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**Additional resources for Accuracy of MSI testing in predicting germline mutations of MSH2 and MLH1 a case study in Bayesian m**

**Example text**

B). Is it possible to calculatep(c) for S2 trials only? What would this statistic measure? 75). 9). 7. 8. 9. 10. 11. Chapter 1 (a). 8 and H and F are unknown, estimate d'. (b). 05, find H and d'. (a). Suppose d' = 1. 5? (b) Plot the ROC from these points on linear and z coordinates, and use the zROC to confirm the value of d'. 2, find d' from H and F and also fromp(c). Is there a pattern to the results? 9) and (. , do they imply the same value of d')! 6). If F is unchanged, what would H have to be to double the participant's sensitivity, according to detection theory?

Lower curve shows distribution due to Old (52) items; values above the criterion k lead to hits, those below to misses. The means of the distributions are Af, and M2. ) Response Selection in the Decision Space The optimal rule (see Green & Swets, 1966, ch. 1) is to establish a criterion that divides the familiarity dimension into two parts. Above the criterion, labeled k in Fig. 5, the participant responds "yes" (the face is familiar enough to be Old); below the criterion, a "no" is called for.

Because both H and F range from 0 to 1, the ROC space, the region in which ROCs must lie, is the unit square. For every value of the false-alarm rate, the plot shows the hit rate that would be obtained to yield a particular sensitivity level. 5 for H; different curves represent different values of d'. When performance is at chance (d! = 0), the ROC is the major diagonal, where the hit and false-alarm rates are equal. For this reason, the major diagonal is sometimes called the chance line. As sensitivity increases, the curves shift toward the upper left corner, where accuracy is perfect (F = 0 and H= 1).