Hi guys, i'm relatively new with sas and i have this data set to analyze but I don't know how to go about it. I hope to get help here. i am trying to use a model to predict growth using three different planting techniques. The data is written below:
| Block | Treatment | Height (ft) | Weight (g) | GLD (in) |
| 1 | Fever | 5.29 | 337.7 | 32.2 |
| 1 | Fever | 8.21 | 1002.07 | 56.4 |
| 1 | Fever | 3.5 | 140.4 | 20.2 |
| 1 | Control | 5.875 | 346.19 | 39.41 |
| 1 | Control | 2.54 | 52.72 | 15.32 |
| 1 | Control | 4.04 | 172.41 | 26.35 |
| 1 | Scramble | 3.41 | 172.08 | 24.5 |
| 1 | Scramble | 4.87 | 203.65 | 27.1 |
| 1 | Scramble | 3.75 | 162.29 | 24.5 |
| 2 | Fever | 5.54 | 345.41 | 34.75 |
| 2 | Fever | 7.25 | 796.15 | 49.29 |
| 2 | Fever | 3 | 154.13 | 22.99 |
| 2 | Control | 3.81 | 217.09 | 27.1 |
| 2 | Control | 4.29 | 195.8 | 25.55 |
| 2 | Control | 4.9 | 167.18 | 25.5 |
| 2 | Scramble | 3.08 | 52.69 | 15.7 |
| 2 | Scramble | 5.58 | 374.04 | 39.25 |
| 2 | Scramble | 2.33 | 40.45 | 11.9 |
| 3 | Fever | 6.21 | 547.04 | 44.5 |
| 3 | Fever | 6.58 | 249.21 | 32 |
| 3 | Fever | 4.68 | 184.06 | 26.5 |
| 3 | Control | 7.1 | 335.9 | 36.3 |
| 3 | Control | 4.125 | 38.55 | 10.69 |
| 3 | Control | 4.58 | 201.48 | 25.65 |
| 3 | Scramble | 3.47 | 280.99 | 28.3 |
| 3 | Scramble | 3.1 | 117.84 | 16.8 |
| 3 | Scramble | 4.21 | 205.43 | 26.1 |
I have an idea of what the model should look like.
W= a(GLD) x Hb
Note: 'b' is a superscript
I hope someone can help me with the statement to use on the sas software.
Any question/recommendation is welcome. Thank you
If I understand you correct, PROC NLIN would be my first choice
data have;
input Block Treatment $ Height Weight GLD;
datalines;
1 Fever 5.29 337.7 32.2
1 Fever 8.21 1002.07 56.4
1 Fever 3.5 140.4 20.2
1 Control 5.875 346.19 39.41
1 Control 2.54 52.72 15.32
1 Control 4.04 172.41 26.35
1 Scramble 3.41 172.08 24.5
1 Scramble 4.87 203.65 27.1
1 Scramble 3.75 162.29 24.5
2 Fever 5.54 345.41 34.75
2 Fever 7.25 796.15 49.29
2 Fever 3 154.13 22.99
2 Control 3.81 217.09 27.1
2 Control 4.29 195.8 25.55
2 Control 4.9 167.18 25.5
2 Scramble 3.08 52.69 15.7
2 Scramble 5.58 374.04 39.25
2 Scramble 2.33 40.45 11.9
3 Fever 6.21 547.04 44.5
3 Fever 6.58 249.21 32
3 Fever 4.68 184.06 26.5
3 Control 7.1 335.9 36.3
3 Control 4.125 38.55 10.69
3 Control 4.58 201.48 25.65
3 Scramble 3.47 280.99 28.3
3 Scramble 3.1 117.84 16.8
3 Scramble 4.21 205.43 26.1
;
proc nlin data=have list noitprint;
parms a 0 b 0;
model Weight = a*GLD + Height**b;
output out=ModelOut predicted=Pred;
run;
April 27 – 30 | Gaylord Texan | Grapevine, Texas
Walk in ready to learn. Walk out ready to deliver. This is the data and AI conference you can't afford to miss.
Register now and save with the early bird rate—just $795!
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.
Ready to level-up your skills? Choose your own adventure.