The proportion of J-specific agreements is equal to the total number of class j agreements divided by the total number of J or S (j) ps (d) ——–. (12) Sposs(j) Before proceeding to the general case, it will examine the simpler situation of estimating specific positive agreements for several binary ratings. One thinks, for example, of an epidemiological application in which a positive assessment of a positive diagnosis for a very rare disease corresponds — one, for example, with a prevalence of 1 in 1,000,000. Here, we may not be very impressed when Po is very high — even above .99. This result is almost exclusively due to an agreement on the absence of disease; We are not informed directly if the diagnosticians agree on the occurrence of diseases. For a given case with two or more binary ratings (positive/negative), you can indicate n and m the number of ratings or the number of positive ratings. In this particular case, there are chords in pairs of pairs of positive notes and x -m (n – 1) possibilities for such an agreement. If we calculate x and y for each case and add the two terms in all cases, then the sum of x is divided by the sum of y the share of the specific positive match in the whole sample. In addition, Cohens`s (1960) criticism of in: that it can also be high among hypothetical advisors who guess in all cases probabilities corresponding to the base interest rates observed. In this example, if both advisors simply „positively“ guess the vast majority of the time, they would generally agree on the diagnosis.
Cohen proposed to correct this by comparing in in to a corresponding quantity, pc, the share of the agreement expected by advisors who guess at random. As described in the kappa coefficients page, this logic is debatable; in particular, it is not clear what advantage there is in comparing a real degree of agreement, in, with a hypothetical value, pc, which would occur according to a patently unrealistic model. The number of possible agreements specifically for category j for case k is equal to njk (nk – 1) (10) Eq. (6) Is like collapsing Table C × C in Table 2×2 compared to Category i, which is considered a „positive“ rating and then Eq`s Positive Agreement Index (PA). (2) to calculate. This is done one after the other for each category i. In any reduced table, you can perform a statistical independence test with Cohen`s Kappa, quota ratio or chi-square, or use a precise Fisher test. if a „true positive“ is the event that the test makes a positive prediction, and that the subject has a positive result under the gold standard, and a „false positive“ is the event that the test makes a positive prediction, and that the subject has a negative result under the gold standard. The ideal value of the APP, with a perfect test, is 1 (100%), and the worst possible value would be zero. Positive and negative forecast values (APP) are the proportions of positive and negative results in statistics and diagnostic tests, which are actual positive or negative results.  PpV and NPV describe the performance of a diagnostic test or other statistical indicator.
A high result can be interpreted as indicating the accuracy of such a statistic.