  |    .1112988    .0591756      1.88    0.060     -.0046833     .2272809
     5#no#yes  |    .0295964     .021688      1.36    0.172     -.0129112     .0721041
     5#yes#no  |    .5337041    .0943284      5.66    0.000      .3488238     .7185845
    5#yes#yes  |     .217977    .1032015      2.11    0.035      .0157058     .4202482
      6#no#no  |    .1357719    .0668047      2.03    0.042      .0048371     .2667067
     6#no#yes  |    .0368492    .0255313      1.44    0.149     -.0131913     .0868897
     6#yes#no  |    .5894528    .0823497      7.16    0.000      .4280503     .7508553
    6#yes#yes  |    .2590692    .1085332      2.39    0.017      .0463479     .4717904
      7#no#no  |    .1646295    .0754133      2.18    0.029      .0168222     .3124368
     7#no#yes  |    .0457954    .0301193      1.52    0.128     -.0132372     .1048281
     7#yes#no  |    .6429948    .0719559      8.94    0.000      .5019639     .7840257
    7#yes#yes  |    .3048877    .1127812      2.70    0.007      .0838407     .5259347
      8#no#no  |    .1982139    .0852247      2.33    0.020      .0311765     .3652512
     8#no#yes  |    .0567856    .0356488      1.59    0.111     -.0130848      .126656
     8#yes#no  |    .6931886    .0641743     10.80    0.000      .5674091      .818968
    8#yes#yes  |    .3549279    .1160734      3.06    0.002      .1274283     .5824275
      9#no#no  |    .2367081    .0964184      2.46    0.014      .0477315     .4256847
     9#no#yes  |    .0702191    .0423739      1.66    0.097     -.0128323     .1532704
     9#yes#no  |    .7391878     .059162     12.49    0.000      .6232324     .8551431
    9#yes#yes  |    .4083562    .1186504      3.44    0.001      .1758057     .6409068
     10#no#no  |    .2800668    .1090148      2.57    0.010      .0664017     .4937319
    10#no#yes  |    .0865389    .0506051      1.71    0.087     -.0126453     .1857232
    10#yes#no  |    .7804745    .0562082     13.89    0.000      .6703084     .8906406
   10#yes#yes  |    .4640416    .1207365      3.84    0.000      .2274024     .7006808
     11#no#no  |    .3279552    .1227526      2.67    0.008      .0873646     .5685459
    11#no#yes  |    .1062184    .0606939      1.75    0.080     -.0127395     .2251763
    11#yes#no  |    .8168449    .0542339     15.06    0.000      .7105483     .9231415
   11#yes#yes  |    .5206383    .1223902      4.25    0.000       .280758     .7605186
---------------+----------------------------------------------------------------

. di (.0481415*22+.012167*12+.3161096*59+.1011761*15)/108 /* dcpct=0, weighted at probability scale */
.19790023

.
end of do-file

. exit, clear