FuzzyImputationTest (version 0.5.0)

FuzzifyMatrix: Fuzzyfing the crisp values.

Description

`FuzzifyMatrix` converts real-valued variables into fuzzy numbers.

Usage

FuzzifyMatrix(
  crispMatrix,
  coreFactor = 0.2,
  supportFactor = 0.2,
  trapezoidal = TRUE,
  varNames = colnames(crispMatrix),
  ...
)

Value

The output is given as a matrix with three (in the case of triangular fuzzy numbers) or four (for trapezoidal fuzzy numbers) columns for each input variable.

Arguments

crispMatrix

Name of the input matrix (or data frame) with real-valued variables to fuzzify.

coreFactor

Value used as the multiplier for the left/right end of the interval of the uniform distribution applied to randomly generated increments of the core.

supportFactor

Value used as the multiplier for the left/right end of the interval of the uniform distribution applied to randomly generated increments of the support.

trapezoidal

Logical value that indicates if trapezoidal (or triangular, otherwise) fuzzy numbers should be generated.

varNames

Names of the input variables.

...

Additional parameters passed to other functions.

Details

The procedure generates trapezoidal fuzzy numbers (when the default trapezoidal=TRUE is set) or triangular ones (for trapezoidal=FALSE) based on the real-valued data from the given matrix or the data frame. To do this, for each variable the standard deviation is calculated. Then, the left and right increments of the core (in the case of trapezoidal fuzzy numbers) are randomly generated using the original value plus/minus two random values from the uniform distribution on the interval [0,coreFactor*(standard deviation)]. In the case of triangular fuzzy numbers, the cores are equal to the original real values. In the same manner, the left and right increments of the support are randomly generated with two random values from the uniform distribution on the interval [0,supportFactor*(standard deviation)].

Examples

# set seed for the random generator

set.seed(12345)

# let's look at the beginning of the iris dataset (four numeric variables)

head(iris[,1:4])

# and fuzzify these variables

fuzzyOutput <- FuzzifyMatrix(iris[,1:4])

head(fuzzyOutput)