Hi @lei2004 and welcome to the SAS Support Communities!
I agree that the formula for R1² in the article is a bit confusing, but (without having access to the original source by Kvålseth) I think you just need to insert the yi, their mean and the back-transformed predicted values (EDIT: that is: exp("(log yi) hat")).
Taking the first of the two numeric examples from section 3 of the article:
data have;
input x y;
log_y=log(y);
cards;
0 .5
1 4
2 6
3 7
16 12
20 22
;
proc summary data=have;
var y;
output out=stats css=css;
run;
proc reg data=have;
model log_y=x;
output out=pred p=log_y_hat;
quit;
data want(keep=sse css rsq);
if _n_=1 then set stats;
set pred end=last;
sse+(y-exp(log_y_hat))**2;
if last;
rsq=1-sse/css;
run;
Result:
css sse rsq
287.208 34.9594 0.87828
(matching the authors' result 0.88)
For the second example I got rsq=-0.31642, again matching the (corrected) value in the article (up to a minor rounding issue).
Equivalently, you could obtain CSS from PROC REG (see ODS table ANOVA for model y=x, which could be included in the existing PROC REG step as model log_y y=x) instead of PROC SUMMARY.
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