@Allen799    Did you place the DOT (.) after the numeric like (comma14. and Dollar14.)? ... View more

@Shmuel  has given the way to read the data.   Your next procedure may require more memory to plot your requirement. Hope you have that memory. Perhaps you may ask for some summary measures. ... View more

@pinkyloop Welcome to Community SAS.   SAS Data step needs several IF - THEN conditions. You can't place all together. Here is another way for the beginner. data grade; input gpa; datalines; 0.3 3.5 4.0 2.5 1.5 3.2 ; run; data aa; set grade; /*lettergrade=(A=(gpa>3.5) B=(gpa 2.5>=3.5) C=(gpa 1.5>=2.5) D=(gpa 0.5>=1.5) F=(gpa<0.5)); */ if (gpa>3.5) then lettergrade='A'; else if (gpa>2.5) then lettergrade='B'; else if (gpa>1.5) then lettergrade='C'; else if (gpa>0.5) then lettergrade='D'; else lettergrade='F'; run; Another way is to use SELECT-WHEN switch statement as:   data bb; set grade; select; when (gpa > 3.5) Lettergrade='A'; when (gpa > 2.5) Lettergrade='B'; when (gpa > 1.5) Lettergrade='C'; when (gpa > 0.5) Lettergrade='D'; otherwise Lettergrade='F'; end; run; All the best to learn SAS programming ... View more

Show your Log.   I am getting the output as:   The FREQ Procedure Frequency Percent Row Pct Col Pct Table of carotid1 by carotid2 carotid1 carotid2 Non_Occuled Occuled Total Non_Occuled 67 67.00 87.01 89.33 10 10.00 12.99 40.00 77 77.00 Occuled 8 8.00 34.78 10.67 15 15.00 65.22 60.00 23 23.00 Total 75 75.00 25 25.00 100 100.00 Statistics for Table of carotid1 by carotid2 McNemar's Test Chi-Square DF Pr > ChiSq Exact Pr >= ChiSq 0.2222 1 0.6374 0.8145 Simple Kappa Coefficient Estimate Standard Error 95% Confidence Limits 0.5068 0.1008 0.3092 0.7045 Sample Size = 100 ... View more

It is a 2 by 2 table . Use Proc Freq to get the estimate of Kappa Coefficient.   data have; input carotid1 :$11. carotid2 :$11. count; datalines; Occuled Occuled 15 Occuled Non_Occuled 8 Non_Occuled Occuled 10 Non_Occuled Non_Occuled 67 ; run; proc freq data=have; table Carotid1*carotid2; exact mcnem; weight count; run; ... View more

@stevo642    The ranks given to second row seems to be wrong. I think it has to be 3 1 2. I have less expertise in IML. Do you accept a Data Step solution? The steps include the sorting of each row by using SORTN() function and at the same time switching the index of the array. Then re-ordered index will be the rank. SMALLEST() takes care of the sorting in the background and returns the i-th smallest value. Using WHICHN() with the i-th smallest value gives the desired Rank.   Note, no attempt is made to check for ties.   %let vn = 3; data rank; set A; array x x1-x&vn; array R R1 - R&vn; do i = 1 to dim(x); R[i] = whichN(smallest(i, of x[*]), of x[*]); put R[i] =; end; keep R:; run; ... View more

@PaigeMiller    I respect your views. But I differ from you.   "Especially for beginners, PROC MEANS is much easier (and it is a much more tested and therefore less risky) than any data step code to calculate the mean. "   I believe that the OP will choose a solution that is convenient to him. He might also be interested to learn other solutions. Besides, there are several others who wish to to learn Data Step Programming in the Community. I further believe that adding and dividing is not more risky for SAS Programmers.   Kind regards DATASP ... View more

@PaigeMiller    The data step program does not change.  The sampsize is computed using CEIL() function. The OP may change to FLOOR() or ROUND().    Using the two missing values for F and R using the OP's Data Set, the new ouput is:   bs _n Company sampsize sum mean_Commision 1 20 F 4 56.50 14.1250 2 15 I 3 47.70 15.9000 3 21 M 5 74.70 14.9400 4 12 R 3 46.80 15.6000 5 16 RO 4 66.29 16.5725 6 12 U 3 40.90 13.6333 7 3 W 1 15.00 15.0000 ... View more

@d0816    You have two answers. Just for fun I give another One Step Data Step. Your Data is presorted as you have claimed. Assuming your Data Set is named as 'HAVE' here comes: data want; do _n = 1 by 1 until(last.Company); set have; by Company; end; sampsize = ceil(_n * 0.20); sum = 0; do _n = 1 by 1 until(last.Company); set have; by Company; if _n <= sampsize then sum + Commission; if last.Company then mean_Commision = sum / sampsize; end; drop Commission; run; I have not dropped the intermediate variables used in the program for the sake giving you additional information. Here is the output:   obs _n Company sampsize sum mean_Commision 1 15 I 3 47.70 15.9000 2 19 F 4 56.50 14.1250 3 21 M 5 74.70 14.9400 4 11 R 3 46.80 15.6000 5 16 RO 4 66.29 16.5725 6 12 U 3 40.90 13.6333 7 3 W 1 15.00 15.0000 ... View more

@Haydn    Do you want 1 for each distinct ID? Then how to count the number of distinct values? I show a simple example and see whether it helps you.   data have; input id x; datalines; 10 23 10 12 10 11 20 30 20 40 30 12 ; run; proc sort data = have; by id; run; data want; set have; by id; if first.id then uniqC + 1; put uniqC =; run; ... View more