CalculateAmbiguityR returns the right-hand ambiguity of the triangular or trapezoidal fuzzy number (see, e.g., (Ban et al., 2015),
(Grzegorzewski and Romaniuk, 2022)).
CalculateAmbiguityR(fuzzyNumber, increases = FALSE)
Input data consist of triangular or trapezoidal fuzzy numbers.
The input data should consist of triangular or trapezoidal fuzzy numbers, given as a single vector or a whole matrix. In each row, there should be a single fuzzy number in one of the forms:
left end of the support, left end of the core, right end of the core, right end of the support, or
left increment of the support, left end of the core, right end of the core, right increment of the support.
In this second case, the parameter
increases=TRUE has to be used.
Then for each fuzzy number, its characteristics, known as the right-hand ambiguity of fuzzy number, is calculated. For the respective formulas, see, e.g., (Ban et al., 2015), (Grzegorzewski and Romaniuk, 2022).
This function returns vector of double values. Each output value is equal to the right-hand ambiguity of the respective fuzzy number.
Ban, A.I., Coroianu, L., Grzegorzewski, P. (2015) Fuzy Numbers: Approximations, Ranking and Applications Institute of Computer Sciences, Polish Academy of Sciences
Grzegorzewski, P., Romaniuk, M. (2022) Bootstrap methods for fuzzy data Uncertainty and Imprecision in Decision Making and Decision Support: New Advances, Challenges, and Perspectives, pp. 28-47 Springer
CalculateFuzziness for calculation of the fuzziness,
CalculateValue for calculation of the value,
CalculateAmbiguityL for calculation of the left-hand ambiguity,
CalculateAmbiguity for calculation of the ambiguity,
CalculateExpValue for calculation of the expected value,
CalculateWidth for calculation of the width
Other characteristics of fuzzy numbers functions:
# prepare some fuzzy numbers (first type of the initial sample) fuzzyValues <- matrix(c(0.25,0.5,1,1.25,0.75,1,1.5,2.2,-1,0,0,2), ncol = 4,byrow = TRUE) # calculate the right-hand ambiguity of the first fuzzy number CalculateAmbiguityR(fuzzyValues[1,]) # calculate the right-hand ambiguity for the whole matrix CalculateAmbiguityR(fuzzyValues) # prepare some fuzzy numbers (second type of the initial sample) fuzzyValuesInc <- matrix(c(0.25,0.5,1,0.25,0.25,1,1.5,0.7,1,0,0,2), ncol = 4,byrow = TRUE) # calculate the right-hand ambiguity of the first fuzzy number CalculateAmbiguityR(fuzzyValuesInc[1,], increases = TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.