slope across variables in each row

[timeless]

How do I calculate the slope of few variables for each row assuming delta X between variables is =1

Example:

ID	v1	v2	v3	v4	v5	slope
a	2	4	6	8	10	?
b	60	70	95	106	120	?
c
d
e

Thanks

SAS 6.1

[ballardw]

Do you mean by "delta x between variables" that you have the equivalent of coordinates such as (1,2), (2,4), (3,6), (4,8) and (5,10)?

And do you want the slope of a best fit line through the points? The first line has a constant slop of 2 between any two pairs but the second row has different slopes between different pairs so a request for a single slope needs some clarification.

If the slope of a "best fit" line is desired I would transform the data into actual x,y coordinates and then run the result through proc reg with the Id as a by variable to get the result for each group:

data have;
   input ID $ v1 v2 v3 v4 v5 ;
datalines;
a 2 4 6 8 10  
b 60 70 95 106 120  
;
run;

data trans;
   set have;
   array v v1-v5;
   do x= 1 to 5;
      y= v[x];
      output;
   end;
   keep id x y;
run;
proc reg data=trans;
   by id;
   model y=x;
run;

The Parameter estimate for X is the slope you may want.

If you want to merge that back onto the original research the options for Prog Reg on output to get a data set  and then merge by ID.

[Reeza]

This assumes a linear trend, with your X's being 1-5 in your case and does not deal with missing values.

In this case there are 6 obs but changing it to 5 is a trivial task.

Overall, I would recommend a transpose and proc reg instead as well.

data test;
input x1 x2 x3 x4 x5 x6;
datalines;
-0.069965723 0.492749371 0.955245597 1.346963522 1.701118239 2.523141195
;
run;

data slope;
set test;
array ys(6) x1-x6;
array vals(6) (1 2 3 4 5 6);
xbar = mean(of vals(*));
ybar = mean(of ys(*));



do i=1 to dim(vals);


num=sum(num, (vals(i)-xbar)*(ys(i)-ybar));
den=sum(den, (vals(i)-xbar)**2);


end;

slope = num/den;
run;