MethodsComparison: Comparison of imputation methods for fuzzy values.

In FuzzyImputationTest: Imputation Procedures and Quality Tests for Fuzzy Data

Comparison of imputation methods for fuzzy values.

Description

'MethodsComparison' compares the quality of built-in imputation methods using various measures and goodness-of-fit statistical tests for the given fuzzy dataset.

Usage

MethodsComparison(
  trueData,
  iterations = 100,
  percentage = 0.05,
  trapezoidal = TRUE,
  verbose = TRUE,
  ...
)

Arguments

trueData	Name of the input matrix (or data frame) with the true values of the variables.
iterations	Number of the repetitions of each analyses (introducing NAs and then imputation of the missing values).
percentage	Desired percentage of missing values (NAs) in each row.
trapezoidal	Logical value depending on the type of fuzzy values (triangular or trapezoidal ones) in the dataset.
verbose	Logical value if the progress bar should be shown.
...	Additional parameters passed to other functions.

Details

The procedure uses the function ImputationTests to compare the quality of the imputation methods for the specified fuzzy dataset. To minimize random effects, each analysis is repeated iterations times with the new randomly generated NA values in the input dataset, and then new imputed values for all built-in methods. To generate the new NAs values, the function IntroducingNA is used. Next, the results, the same as forImputationTests (apart from trueValues and mask), are averaged and their standard errors calculated (see the column se).

The input dataset can be given as matrix or data frame.

To get overall comparison of the methods, summary(object,...) can be used for the output object from this method. The values diff are equal to the differences of p-values between the respective tests for the parts true and imputed there.

Value

The output is an S3 object of the class metComp given as a list of the matrices: nonFNNumbers - the vector with the numbers of non-FNs samples for each variable (with the overall mean), errorMatrix – the output from the function ErrorMatrix, statisticalMeasures – the output from the function StatisticalMeasures, statisticalTests – the output from the function ApplyStatisticalTests, fuzzyMeasures – the output from the function CalculateFuzzyMeasures.

See Also

ImputationTests for the single imputation benchmark, summary.metComp.

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)


# check the quality of the imputed values

## Not run: 

MethodsComparison(matrix1,iterations=10,trapezoidal=TRUE)


## End(Not run)

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