Reporting Adjusted R2 on Stata

[Paul Bahk]

Hello Everyone! I am new to STATA and I am having trouble reporting the Adjusted R2

When I run a regression with fixed effects and clustered standard errors, only the R2 is reported... Is there a way to include the adjusted R2 as well??
Thank you!

[Carlo Lazzaro]

Paul:
yes, you can recall it after -regress-:

use "https://www.stata-press.com/data/r16/nlswork.dta"
. reg ln_wage c.age##c.age i.idcode i.year if idcode<=4, vce(cluster idcode)

Linear regression                               Number of obs     =         50
                                                F(2, 3)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6590
                                                Root MSE          =     .28764

                                 (Std. Err. adjusted for 4 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .2364322   .2603253     0.91   0.431     -.592039    1.064903
             |
 c.age#c.age |  -.0056102   .0021351    -2.63   0.078    -.0124052    .0011848
             |
      idcode |
          2  |   -.394374   .0296125   -13.32   0.001    -.4886143   -.3001337
          3  |   .1010589   1.385597     0.07   0.946     -4.30853    4.510648
          4  |   .5522662   1.411066     0.39   0.722    -3.938377    5.042909
             |
        year |
         69  |    .224994   .2362011     0.95   0.411    -.5267035    .9766914
         70  |   .1649135   .4084011     0.40   0.713    -1.134801    1.464628
         71  |   .1712431   .5615442     0.30   0.780    -1.615841    1.958327
         72  |   .3136266   .8258606     0.38   0.729     -2.31463    2.941883
         73  |   .4386164   1.160727     0.38   0.731    -3.255333    4.132566
         75  |    .575746   1.642352     0.35   0.749     -4.65095    5.802442
         77  |    .651904   2.091338     0.31   0.776    -6.003666    7.307474
         78  |   .9314592   2.431102     0.38   0.727    -6.805391     8.66831
         80  |   .9700889   3.035563     0.32   0.770    -8.690428    10.63061
         82  |   1.063147   3.462289     0.31   0.779    -9.955403     12.0817
         83  |   1.379563   3.652919     0.38   0.731    -10.24566    13.00478
         85  |   1.854813   3.994786     0.46   0.674    -10.85838      14.568
         87  |   2.153058   4.439545     0.48   0.661    -11.97556    16.28167
         88  |   2.571633   4.744555     0.54   0.625    -12.52766    17.67092
             |
       _cons |  -1.093667   4.089099    -0.27   0.806    -14.10701    11.91967
------------------------------------------------------------------------------

. di e(r2_a)
.44310999

.

[Paul Bahk]

Paul:
yes, you can recall it after -regress-:

use "https://www.stata-press.com/data/r16/nlswork.dta"
. reg ln_wage c.age##c.age i.idcode i.year if idcode<=4, vce(cluster idcode)

Linear regression Number of obs = 50
F(2, 3) = .
Prob > F = .
R-squared = 0.6590
Root MSE = .28764

(Std. Err. adjusted for 4 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .2364322 .2603253 0.91 0.431 -.592039 1.064903
|
c.age#c.age | -.0056102 .0021351 -2.63 0.078 -.0124052 .0011848
|
idcode |
2 | -.394374 .0296125 -13.32 0.001 -.4886143 -.3001337
3 | .1010589 1.385597 0.07 0.946 -4.30853 4.510648
4 | .5522662 1.411066 0.39 0.722 -3.938377 5.042909
|
year |
69 | .224994 .2362011 0.95 0.411 -.5267035 .9766914
70 | .1649135 .4084011 0.40 0.713 -1.134801 1.464628
71 | .1712431 .5615442 0.30 0.780 -1.615841 1.958327
72 | .3136266 .8258606 0.38 0.729 -2.31463 2.941883
73 | .4386164 1.160727 0.38 0.731 -3.255333 4.132566
75 | .575746 1.642352 0.35 0.749 -4.65095 5.802442
77 | .651904 2.091338 0.31 0.776 -6.003666 7.307474
78 | .9314592 2.431102 0.38 0.727 -6.805391 8.66831
80 | .9700889 3.035563 0.32 0.770 -8.690428 10.63061
82 | 1.063147 3.462289 0.31 0.779 -9.955403 12.0817
83 | 1.379563 3.652919 0.38 0.731 -10.24566 13.00478
85 | 1.854813 3.994786 0.46 0.674 -10.85838 14.568
87 | 2.153058 4.439545 0.48 0.661 -11.97556 16.28167
88 | 2.571633 4.744555 0.54 0.625 -12.52766 17.67092
|
_cons | -1.093667 4.089099 -0.27 0.806 -14.10701 11.91967
------------------------------------------------------------------------------

. di e(r2_a)
.44310999

.

Thank you so much for your reply Carlo!!

May I ask one more question please??? I see that you typed c.age##c.age on the example regression above. Is there a difference between using one # or using two ## ??

[Carlo Lazzaro]

Paul:
yes, there is.
Single #: interaction only.
Double #: interaction + conditional main effect of the interacted terms (if they differ).
Double #: linear + square term of the same predictor (this is the case of my previous example).
See -fvvarlist- for further details.