Displaying Predictive Margins per 1,000

[Fouziah Almouqati]

Dear all,

I am running margins after an nbreg model using the code below:

 margins pre_year_cat

Here are the results:

Expression: Predicted number of events, predict()

Delta-method
Margin    std. err.     z     P>z    [95% conf.    interval]

pre_year_cat 
2015      .1045401    .0014501    72.09    0.000    .1016979    .1073822
2016      .1289053     .001505    85.65    0.000    .1259556    .131855
2017      .1270988    .0014596    87.08    0.000    .124238    .1299597
2018      .1417483    .0015568    91.05    0.000    .1386971    .1447995
2019      .1388607    .0014831    93.63    0.000    .1359538    .1417676
2020      .1742347    .0017418   100.03    0.000    .1708207    .1776486
2021      .1909975    .0018776   101.73    0.000    .1873175    .1946775
2022      .1855599    .0019013    97.60    0.000    .1818335    .1892863

How can I show the margins per 1,000? Instead of .1045401, I want them to be 104, and I do not want to calculate them manually as I plan to use marginsplot afterwards.

[Clyde Schechter]

margins pre_year_cat, expression(1000*predict(pr))

[Fouziah Almouqati]

Thanks Clyde.

I tried the code and it does produce the below result

nbreg total_CT_AT ib(first).pre_year_cat ib(first).age_grp

Negative binomial regression                          Number of obs =  552,366
                                                      LR chi2(10)   = 18053.38
Dispersion: mean                                      Prob > chi2   =   0.0000
Log likelihood = -235929.96                           Pseudo R2     =   0.0369

------------------------------------------------------------------------------
 total_CT_AT | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
pre_year_cat |
       2016  |   .2095076    .018123    11.56   0.000     .1739872     .245028
       2017  |   .1953946   .0180015    10.85   0.000     .1601123    .2306769
       2018  |   .3044825   .0176856    17.22   0.000     .2698194    .3391456
       2019  |    .283901   .0175018    16.22   0.000      .249598    .3182039
       2020  |   .5108328   .0170897    29.89   0.000     .4773376     .544328
       2021  |   .6026901   .0169962    35.46   0.000     .5693781    .6360021
       2022  |   .5738074   .0172445    33.27   0.000     .5400088    .6076059
             |
     age_grp |
      35-54  |   .6154216    .012735    48.33   0.000     .5904615    .6403817
      55-74  |   1.033554   .0123243    83.86   0.000     1.009398    1.057709
        75+  |   1.374228   .0122126   112.53   0.000     1.350292    1.398164
             |
       _cons |  -3.060938   .0166162  -184.21   0.000    -3.093505    -3.02837
-------------+----------------------------------------------------------------
    /lnalpha |   .2328044   .0164584                      .2005465    .2650623
-------------+----------------------------------------------------------------
       alpha |   1.262135   .0207727                       1.22207    1.303512
------------------------------------------------------------------------------
LR test of alpha=0: chibar2(01) = 7314.49              Prob >= chibar2 = 0.000

. margins pre_year_cat, expression(1000*predict(pr))
option pr not allowed
r(198);

It may help if I gave an example of the data

* Example generated by -dataex-. For more info, type help dataex
clear
input float(total_CT_AT pre_year_cat) int age_grp
0 1 2
0 1 0
0 1 2
0 1 2
0 1 0
0 1 2
0 1 1
0 1 2
0 1 0
0 1 0
0 1 3
0 1 2
0 1 2
0 1 1
0 1 0
0 1 2
1 1 1
0 1 2
0 1 2
0 1 1
0 1 3
0 1 0
0 1 3
0 1 2
0 1 0
1 1 3
1 1 0
0 1 1
0 1 1
0 1 3
0 1 1
0 1 1
0 1 2
0 1 1
0 1 0
0 1 3
0 1 1
0 1 2
0 1 1
1 1 2
0 1 2
0 1 0
0 1 3
0 1 1
0 1 1
0 1 2
0 1 1
0 1 3
0 1 1
0 1 3
0 1 0
0 1 1
0 1 0
0 1 2
0 1 0
0 1 0
0 1 1
0 1 2
0 1 1
0 1 1
1 1 3
1 1 3
0 1 0
0 1 0
0 1 3
0 1 3
1 1 0
0 1 0
0 1 2
0 1 1
1 1 3
0 1 3
0 1 1
1 1 0
0 1 0
0 1 0
0 1 3
0 1 2
0 1 0
0 1 3
0 1 1
0 1 3
0 1 2
0 1 3
0 1 1
0 1 1
0 1 0
0 1 1
0 1 0
0 1 2
0 1 2
0 1 3
0 1 3
0 1 1
0 1 3
0 1 1
0 1 1
0 1 1
0 1 2
1 1 3
end
label values pre_year_cat pre_year_cat
label def pre_year_cat 1 "2015", modify
label values age_grp age_grp
label def age_grp 0 "18-34", modify
label def age_grp 1 "35-54", modify
label def age_grp 2 "55-74", modify
label def age_grp 3 "75+", modify

[Clyde Schechter]

Sorry, my mistake:

margins pre_year_cat, expression(1000*predict())

No -pr- inside -predict()-.

[Fouziah Almouqati]

Thank you, it worked.