
TEST SENSITIVITYName:
The parameters N_{11}, N_{12}, N_{21}, and N_{22} denote the counts for each category. Success and failure can denote any binary response. Dataplot expects "success" to be coded as "1" and "failure" to be coded as "0". Some typical examples would be:
In these examples, the "ground truth" is typically given as variable 1 while some estimator of the ground truth is given as variable 2. The test sensitivity is then N_{11}/(N_{11}+N_{12}). That is, the test sensitivity is the conditional probability that variable 2 is a success given that variable 1 is a success. In the context of the first example above, the test sensitivity is the probability that the test detects the disease given that the disease is present.
<SUBSET/EXCEPT/FOR qualification> where <y1> is the first response variable; <y2> is the second response variable; <par> is a parameter where the computed test sensitivity is stored; and where the <SUBSET/EXCEPT/FOR qualification> is optional.
LET A = TEST SENSITIVITY Y1 Y2 SUBSET TAG > 2
let n = 1 . let p = 0.2 let y1 = binomial rand numb for i = 1 1 100 let p = 0.1 let y2 = binomial rand numb for i = 1 1 100 . let p = 0.4 let y1 = binomial rand numb for i = 101 1 200 let p = 0.08 let y2 = binomial rand numb for i = 101 1 200 . let p = 0.15 let y1 = binomial rand numb for i = 201 1 300 let p = 0.18 let y2 = binomial rand numb for i = 201 1 300 . let p = 0.6 let y1 = binomial rand numb for i = 301 1 400 let p = 0.45 let y2 = binomial rand numb for i = 301 1 400 . let p = 0.3 let y1 = binomial rand numb for i = 401 1 500 let p = 0.1 let y2 = binomial rand numb for i = 401 1 500 . let x = sequence 1 100 1 5 . let a = test sensitivity y1 y2 subset x = 1 tabulate test sensitivity y1 y2 x . label case asis xlimits 1 5 major xtic mark number 5 minor xtic mark number 0 xtic mark offset 0.5 0.5 ytic mark offset 0.05 0.05 y1label Test Sensitivity x1label Group ID character x blank line blank solid . test sensitivity plot y1 y2 x
 
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Date created: 04/13/2007 