use margins command to predict y when x is not a factor variable

[Julie Lau]

sysuse auto
reg price weight rep78


I want to calculate predicted price for each observation with rep78 equal to the mean and weight equal to the actual value, and make the predicted price a new variable called yhat.
for example, observation 1: weight=2930, rep78=3 (the mean rep78 is 3.405797 for the whole sample). The predicted price for the first observation is calculated based on weight=2930, rep78=3.405797.

I tried "margins weight, atmeans" which reports error, because weight should be a factor variable instead, rather than continuous variable as it is.
I tried "margins, at((asobs) weight) atmeans", but the predicted price is based on weight equal to its mean, which is not what I want.

What's more, I don't know how to generate a new variable yhat equal to the predicted price.

Thanks a lot!

[Andrew Musau]

sysuse auto, clear
reg price weight rep78
levelsof weight if e(sample), local(wlevels)
sum rep78 if e(sample)
margins, at(rep78=(`r(mean)') weight=(`wlevels'))
*IN THIS CASE, EQUIVALENT TO
sum rep78 if e(sample)
margins, at(rep78=(`r(mean)')) over(weight)

Res.:

. margins, at(rep78=(`r(mean)') weight=(`wlevels'))

Adjusted predictions                            Number of obs     =         69
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : weight          =        1760
               rep78           =    3.405797

2._at        : weight          =        1800
               rep78           =    3.405797

3._at        : weight          =        1830
               rep78           =    3.405797

4._at        : weight          =        1930
               rep78           =    3.405797

5._at        : weight          =        1980
               rep78           =    3.405797

6._at        : weight          =        1990
               rep78           =    3.405797

7._at        : weight          =        2020
               rep78           =    3.405797

8._at        : weight          =        2040
               rep78           =    3.405797

9._at        : weight          =        2050
               rep78           =    3.405797

10._at       : weight          =        2070
               rep78           =    3.405797

11._at       : weight          =        2110
               rep78           =    3.405797

12._at       : weight          =        2120
               rep78           =    3.405797

13._at       : weight          =        2130
               rep78           =    3.405797

14._at       : weight          =        2160
               rep78           =    3.405797

15._at       : weight          =        2200
               rep78           =    3.405797

16._at       : weight          =        2240
               rep78           =    3.405797

17._at       : weight          =        2280
               rep78           =    3.405797

18._at       : weight          =        2370
               rep78           =    3.405797

19._at       : weight          =        2410
               rep78           =    3.405797

20._at       : weight          =        2580
               rep78           =    3.405797

21._at       : weight          =        2650
               rep78           =    3.405797

22._at       : weight          =        2670
               rep78           =    3.405797

23._at       : weight          =        2690
               rep78           =    3.405797

24._at       : weight          =        2730
               rep78           =    3.405797

25._at       : weight          =        2750
               rep78           =    3.405797

26._at       : weight          =        2830
               rep78           =    3.405797

27._at       : weight          =        2930
               rep78           =    3.405797

28._at       : weight          =        3170
               rep78           =    3.405797

29._at       : weight          =        3180
               rep78           =    3.405797

30._at       : weight          =        3200
               rep78           =    3.405797

31._at       : weight          =        3210
               rep78           =    3.405797

32._at       : weight          =        3220
               rep78           =    3.405797

33._at       : weight          =        3250
               rep78           =    3.405797

34._at       : weight          =        3260
               rep78           =    3.405797

35._at       : weight          =        3280
               rep78           =    3.405797

36._at       : weight          =        3300
               rep78           =    3.405797

37._at       : weight          =        3310
               rep78           =    3.405797

38._at       : weight          =        3330
               rep78           =    3.405797

39._at       : weight          =        3350
               rep78           =    3.405797

40._at       : weight          =        3370
               rep78           =    3.405797

41._at       : weight          =        3400
               rep78           =    3.405797

42._at       : weight          =        3430
               rep78           =    3.405797

43._at       : weight          =        3470
               rep78           =    3.405797

44._at       : weight          =        3600
               rep78           =    3.405797

45._at       : weight          =        3670
               rep78           =    3.405797

46._at       : weight          =        3690
               rep78           =    3.405797

47._at       : weight          =        3700
               rep78           =    3.405797

48._at       : weight          =        3720
               rep78           =    3.405797

49._at       : weight          =        3740
               rep78           =    3.405797

50._at       : weight          =        3830
               rep78           =    3.405797

51._at       : weight          =        3880
               rep78           =    3.405797

52._at       : weight          =        3900
               rep78           =    3.405797

53._at       : weight          =        4030
               rep78           =    3.405797

54._at       : weight          =        4060
               rep78           =    3.405797

55._at       : weight          =        4080
               rep78           =    3.405797

56._at       : weight          =        4130
               rep78           =    3.405797

57._at       : weight          =        4290
               rep78           =    3.405797

58._at       : weight          =        4330
               rep78           =    3.405797

59._at       : weight          =        4720
               rep78           =    3.405797

60._at       : weight          =        4840
               rep78           =    3.405797

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3082.997    576.826     5.34   0.000     1931.327    4234.667
          2  |   3179.317    563.154     5.65   0.000     2054.944     4303.69
          3  |   3251.557   552.9735     5.88   0.000      2147.51    4355.604
          4  |   3492.357   519.5451     6.72   0.000     2455.052    4529.662
          5  |   3612.757   503.1579     7.18   0.000      2608.17    4617.344
          6  |   3636.837   499.9094     7.27   0.000     2638.736    4634.938
          7  |   3709.077   490.2251     7.57   0.000     2730.311    4687.843
          8  |   3757.237   483.8221     7.77   0.000     2791.255    4723.219
          9  |   3781.317   480.6371     7.87   0.000     2821.694     4740.94
         10  |   3829.477   474.3015     8.07   0.000     2882.504    4776.451
         11  |   3925.797   461.7738     8.50   0.000     3003.836    4847.758
         12  |   3949.877   458.6732     8.61   0.000     3034.107    4865.648
         13  |   3973.957   455.5857     8.72   0.000     3064.351    4883.563
         14  |   4046.197   446.4042     9.06   0.000     3154.923    4937.472
         15  |   4142.517   434.3618     9.54   0.000     3275.286    5009.749
         16  |   4238.837   422.5656    10.03   0.000     3395.158    5082.517
         17  |   4335.157   411.0366    10.55   0.000     3514.496    5155.818
         18  |   4551.877   386.1966    11.79   0.000     3780.811    5322.944
         19  |   4648.197   375.7066    12.37   0.000     3898.075     5398.32
         20  |   5057.557   335.7728    15.06   0.000     4387.165    5727.949
         21  |   5226.117   321.9603    16.23   0.000     4583.303    5868.932
         22  |   5274.277   318.3439    16.57   0.000     4638.683    5909.871
         23  |   5322.437   314.8836    16.90   0.000     4693.752    5951.123
         24  |   5418.757   308.4524    17.57   0.000     4802.912    6034.603
         25  |   5466.917   305.4918    17.90   0.000     4856.983    6076.852
         26  |   5659.558   295.4648    19.15   0.000     5069.643    6249.472
         27  |   5900.358   287.3467    20.53   0.000     5326.651    6474.064
         28  |   6478.278   289.6729    22.36   0.000     5899.927    7056.628
         29  |   6502.358   290.4399    22.39   0.000     5922.476     7082.24
         30  |   6550.518   292.1277    22.42   0.000     5967.266    7133.769
         31  |   6574.598   293.0476    22.44   0.000     5989.509    7159.686
         32  |   6598.678   294.0175    22.44   0.000     6011.653    7185.703
         33  |   6670.918   297.2227    22.44   0.000     6077.493    7264.342
         34  |   6694.998   298.3877    22.44   0.000     6099.247    7290.748
         35  |   6743.158   300.8595    22.41   0.000     6142.472    7343.843
         36  |   6791.318   303.5163    22.38   0.000     6185.328    7397.307
         37  |   6815.398   304.9125    22.35   0.000      6206.62    7424.175
         38  |   6863.558   307.8377    22.30   0.000      6248.94    7478.175
         39  |   6911.718   310.9356    22.23   0.000     6290.915    7532.521
         40  |   6959.878   314.2011    22.15   0.000     6332.555      7587.2
         41  |   7032.118   319.4022    22.02   0.000     6394.411    7669.825
         42  |   7104.358   324.9513    21.86   0.000     6455.572    7753.144
         43  |   7200.678    332.861    21.63   0.000     6536.099    7865.256
         44  |   7513.718   362.1401    20.75   0.000     6790.682    8236.754
         45  |   7682.278   379.8435    20.22   0.000     6923.896     8440.66
         46  |   7730.438   385.1159    20.07   0.000     6961.529    8499.347
         47  |   7754.518   387.7854    20.00   0.000     6980.279    8528.756
         48  |   7802.678   393.1887    19.84   0.000     7017.651    8587.704
         49  |   7850.838   398.6749    19.69   0.000     7054.858    8646.818
         50  |   8067.558   424.3015    19.01   0.000     7220.413    8914.703
         51  |   8187.958   439.1329    18.65   0.000     7311.201    9064.715
         52  |   8236.118   445.1718    18.50   0.000     7347.304    9124.932
         53  |   8549.158   485.7198    17.60   0.000     7579.387    9518.929
         54  |   8621.398   495.3597    17.40   0.000      7632.38    9610.416
         55  |   8669.558   501.8385    17.28   0.000     7667.605    9671.511
         56  |   8789.958   518.2065    16.96   0.000     7755.325    9824.591
         57  |   9175.238   572.0086    16.04   0.000     8033.186    10317.29
         58  |   9271.558   585.7497    15.83   0.000     8102.071    10441.05
         59  |   10210.68   724.0918    14.10   0.000     8764.982    11656.37
         60  |   10499.64   767.8462    13.67   0.000     8966.584    12032.69
------------------------------------------------------------------------------

.

What's more, I don't know how to generate a new variable yhat equal to the predicted price.

sysuse auto, clear
reg price weight rep78
predict yhat, xb

See

help predict

[Nick Cox]

Cross-posted at https://stackoverflow.com/questions/...actor-variable

Please note our policy on cross-posting, which is that you are asked to tell us about it. (The same is good practice anywhere, regardless of whether it is explicit policy.)