The weighted Kappa can be then calculated by plugging these weighted Po and Pe in the following formula: The proportion of expected chance agreement (Pe) is the sum of the weighted product of rows and columns marginal proportions. The proportion of observed agreement (Po) is the sum of weighted proportions. The type of commonly used weighting schemes are explained in the next sections. The weights range from 0 to 1, with weight = 1 assigned to all diagonal cells (corresponding to where both raters agree) (Friendly, Meyer, and Zeileis 2015). To compute a weighted kappa, weights are assigned to each cell in the contingency table. The proportion in each cell is obtained by dividing the count in the cell by total N cases (sum of the all the table counts). For each table cell, the proportion can be calculated as the cell count divided by N.Similarly, for a given column i, the marginal proportion is P+i = n+i/N.
For a row i, the marginal proportion is Pi+ = ni+/N. This is also known as the marginal frequencies or probabilities. For a give row/column, the marginal proportion is the row/column margin divide by N.Here, the total sum of a given column i is named n+i , n+k) indicates the sum of each column, known as column margins. Here, the total sum of a given row i is named ni+. , nk+) indicates the sum of each row, known as row margins or marginal counts. These counts are indicated by the notation n11, n12. The table cells contain the counts of cross-classified categories. Let’s consider the following k×k contingency table summarizing the ratings scores from two raters. This is only suitable in the situation where you have ordinal or ranked variables. In other words, the weighted kappa allows the use of weighting schemes to take into account the closeness of agreement between categories. This chapter describes the weighted kappa, a variant of the Cohen’s Kappa, that allows partial agreement (J. For ordinal rating scale it may preferable to give different weights to the disagreements depending on the magnitude. This is most appropriate when you have nominal variables. It takes no account of the degree of disagreement, all disagreements are treated equally. The Classical Cohen’s Kappa only counts strict agreement, where the same category is assigned by both raters (Friendly, Meyer, and Zeileis 2015). In a previous chapter (Chapter we described the classical Cohen’s Kappa, which is a popular measure of inter-rater reliability or inter-rater agreement. High agreement would indicate consensus in the diagnosis and interchangeability of the observers (Warrens 2013). The analysis of the agreement between the two observers can be used to measure the reliability of the rating system.
An example is two clinicians that classify the extent of disease in patients. Its simple user interfaceĪnd workflow allows for fast training and self-learning for occasional users.In biomedical, behavioral research and many other fields, it is frequently required that a group of participants is rated or classified into categories by two observers (or raters, methods, etc). Saphir is the industry standard PTA software, used by nearly all major IOC’s, NOC’s, Independents and Service Companies. The development of deconvolution allows us to combine several of these shut-ins in time to provide information much deeper into the reservoir than would normally be feasible by a shut-in alone. Specific operations require specific processing, such as multirate gas tests, interference tests, multilayer tests, etc.
The diagnostic plot of choice is the loglog plot where the pressure and the Bourdet derivative allow the identification of reservoir geometry and properties, these are then matched with models. The pressure response from these shut-ins can be used to provide information about the reservoir within the radius of investigation for radial flow or the area of investigation for more complex geometries. The data are captured during dedicated well test operations such as DSTs or production tests or during routine operational shut-ins by Permanent Downhole Gauges (PDG). Pressure Transient Analysis, or PTA, is, in most cases, about analyzing high frequency, high resolution shut-in data.
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