I am supposed to generate a linear regression with multiple conditions.

Data is as shown below:

Code:
input byte id float time int conc str8 diet float(elim pred lnalcohol pred_rep)
1    3  75 "noGuar"   .31319055 4.3103285  4.317488 4.4182615
1  3.5  64 "noGuar"   .31319055 4.1537333  4.158883 4.2178164
1    4  53 "noGuar"   .31319055  3.997138  3.970292 4.0173717
1  4.5  47 "noGuar"   .31319055  3.840543 3.8501475  3.816927
1    5  40 "noGuar"   .31319055 3.6839476 3.6888795  3.616482
1  3.5  65 "withGuar" .28823623   4.18296 4.1743875 4.2178164
1    4  57 "withGuar" .28823623 4.0388417 4.0430512 4.0173717
1  4.5  50 "withGuar" .28823623  3.894724  3.912023  3.816927
1    5  42 "withGuar" .28823623 3.7506056   3.73767  3.616482
2    3  82 "noGuar"    .4008896 4.4182615  4.406719 4.4182615
2  3.5  68 "noGuar"    .4008896 4.2178164 4.2195077 4.2178164
2    4  57 "noGuar"    .4008896 4.0173717 4.0430512 4.0173717
2  4.5  45 "noGuar"    .4008896  3.816927 3.8066626  3.816927
2    5  37 "noGuar"    .4008896  3.616482  3.610918  3.616482
2 1.33 122 "withGuar" .22233166  4.846237  4.804021  5.087747
2 1.67 124 "withGuar" .22233166  4.770644  4.820282  4.951445
2    2 108 "withGuar" .22233166  4.697274 4.6821313  4.819151
2  2.5 103 "withGuar" .22233166 4.5861087  4.634729  4.618706
2    3  92 "withGuar" .22233166 4.4749427 4.5217886 4.4182615
2  3.5  65 "withGuar" .22233166 4.3637767 4.1743875 4.2178164
2    4  75 "withGuar" .22233166  4.252611  4.317488 4.0173717
2  4.5  68 "withGuar" .22233166  4.141445 4.2195077  3.816927
2    5  54 "withGuar" .22233166  4.030279  3.988984  3.616482
3    4  17 "noGuar"   1.2237754    2.8603  2.833213 4.0173717
3  4.5  10 "noGuar"   1.2237754 2.2484121 2.3025851  3.816927
3    5   5 "noGuar"   1.2237754 1.6365244  1.609438  3.616482
3    4  26 "withGuar"  .9555115 3.2947736 3.2580965 4.0173717
3  4.5  18 "withGuar"  .9555115  2.817018  2.890372  3.816927
3    5  10 "withGuar"  .9555115  2.339262 2.3025851  3.616482
4  3.5  37 "noGuar"   .57818496  3.668399  3.610918 4.2178164
4    4  30 "noGuar"   .57818496 3.3793066 3.4011974 4.0173717
4  4.5  25 "noGuar"   .57818496  3.090214  3.218876  3.816927
4    5  15 "noGuar"   .57818496  2.801122   2.70805  3.616482
4  2.5  60 "withGuar"  .3793953 4.0677114 4.0943446  4.618706
4    3  46 "withGuar"  .3793953  3.878014 3.8286414 4.4182615
4  3.5  40 "withGuar"  .3793953  3.688316 3.6888795 4.2178164
4    4  35 "withGuar"  .3793953 3.4986186  3.555348 4.0173717
4  4.5  26 "withGuar"  .3793953  3.308921 3.2580965  3.816927
4    5  23 "withGuar"  .3793953  3.119223  3.135494  3.616482
5    4  20 "noGuar"    1.609438  3.032923  2.995732 4.0173717
5  4.5  10 "noGuar"    1.609438  2.228204 2.3025851  3.816927
5    5   4 "noGuar"    1.609438  1.423485 1.3862944  3.616482
5    4  22 "withGuar"  1.299283  3.105544 3.0910425 4.0173717
5  4.5  12 "withGuar"  1.299283  2.455903  2.484907  3.816927
5    5   6 "withGuar"  1.299283 1.8062614 1.7917595  3.616482
6    4  25 "noGuar"    .9162908 3.2620895  3.218876 4.0173717
6  4.5  18 "noGuar"    .9162908  2.803944  2.890372  3.816927
6    5  10 "noGuar"    .9162908  2.345799 2.3025851  3.616482
6 1.67  50 "withGuar"  .3639005  3.892754  3.912023  4.951445
6    2  42 "withGuar"  .3639005  3.772667   3.73767  4.819151
6  2.5  32 "withGuar"  .3639005  3.590717  3.465736  4.618706
6    3  35 "withGuar"  .3639005 3.4087665  3.555348 4.4182615
6  3.5  26 "withGuar"  .3639005  3.226816 3.2580965 4.2178164
6    4  23 "withGuar"  .3639005  3.044866  3.135494 4.0173717
6  4.5  15 "withGuar"  .3639005  2.862916   2.70805  3.816927
6    5  15 "withGuar"  .3639005 2.6809654   2.70805  3.616482
7    4  40 "noGuar"    .3566749  3.703815 3.6888795 4.0173717
7  4.5  35 "noGuar"    .3566749 3.5254774  3.555348  3.816927
7    5  28 "noGuar"    .3566749   3.34714 3.3322046  3.616482
7    3  61 "withGuar"  .1965659  4.101937 4.1108737 4.4182615
7  3.5  55 "withGuar"  .1965659  4.003654 4.0073333 4.2178164
7    4  48 "withGuar"  .1965659  3.905371  3.871201 4.0173717
7  4.5  46 "withGuar"  .1965659  3.807088 3.8286414  3.816927
end
My current code:

Code:
regress lnalcohol time if diet == "withGuar" & id == 1 
regress lnalcohol time if diet == "noGuar" & id == 1
regress lnalcohol time if diet == "withGuar" & id == 2
regress lnalcohol time if diet == "noGuar" & id == 2
Code:
predict pred_rep, xb
I am supposed to verify pred, with the new variable, pred_rep which is similar to pred. To do this, I need to create a regression line. The current problem is that I only get the correct pred_rep values for the last line of code. In this case,
Code:
regress lnalcohol time if diet == "noGuar" & id == 2
gives me the correct output for pred_rep but not for the other diet/id combinations.

How do I combine the regression codes above to form one output to resolve this problem?