Reporting margins in a detailed tables

This post show how to report margsin after a regression model. In case you are interested in reporting a nested table or several margin equations, please read this post.

For creating a table of margins after any regression command, we shall start with estimating the regression model. In the next command where we run the `margins `command, we shall add asdocx to the beginning of the line. Also, we can add other asdocx options after comma, such the option `dec() `and `replace`. In the following example, I create a detailed table of margins.

```* Load some example data
webuse fullauto

* Create a factor variable for
gen x = mod(_n,2)

* Estimate regression without using asdocx
ologit rep77 i.foreign i.x length mpg

* add asdocx to the margin command: replace, nest and tzok belong to asdocx
asdocx margins foreign x, replace save(ologi.xlsx) abb(.)```
Predictive margins
Coef. St.Err. t-value p-value [95% Co Interval] Sig
1bn._predict#0bn.foreign 0.105 0.055 1.91 0.056 -0.003 0.213 *
1bn._predict#1.foreign 0.007 0.006 1.27 0.203 -0.004 0.018
1bn._predict#0bn.x 0.053 0.03 1.75 0.079 -0.006 0.112 *
1bn._predict#1.x 0.042 0.024 1.71 0.087 -0.006 0.089 *
2._predict#0bn.foreign 0.255 0.062 4.09 0 0.133 0.378 ***
2._predict#1.foreign 0.038 0.02 1.92 0.054 -0.001 0.077 *
2._predict#0bn.x 0.182 0.054 3.40 0.001 0.077 0.287 ***
2._predict#1.x 0.154 0.046 3.37 0.001 0.064 0.244 ***
3._predict#0bn.foreign 0.412 0.064 6.49 0 0.288 0.537 ***
3._predict#1.foreign 0.196 0.062 3.17 0.002 0.075 0.317 ***
3._predict#0bn.x 0.413 0.059 7.01 0 0.298 0.529 ***
3._predict#1.x 0.402 0.06 6.75 0 0.286 0.519 ***
4._predict#0bn.foreign 0.2 0.046 4.34 0 0.11 0.29 ***
4._predict#1.foreign 0.468 0.094 4.99 0 0.284 0.651 ***
4._predict#0bn.x 0.284 0.06 4.75 0 0.167 0.402 ***
4._predict#1.x 0.319 0.061 5.20 0 0.199 0.439 ***
5._predict#0bn.foreign 0.027 0.015 1.78 0.075 -0.003 0.057 *
5._predict#1.foreign 0.291 0.121 2.41 0.016 0.054 0.527 **
5._predict#0bn.x 0.068 0.028 2.38 0.017 0.012 0.123 **
5._predict#1.x 0.083 0.036 2.32 0.02 0.013 0.153 **
Mean dependent var 0.500 SD dependent var 0.504