Agreement Indices

With the exception of the notion of power, it is a pseudo-bayes (see z.B. Chapter 9 of Gilks et al.1996 and the indications of agreement ai are factors of this notion. The Bayes factor is used here to compare the restricted model to the totally unlimited model. The term “power” offers only the comfort of an appropriate stop of reception, regardless of the total number of terms (see below). In this case, the denominator μ is added together by adding up the differences of all X and Y points compared to the average of X. The original version was based on gridded deviations, but was later modified 15 using absolute deviations, arguing that MAD (or MAE in this case, because it refers to errors between forecasts and observations instead of deviation) is a more natural measure of average errors and less ambiguous than RMSD (or RMSE)12. Another refinement of the index16 was intended to remove the predictions from the denominator, but as others have argued14, this amounts to resizing the expression of the coefficient of effectiveness, while the interesting reference point is lost. Again, these indices do not meet the requirement for symmetry. A much simpler way to solve this problem is described below. Positive agreement and negative agreement We can also calculate the agreement observed separately for each rating category.

The resulting indices are generically referred to as the shares of specific agreements (Ciccetti – Feinstein, 1990; Spitzer – Fleiss, 1974). With regard to binary ratings, there are two such indices, a positive agreement (PA) and a negative agreement (NA). They are calculated as follows: 2a 2d PA – ———-; NA – ———-. (2) 2a – b – c 2d – b – pa, z.B. estimates the probability conditional, because one of the randomly selected advisors gives a positive assessment, the other advisor also does. The results are shown in the maps in Figure 5. All maps show the expected patterns of concordance over time: areas where the NDVI signal is highly dynamic, such as the northern production areas, are more consistent than desert areas, where the signal is mainly audible. However, there is a big difference where each metric provides negative values: the map does not display negative values, the Watterson M metric map only takes negative values when the correlation is negative, but the map of the Ji-Gallo AC index shows huge areas of negative values throughout the territory. The comparison between b and r reflects the added value of using the former, which includes distortions that do not exist in the latter. The extent of these distortions in relation to global deviations can be interconnected in the map, while datasets compliance is displayed in the map regardless of these distortions. Cohens Kappa measures the agreement between two advisors who classify each of the N elements into exclusion categories C. The definition of “textstyle” is as follows: the total number of chords especially at the J level is K S (j) – SUM njk (njk – 1) in all cases.