gistfile1.clj

(defn bayes [a b-given-a b-given-c] 
  ; p(a|b ) = p(b | a) * p(b) / p(a) 
  (let [c (- 1.0 a)] ; Complementary event of a
        (/ (* b-given-a a) 
       (+ (* b-given-a a) (* b-given-c c)))))

(def plots (take 20 (iterate #(+ % 0.005) 0.005)))

(println "Accuracy of a drug test that is 99% sensitive, 99% specific")
(println "(eg. 99% true positive for users, 99% true negative for non-users)")
(print (map #(format "Accuracy if %.2f%% of test set are users: %.3f\n" 
               (* 100 %) 
               (bayes % 0.99 0.01)) plots))

gistfile2.txt

Ξ ~ → clojure bayesdrug.clj
Accuracy of a drug test that is 99% sensitive, 99% specific
(eg. 99% true positive for users, 99% true negative for non-users)
Accuracy if 0.50% of test set are users: 0.332
 Accuracy if 1.00% of test set are users: 0.500
 Accuracy if 1.50% of test set are users: 0.601
 Accuracy if 2.00% of test set are users: 0.669
 Accuracy if 2.50% of test set are users: 0.717
 Accuracy if 3.00% of test set are users: 0.754
 Accuracy if 3.50% of test set are users: 0.782
 Accuracy if 4.00% of test set are users: 0.805
 Accuracy if 4.50% of test set are users: 0.823
 Accuracy if 5.00% of test set are users: 0.839
 Accuracy if 5.50% of test set are users: 0.852
 Accuracy if 6.00% of test set are users: 0.863
 Accuracy if 6.50% of test set are users: 0.873
 Accuracy if 7.00% of test set are users: 0.882
 Accuracy if 7.50% of test set are users: 0.889
 Accuracy if 8.00% of test set are users: 0.896
 Accuracy if 8.50% of test set are users: 0.902
 Accuracy if 9.00% of test set are users: 0.907
 Accuracy if 9.50% of test set are users: 0.912
 Accuracy if 10.00% of test set are users: 0.917