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TAD (Time Average Difference) is area under the curve (AUC).
Using Trapezoidal Rule for the Area Under a Curve Calculation
Estimate the Area Under a ROC Curve
SAS Calculations of AUC for Multiple Metabolic Readings
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AREA.SAS
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PURPOSE: This program uses PROC EXPAND to calculate the approximate area under the curve for some sample data. The sample data should consist of (x,y) pairs.
DETAILS: For this example, the sample data is generated from a high degree polynomial. PROC EXPAND is then used to compute the approximate area under the curve using each of the following methods:
a. Cubic Spline interpolation. b. Trapezoid rule.
The exact area, given by the definite integral, is calculated for the polynomial curve in order to assess the precision of the approximations.
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%let lower=-2; %let upper=1; %let interval=0.2;
* generate some data according to a high order polynomial; data kvm; do x=&lower to &upper by &interval; y=15+(x-2)*(x-1.5)*(x-1)*(x-.5)*x*(x+.5)*(x+1)*(x+1.5)*(x+2); output; end;
proc sort; by x;
/* PROC EXPAND will include a contribution for the last interval. For an accurate approximation to the integral, we need to make sure that this last contribution is negligible. So we’ll append an additional x value which is extremely close to the last x value. Of course, the two Y values will be identical. But the result is that the last interval is extremely short, so any contribution to the integral approximation is negligible. */
proc print data=kvm(obs=50); title ‘First few observations of the original data’; run;
data one; set kvm end=eof; output; if eof then do; x=x+(1e-10); output; end; run;
proc print data=one(obs=50); title ‘First few observations of the original data’; run;
proc gplot data=one; title ‘original series’; plot y*x; run;
proc expand data=one out=three method=spline ; convert y=total/observed=(beginning,total) transformout=(sum); id x; run;
proc sort data=three; by descending total;
proc print data=three(obs=1); var total; title ‘Approximate Integral Using Spline method’; run;
*************************************************;
proc expand data=one out=three method=join; convert y=total/observed=(beginning,total) transformout=(sum); id x; run;
proc sort data=three; by descending total;
proc print data=three(obs=1); title ‘Approximate Integral Using Trapezoid Rule’; var total; run;
***********************************************;
/* Since this data was generate using a high order polynomial, it’s easy to compute the definite integral value. Then we can compare our approximations and assess their precision. */
data four; set one; x3= x*x*x; x5= x*x*x*x*x; x7= x*x*x*x*x*x*x; x9= x*x*x*x*x*x*x*x*x; run;
proc autoreg data=four outest=five noprint; title ‘Calculate polynomial coefficients’; model y=x x3 x5 x7 x9; run;
proc print data=five; run;
data six; set five; title ‘brute force (hand) integration of polynomial’; val=&upper; eval=intercept*val+x*(val**2)/2+x3/4*(val**4)+x5/6*(val**6) +x7/8*(val**8)+x9/10*(val**10); temp=eval; val=&lower; eval=intercept*val+x*(val**2)/2+x3/4*(val**4)+x5/6*(val**6) +x7/8*(val**8)+x9/10*(val**10); integral=temp-eval; keep integral; run;
proc print; run; title;
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