FuzzyImputation: Main method to impute fuzzy values.

In FuzzyImputationTest: Imputation Procedures and Quality Tests for Fuzzy Data

Main method to impute fuzzy values.

Description

'FuzzyImputation' imputes (i.e., replaces missing values) fuzzy numbers using various methods.

Usage

FuzzyImputation(
  dataToImpute,
  method = "dimp",
  trapezoidal = TRUE,
  checkFuzzy = FALSE,
  verbose = TRUE,
  pmmWarnings = TRUE,
  ...
)

Arguments

dataToImpute	Name of the input matrix (data frame or list) of fuzzy numbers with some NAs.
method	Name of the imputation method (possible values: dimp,missForest,miceRanger,knn,pmm).
trapezoidal	Logical value depending on the type of fuzzy values (triangular or trapezoidal ones) in the dataset.
checkFuzzy	If TRUE is set, after each imputation, the output values are checked if they are proper fuzzy numbers. If there are some improper fuzzy numbers, they are removed, and the imputation procedure is repeated.
verbose	If TRUE is set, the current simulation number is printed.
pmmWarnings	Suppress warnings from pmm method.
...	Additional parameters that are passed to the imputation procedure.

Details

The procedure randomly imputes missing values (NAs) with suitable data in the case of a data frame (or a matrix, or a list) consisting of fuzzy numbers (triangular fuzzy numbers if trapezoidal=FALSE is set, or trapezoidal ones if the default trapezoidal=TRUE is used). The output is given as a matrix without NAs, where each row is related to fuzzy numbers (given by 3 values for the triangular fuzzy numbers, or 4 values in the case of trapezoidal ones) for the consecutive variables. Many fuzzy variables (not only the single one) can be used. The input has to consist of fuzzy numbers of the same type (i.e., mixing triangular and trapezoidal fuzzy numbers is not allowed).

Various possible imputation methods can be used when the parameter method is specified – both the general ones (missForest or miceRanger from the respective packages, or knn from VIM package, or pmm from mice package) and a more specific ones, tailored for the fuzzy data (dimp in the case of the DIMP method). Please note that due to the imputation, some output values can be improper fuzzy variables (e.g., a core of a fuzzy number can have greater value than its right end of the support). To avoid this, checkFuzzy=TRUE should be set. In this case, the imputation procedure is repeated until all of the results are proper triangular or trapezoidal fuzzy numbers. The improper values are removed and replaced with the respective fuzzy numbers from the input dataset. However, many repetitions (even unacceptably many) are then possible.

Value

The output is given as a matrix.

Examples

# seed PRNG

set.seed(1234)

# load the necessary library

library(FuzzySimRes)

# generate sample of trapezoidal fuzzy numbers with FuzzySimRes library

list1<-SimulateSample(20,originalPD="rnorm",parOriginalPD=list(mean=0,sd=1),
incrCorePD="rexp", parIncrCorePD=list(rate=2),
suppLeftPD="runif",parSuppLeftPD=list(min=0,max=0.6),
suppRightPD="runif", parSuppRightPD=list(min=0,max=0.6),
type="trapezoidal")

# convert fuzzy data into a matrix

matrix1 <- FuzzyNumbersToMatrix(list1$value)

# check starting values

head(matrix1)

# add some NAs to the matrix

matrix1NA <- IntroducingNA(matrix1,percentage = 0.1)

head(matrix1NA)

# impute missing values with the DIMP method

set.seed(12345)

FuzzyImputation(matrix1NA)

# impute missing values with the miceRanger method

set.seed(12345)

FuzzyImputation(matrix1NA,method = "miceRanger")

FuzzyImputationTest documentation built on April 3, 2025, 9:23 p.m.