Dear all,
I am struggling to understand the -adjust- command after regression
involving categorical variables. My aim in using -adjust- is to obtain
the predicted values adjusted for the categorical variable, but I am not
explicitly interested in the categorical variable and so do not want it
appearing in the -by()- option of -adjust-. I have been unable to find
any examples of this kind of use of -adjust-. I have reproduced my query
using the auto dataset below. I am using Stata 10.1 SE.
sysuse auto, clear
descr
** just for this example, assume that rep78 is categorical
xi: regress price weight turn i.rep i.foreign
** output
price Coef. Std. Err. t P>t [95%
Conf. Interval]
weight 4.243125 .6699849 6.33 0.000 2.903407
5.582842
turn -208.6987 125.9326 -1.66 0.103 -460.5164
43.11914
_Irep78_2 822.0914 1691.818 0.49 0.629 -2560.907
4205.09
_Irep78_3 710.281 1560.7 0.46 0.651 -2410.531
3831.093
_Irep78_4 341.2531 1631.858 0.21 0.835 -2921.848
3604.355
_Irep78_5 876.4049 1740.224 0.50 0.616 -2603.387
4356.197
_Iforeign_1 3239.838 859.1453 3.77 0.000 1521.871
4957.805
_cons -32.54137 4097.528 -0.01 0.994 -8226.054
8160.972
** want the predicted values by foreign - not specifically interested in
rep78 but wanted to adjust for it, but I am unsure as to how to treat
rep78
** option 1 - set continuous values to mean but leave rep78 as is
adjust weight turn, by(foreign)
** output
----------------------
Car type | xb
----------+-----------
Domestic | 5164.18
Foreign | 8390.29
----------------------
** However, you see that 8390.29-5164.18=3226.11, and not 3239.838 as
predicted by the model above
** option 2 - treat dummies created by -xi- as continuous, and also set
them to their mean
adjust weight turn _Irep78_2 _Irep78_3 _Irep78_4 _Irep78_5, by(foreign)
** output
----------------------
Car type | xb
----------+-----------
Domestic | 5160.01
Foreign | 8399.84
----------------------
You see that the final -adjust- command gives 8399.84-5160.01=3239.83, as
given by the regression model above. So it appears that the second
treatment of the categorical gives the 'correct' predictions. However, I
am struggling to interpret exactly what this means for rep78, and does it
make sense to set variables that are 0/1 to their mean?
I would be extremely grateful for any assistance with this.
Many thanks,
Gillian
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