
POSITIVE PREDICTIVE VALUEName:
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 positive predictive value is then N_{11}/(N_{11}+N_{21}). This is the conditional probability of variable 1 being true given that variable 2 is true. In the context of the first example above, this is the probability that the disease is present when there is a positive test result. Fleiss and his coauthors recommend positive predictive value and negative predictive value as an alternative to false positive and false negative due to the fact that the definitions of false positive and false negative have been inconsistent in the literature.
<SUBSET/EXCEPT/FOR qualification> where <y1> is the first response variable; <y2> is the second response variable; <par> is a parameter where the computed positive predictive value is stored; and where the <SUBSET/EXCEPT/FOR qualification> is optional.
LET A = POSITIVE PREDICTIVE VALUE Y1 Y2 SUBSET TAG > 2
CROSS TABULATE FALSE POSITIVE Y1 Y2 X1 X2 POSITIVE PREDICTIVE VALUE PLOT Y1 Y2 X CROSS TABULATE POSITIVE PREDICTIVE VALUE PLOT Y1 Y2 X1 X2 BOOTSTRAP POSITIVE PREDICTIVE VALUE PLOT Y1 Y2 JACKNIFE POSITIVE PREDICTIVE VALUE PLOT Y1 Y2
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 = positive predictive value y1 y2 subset x = 1 tabulate positive predictive value 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 Positive Predictive Value x1label Group ID character x blank line blank solid . positive predictive value plot y1 y2 x
 
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Date created: 06/06/2007 