StatisticalMeasures {FuzzyImputationTest} | R Documentation |
Calculation of statistical measures for errors of the imputed data.
Description
StatisticalMeasures
calculates various statistical measures between the real and imputed data.
Usage
StatisticalMeasures(trueData, imputedData, imputedMask, ...)
Arguments
trueData |
Name of the input matrix (or data frame) with the true values of the variables. |
imputedData |
Name of the input matrix (or data frame) with the imputed values. |
imputedMask |
Matrix (or data frame) with logical values where |
... |
Additional parameters passed to other functions. |
Details
The procedure calculates different statistical measures between the real and imputed data for each column, namely:
TrueMean - the mean only for the real but missing data,
ImpMean - the mean only for the imputed values,
TrueSD - the standard deviation only for the real but missing data,
ImpSD - the standard deviation only for the imputed values,
GenMean - the mean for the all real data (given by
trueData
),GenImpMean - the mean for real data with the respectively imputed values (given by
imputedData
),GenSD - the standard deviation for the all real data (given by
trueData
),GenImpSD - the standard deviation for real data with the respectively imputed values (given by
imputedData
),AbsDiffTrueImpMean - the absolute difference between TrueMean and ImpMean,
AbsDiffTrueImpSD - the absolute difference between TrueSD and ImSD,
AbsDiffGenImpMean - the absolute difference between GenMean and GenImpMean,
AbsDiffGenImpSD - the absolute difference between GenSD and GenImpSD.
To properly distinguish the real values with their imputed counterparts, the additional matrix imputedMask
should be provided.
In this matrix, the logical value TRUE
points out the cells with the imputed values.
Otherwise, FALSE
should be used. These input datasets should be given as matrices
or data frames.
Value
The output is given as a matrix with columns related to all columns of the input dataset plus the overall mean.
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
matrix1DImp <- ImputationDimp(matrix1NA)
# find cells with NAs
matrix1Mask <- is.na(matrix1NA)
# calculate errors for the imputed values
StatisticalMeasures(matrix1,matrix1DImp,matrix1Mask)