Hi Everyone,
I need your help to be able to use the appropriate statistical procedure for my dataset below
Here is the detail of the data.
sub_id = subjects, measurement is taken on subject at different time point.
position: where measurement is taken (anterior or posterior)
time_in_seconds: time to apply treatment
resistant:
force: force of treatment application
The y variable is whether there is a block after treatment has been applied i.e. y =1 or there is a gap y= 0 and it also contains missing data.
Question:
I want to know how Time+Force+resistance affect the outcome i.e y =gap/block
Sub_ID | Position | Time_in_Seconds | Resistance_ ohms | Force_N | y_variable |
1 | Ant | 3.82 | -23 | 37 | 1 |
1 | Ant | 4.94 | -24 | 4.4 | 0 |
1 | Ant | 3.49 | -41.5 | 15.8 | 1 |
1 | Ant | 3.07 | -39.5 | 21.15 | 1 |
1 | Post | 3.43 | -39 | 29 | 1 |
1 | Post | 3.53 | -45.5 | 14.15 | 1 |
1 | Post | 4.44 | -46 | 9.55 | 1 |
1 | Post | 3.37 | -19 | 12.9 | 1 |
1 | Ant | 3.35 | -46.5 | 7.2 | 1 |
1 | Ant | 3.19 | -43 | 14.2 | 1 |
1 | Ant | 3.61 | -41 | 24.55 | 1 |
1 | Ant | 4.24 | -48 | 23.15 | 1 |
1 | Post | 2.09 | -33 | 27.25 | 1 |
1 | Post | 2.83 | -32 | 21.2 | 1 |
1 | Post | 3.26 | -28 | 29.7 | 0 |
1 | Post | 3.34 | -41.5 | 10.15 | 1 |
2 | Ant | 5.29 | -30 | 10.9 | 1 |
2 | Ant | 4.22 | -29 | 11.3 | 1 |
2 | Ant | 2.40 | -15 | 10.2 | 1 |
2 | Ant | 3.32 | -18.5 | 8.75 | 1 |
2 | Post | 1.85 | -27 | 9.3 | 1 |
2 | Post | 4.31 | -37 | 8.75 | 1 |
2 | Post | 1.82 | -29.5 | 13.95 | 1 |
2 | Post | 3.98 | -24.5 | 9.8 | 0 |
2 | Ant | 4.06 | -39.5 | 21.45 | 1 |
2 | Ant | 2.90 | -35.5 | 16.5 | 1 |
2 | Ant | 3.23 | -35.5 | 18.2 | 1 |
2 | Ant | 4.24 | -31 | 14.3 | 1 |
2 | Post | 2.45 | -31 | 9.6 | 1 |
2 | Post | 3.51 | -20 | 6 | 1 |
2 | Post | 4.27 | -17.5 | 8.4 | 1 |
2 | Post | 2.67 | -25.5 | 25 | 0 |
3 | Ant | 3.065092996 | -40 | 10.7 | 1 |
3 | Ant | 3.74 | -38 | 17.8 | . |
3 | Ant | 3.61 | -27 | 10.1 | 0 |
3 | Ant | 2.08 | -26.5 | 6.45 | . |
3 | Post | 2.12 | -35 | 20.4 | 1 |
3 | Post | 3.244 | -39 | 27.5 | 1 |
3 | Post | 4.02 | -42 | 19.9 | 1 |
3 | Post | 1.94 | -19 | 16.6 | 1 |
3 | Ant | 4.37 | -14 | 4.2 | 0 |
3 | Ant | 4.68 | -33 | 6.9 | 0 |
3 | Ant | 3.35 | -30.5 | 8.65 | 1 |
3 | Ant | 1.72 | -33 | 14.1 | 1 |
3 | Post | 0.81 | -27 | 12.2 | 1 |
3 | Post | 3.90 | -26 | 18.35 | 1 |
3 | Post | 4.19 | -29 | 9.4 | 1 |
3 | Post | 4.46 | -19 | 10.2 | 1 |
4 | Ant | 2.89 | -42 | 11.3 | 1 |
4 | Ant | 2.20 | -28 | 12.45 | 1 |
4 | Ant | 2.97 | -31 | 19.5 | 1 |
4 | Ant | 2.06 | -31 | 22.3 | 1 |
4 | Post | 3.35 | -44.5 | 32.9 | 1 |
4 | Post | 2.10 | -35 | 15.3 | 1 |
4 | Post | 3.42 | -35 | 8.35 | 1 |
4 | Post | 4.16 | -33 | 20.9 | 1 |
4 | Ant | 4.06 | -15.5 | 6 | 1 |
4 | Ant | 5.00 | -25 | 21.5 | 1 |
4 | Ant | 4.13 | -33.5 | 24.25 | 1 |
4 | Ant | 5.56 | -34 | 16.7 | 1 |
4 | Post | 4.14 | -35 | 31.75 | 1 |
4 | Post | 4.49 | -33.5 | 25.4 | 1 |
4 | Post | 4.17 | -29 | 41.9 | 1 |
4 | Post | 3.85 | -28 | 28.8 | 1 |
5 | Ant | 2.67 | -23 | 28.2 | 0 |
5 | Ant | 1.68 | -23 | 10.3 | 1 |
5 | Ant | 2.07 | -19.5 | 9.85 | 1 |
5 | Ant | 1.06 | -25 | 12.7 | 1 |
5 | Post | 5.02 | -31 | 10.4 | 0 |
5 | Post | 2.53 | -23 | 11.7 | 1 |
5 | Post | 3.40 | -71.5 | 27.15 | 1 |
5 | Post | 4.78 | -41.5 | 31.85 | 1 |
5 | Ant | 2.15 | -42 | 15.95 | 1 |
5 | Ant | 2.89 | -26.5 | 16.9 | 1 |
5 | Ant | 2.25 | -33 | 9.8 | 1 |
5 | Ant | 2.76 | -28 | 12.1 | 0 |
5 | Post | 3.04 | -22 | 9 | 1 |
5 | Post | 4.45 | -37.5 | 9.25 | 1 |
5 | Post | 4.10 | -20.5 | 15.3 | 1 |
5 | Post | 4.41 | -31.5 | 18.05 | 1 |
6 | Ant | 1.61 | -26 | 18.7 | 1 |
6 | Ant | 1.68 | -26 | 7.4 | 0 |
6 | Ant | 3.93 | -29 | 7.4 | 0 |
6 | Ant | 4.45 | -21.5 | 9.15 | . |
6 | Post | 5.48 | -28 | 25.05 | 1 |
6 | Post | 4.11 | -48 | 30.7 | 1 |
6 | Post | 3.20 | -23 | 22.3 | 1 |
6 | Post | 2.77 | -28 | 15.3 | 1 |
6 | Ant | 2.34 | -22 | 10.95 | 1 |
6 | Ant | 2.25 | -30 | 6.2 | 1 |
6 | Ant | 4.16 | -24.5 | 6.6 | |
6 | Ant | 4.62 | -33 | 12 | 0 |
6 | Post | 2.32 | -31 | 16.65 | 1 |
6 | Post | 4.03 | -31 | 19 | 1 |
6 | Post | 3.43 | -17 | 12 | . |
6 | Post | 3.51 | -14 | 11.1 | 0 |
7 | Ant | 2.99 | -30 | 7.65 | 1 |
7 | Ant | 1.80517419 | -25 | 15.95 | 1 |
7 | Ant | 2.106053494 | -32 | 13.8 | 1 |
7 | Ant | 3.096114016 | -29 | 17.55 | 1 |
7 | Post | 3.167542074 | -24 | 11.45 | 1 |
7 | Post | 3.338268984 | -24 | 22.7 | 1 |
7 | Post | 2.659685183 | -32.5 | 21.95 | 1 |
7 | Post | 3.751749917 | -17 | 8.25 | 1 |
7 | Ant | 2.197529839 | -25 | 8.8 | 0 |
7 | Ant | 3.664137015 | -35 | 10.1 | 0 |
7 | Ant | 3.545702335 | -39 | 9.4 | 0 |
7 | Ant | 2.023625001 | -35 | 10.1 | 1 |
7 | Post | 2.372086883 | -33 | 24.25 | 1 |
7 | Post | 3.582104056 | -37.5 | 17.3 | 1 |
7 | Post | 3.45055168 | -38 | 12.6 | 1 |
7 | Post | 3.841677068 | -23 | 9.8 | 1 |
8 | Ant | 3.432008272 | -25 | 18.6 | 0 |
8 | Ant | 2.136605495 | -32.5 | 14.1 | 1 |
8 | Ant | 1.841190586 | -18 | 7 | 0 |
8 | Ant | 2.15865836 | -25.5 | 6.8 | 0 |
8 | Post | 3.359805409 | -30 | 15.5 | 1 |
8 | Post | 3.631259765 | -31 | 28 | 1 |
8 | Post | 4.674356585 | -32.5 | 20.9 | 1 |
8 | Post | 4.044977037 | -25 | 12.9 | 0 |
8 | Ant | 3.346860731 | -28 | 14.6 | 1 |
8 | Ant | 3.850582629 | -46 | 24.5 | 1 |
8 | Ant | 5.340021635 | -31.5 | 8.5 | 1 |
8 | Ant | 3.980653721 | -26 | 11.6 | 1 |
8 | Post | 3.704121331 | -26 | 14.8 | 1 |
8 | Post | 3.848852913 | -31.5 | 15.8 | 1 |
8 | Post | 4.939191479 | -18 | 9.8 | 1 |
8 | Post | 3.134066196 | -15 | 9.1 | 1 |
9 | Ant | 1.309024248 | -25 | 9 | 1 |
9 | Ant | 3.369404446 | -27 | 11.55 | 1 |
9 | Ant | 1.841284373 | -26 | 13.15 | 1 |
9 | Ant | 3.675231524 | -21 | 12.4 | 1 |
9 | Post | 3.06826061 | -22 | 24.2 | 1 |
9 | Post | 3.59966626 | -19 | 17.3 | 1 |
9 | Post | 4.466268907 | -16.5 | 15.7 | 1 |
9 | Post | 1.882204503 | -27 | 11.3 | . |
9 | Ant | 3.89896461 | -25 | 21.1 | 1 |
9 | Ant | 2.295202494 | -26 | 9 | 1 |
9 | Ant | 3.272687389 | -24.5 | 4.95 | 0 |
9 | Ant | 4.201883396 | -13 | 2.7 | 0 |
9 | Post | 4.374845784 | -25 | 10.8 | 0 |
9 | Post | 4.459715564 | -15.5 | 11.8 | 1 |
9 | Post | 3.227763303 | -19.5 | 21.05 | 0 |
9 | Post | 2.517233031 | -20.5 | 31.6 | 1 |
This may serve as a starting point for anyone wishing to tackle this question:
data have;
infile datalines truncover;
input ID Position $ Time Resistance Force y;
datalines;
1 Ant 3.82 -23 37 1
1 Ant 4.94 -24 4.4 0
1 Ant 3.49 -41.5 15.8 1
1 Ant 3.07 -39.5 21.15 1
1 Post 3.43 -39 29 1
1 Post 3.53 -45.5 14.15 1
1 Post 4.44 -46 9.55 1
1 Post 3.37 -19 12.9 1
1 Ant 3.35 -46.5 7.2 1
1 Ant 3.19 -43 14.2 1
1 Ant 3.61 -41 24.55 1
1 Ant 4.24 -48 23.15 1
1 Post 2.09 -33 27.25 1
1 Post 2.83 -32 21.2 1
1 Post 3.26 -28 29.7 0
1 Post 3.34 -41.5 10.15 1
2 Ant 5.29 -30 10.9 1
2 Ant 4.22 -29 11.3 1
2 Ant 2.40 -15 10.2 1
2 Ant 3.32 -18.5 8.75 1
2 Post 1.85 -27 9.3 1
2 Post 4.31 -37 8.75 1
2 Post 1.82 -29.5 13.95 1
2 Post 3.98 -24.5 9.8 0
2 Ant 4.06 -39.5 21.45 1
2 Ant 2.90 -35.5 16.5 1
2 Ant 3.23 -35.5 18.2 1
2 Ant 4.24 -31 14.3 1
2 Post 2.45 -31 9.6 1
2 Post 3.51 -20 6 1
2 Post 4.27 -17.5 8.4 1
2 Post 2.67 -25.5 25 0
3 Ant 3.065092996 -40 10.7 1
3 Ant 3.74 -38 17.8 .
3 Ant 3.61 -27 10.1 0
3 Ant 2.08 -26.5 6.45 .
3 Post 2.12 -35 20.4 1
3 Post 3.244 -39 27.5 1
3 Post 4.02 -42 19.9 1
3 Post 1.94 -19 16.6 1
3 Ant 4.37 -14 4.2 0
3 Ant 4.68 -33 6.9 0
3 Ant 3.35 -30.5 8.65 1
3 Ant 1.72 -33 14.1 1
3 Post 0.81 -27 12.2 1
3 Post 3.90 -26 18.35 1
3 Post 4.19 -29 9.4 1
3 Post 4.46 -19 10.2 1
4 Ant 2.89 -42 11.3 1
4 Ant 2.20 -28 12.45 1
4 Ant 2.97 -31 19.5 1
4 Ant 2.06 -31 22.3 1
4 Post 3.35 -44.5 32.9 1
4 Post 2.10 -35 15.3 1
4 Post 3.42 -35 8.35 1
4 Post 4.16 -33 20.9 1
4 Ant 4.06 -15.5 6 1
4 Ant 5.00 -25 21.5 1
4 Ant 4.13 -33.5 24.25 1
4 Ant 5.56 -34 16.7 1
4 Post 4.14 -35 31.75 1
4 Post 4.49 -33.5 25.4 1
4 Post 4.17 -29 41.9 1
4 Post 3.85 -28 28.8 1
5 Ant 2.67 -23 28.2 0
5 Ant 1.68 -23 10.3 1
5 Ant 2.07 -19.5 9.85 1
5 Ant 1.06 -25 12.7 1
5 Post 5.02 -31 10.4 0
5 Post 2.53 -23 11.7 1
5 Post 3.40 -71.5 27.15 1
5 Post 4.78 -41.5 31.85 1
5 Ant 2.15 -42 15.95 1
5 Ant 2.89 -26.5 16.9 1
5 Ant 2.25 -33 9.8 1
5 Ant 2.76 -28 12.1 0
5 Post 3.04 -22 9 1
5 Post 4.45 -37.5 9.25 1
5 Post 4.10 -20.5 15.3 1
5 Post 4.41 -31.5 18.05 1
6 Ant 1.61 -26 18.7 1
6 Ant 1.68 -26 7.4 0
6 Ant 3.93 -29 7.4 0
6 Ant 4.45 -21.5 9.15 .
6 Post 5.48 -28 25.05 1
6 Post 4.11 -48 30.7 1
6 Post 3.20 -23 22.3 1
6 Post 2.77 -28 15.3 1
6 Ant 2.34 -22 10.95 1
6 Ant 2.25 -30 6.2 1
6 Ant 4.16 -24.5 6.6
6 Ant 4.62 -33 12 0
6 Post 2.32 -31 16.65 1
6 Post 4.03 -31 19 1
6 Post 3.43 -17 12 .
6 Post 3.51 -14 11.1 0
7 Ant 2.99 -30 7.65 1
7 Ant 1.80517419 -25 15.95 1
7 Ant 2.106053494 -32 13.8 1
7 Ant 3.096114016 -29 17.55 1
7 Post 3.167542074 -24 11.45 1
7 Post 3.338268984 -24 22.7 1
7 Post 2.659685183 -32.5 21.95 1
7 Post 3.751749917 -17 8.25 1
7 Ant 2.197529839 -25 8.8 0
7 Ant 3.664137015 -35 10.1 0
7 Ant 3.545702335 -39 9.4 0
7 Ant 2.023625001 -35 10.1 1
7 Post 2.372086883 -33 24.25 1
7 Post 3.582104056 -37.5 17.3 1
7 Post 3.45055168 -38 12.6 1
7 Post 3.841677068 -23 9.8 1
8 Ant 3.432008272 -25 18.6 0
8 Ant 2.136605495 -32.5 14.1 1
8 Ant 1.841190586 -18 7 0
8 Ant 2.15865836 -25.5 6.8 0
8 Post 3.359805409 -30 15.5 1
8 Post 3.631259765 -31 28 1
8 Post 4.674356585 -32.5 20.9 1
8 Post 4.044977037 -25 12.9 0
8 Ant 3.346860731 -28 14.6 1
8 Ant 3.850582629 -46 24.5 1
8 Ant 5.340021635 -31.5 8.5 1
8 Ant 3.980653721 -26 11.6 1
8 Post 3.704121331 -26 14.8 1
8 Post 3.848852913 -31.5 15.8 1
8 Post 4.939191479 -18 9.8 1
8 Post 3.134066196 -15 9.1 1
9 Ant 1.309024248 -25 9 1
9 Ant 3.369404446 -27 11.55 1
9 Ant 1.841284373 -26 13.15 1
9 Ant 3.675231524 -21 12.4 1
9 Post 3.06826061 -22 24.2 1
9 Post 3.59966626 -19 17.3 1
9 Post 4.466268907 -16.5 15.7 1
9 Post 1.882204503 -27 11.3 .
9 Ant 3.89896461 -25 21.1 1
9 Ant 2.295202494 -26 9 1
9 Ant 3.272687389 -24.5 4.95 0
9 Ant 4.201883396 -13 2.7 0
9 Post 4.374845784 -25 10.8 0
9 Post 4.459715564 -15.5 11.8 1
9 Post 3.227763303 -19.5 21.05 0
9 Post 2.517233031 -20.5 31.6 1
;
proc sort data=have; by position id; run;
proc sgscatter data=have;
where resistance gt -50; /* remove outlier */
by position;
matrix force time resistance / group=y;
run;
I would start by performing a logistic regression. (PROC LOGISTIC)
It is repeated measured model, therefore you need Mixed Model of Logistic.
@SteveDenham @lvm @StatDave could give you right sas code .
But you better to post it at Stat forum.
https://communities.sas.com/t5/Statistical-Procedures/bd-p/statistical_procedures
Build your skills. Make connections. Enjoy creative freedom. Maybe change the world. Registration is now open through August 30th. Visit the SAS Hackathon homepage.
Register today!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.
Select SAS Training centers are offering in-person courses. View upcoming courses for: