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Sheridan County School District #2 > Refined Index Of Agreement

Refined Index Of Agreement

The spatial representation of the temporal correspondence between the time series from two Earth observation satellites, according to different compliance metrics described in the text, calculated and illustrated by statistical software r. (version 3.2.1, www.R-project.org/). The scope of the index remains a pragmatic extension of r and is therefore used in a context where a linear operating agreement is desired. It is not designed as a tool to study new functional associations in data (for example. B the maximum information coefficient32). However, its use could go beyond the symmetrical comparison of the dataset agreement and combine the list of existing methods2 to characterize the model`s performance against a reference. The index has also been demonstrated here by timed data case studies, but it should also be usable for each pair of vectors of each type of data, as well as r. Willmott et al. (2011) proposed a new index, d,and compared dr to “mean absolute error (MAE) ” recordings that vary logically with MAE. However, this should be compared to an average absolute relative error, as MAE may vary with different samples/data sets, while the “average absolute relative error” value may be the same (i.e., there is no change in the relative model).

In this study, the dr index does not follow the logical trend within a given data set, as in Table 2 (combined analysis); and also ambiguously between different sets (1st year and data combined) – with a PMARE value. Similar inconsistencies are also observed for random records (Table 4, 1 . . . 3. Recordings – with PMARE). Another common approach is to take into account that a statistical model can be adapted to the data. In this case, a measure of match can be inferred from the determination coefficient, which indicates how well the data corresponds to the chosen model. For linear models, the determination coefficient corresponds to the r square and varies from 0 to 1.

Another interesting feature is that this number represents the share of variance explained by the model. One of the drawbacks of R and R2 is that they only measure the strength of the relationship between the data, but they do not give any indication as to the size of the data sets. Watterson8 proposed to create an index to assess the performance of the climate model by applying a transformation of Arcsine to the Mielke index: the results are presented 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.