Nothing
tests/testthat/_snaps/ImputationTests.md
In FuzzyImputationTest: Imputation Procedures and Quality Tests for Fuzzy Data
Function returns correct values
Code
set.seed(123456)
ImputationTests(trueData = testMatrix1, imputedData = testMatrix1Res,
imputedMask = testMatrix1Mask, cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
$trueValues
[,1] [,2] [,3] [,4]
[1,] 0.1199240 0.188146026 0.31481645 0.454346750
[2,] -0.4088952 -0.009244923 0.44777067 1.001830760
[3,] 0.4277991 0.603188626 1.92344212 2.062777662
[4,] -2.2328662 -2.042898771 0.35867035 0.907465245
[5,] -2.5630808 -2.064273777 -1.23140885 -0.927024725
[6,] 0.2323925 0.341050237 0.89701427 1.084108858
[7,] -0.9907339 -0.617642400 -0.06840412 0.117407837
[8,] -0.4229014 0.007461673 0.41849777 0.937398067
[9,] -1.3006321 -1.275517766 -0.46704551 -0.436057542
[10,] -3.6169809 -3.278239026 -1.14842928 -0.917869269
[11,] -0.5573558 -0.515324252 -0.42162779 -0.303922994
[12,] -1.6530331 -1.113884835 -0.22295531 -0.142508395
[13,] -1.7779261 -1.698957584 0.58819769 0.609471724
[14,] -1.6492244 -1.310178700 -0.08413849 0.008898696
[15,] -0.4535884 -0.375611086 1.18356358 1.753746539
[16,] -0.8892359 -0.816145242 -0.47619296 -0.029626635
[17,] 1.5656538 2.115238384 3.00592957 3.082389607
[18,] 0.9068369 1.160665144 1.54206699 1.736698607
[19,] -0.8969491 -0.608124123 -0.30055712 0.287914788
[20,] -1.6964713 -1.350383708 -0.07207102 0.289067511
$mask
[,1] [,2] [,3] [,4]
[1,] FALSE FALSE FALSE FALSE
[2,] FALSE TRUE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE
[6,] FALSE TRUE TRUE FALSE
[7,] FALSE FALSE FALSE TRUE
[8,] TRUE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE TRUE
[10,] FALSE FALSE FALSE FALSE
[11,] FALSE TRUE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE
[13,] TRUE FALSE FALSE FALSE
[14,] TRUE FALSE TRUE FALSE
[15,] FALSE FALSE TRUE FALSE
[16,] FALSE TRUE FALSE FALSE
[17,] TRUE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE
[19,] FALSE FALSE TRUE TRUE
[20,] FALSE FALSE FALSE TRUE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
X1 X2 X3 X4 mean
MAE 0.15816363 0.18044286 0.3764461 0.10809568 0.20578707
WMAE 0.25232132 9.12089408 0.7923513 0.49790500 2.66586793
MSE 0.04968434 0.04419471 0.1953087 0.01864308 0.07695769
WMSE 0.18867129 319.26814069 0.8957060 0.40012277 80.18816020
NRMSE 0.09791167 0.09052128 0.2146912 0.06818963 0.11782844
$statisticalMeasures
X1 X2 X3 X4 mean
TrueMean -0.571099525 -0.24991605 0.42397056 0.064583148 -0.08311547
ImpMean -0.542822592 -0.08596016 0.32411334 -0.003713996 -0.07709585
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.068297145 0.09009680
GenMean -0.892863415 -0.63303381 0.30935695 0.578825655 -0.15942865
GenImpMean -0.887208030 -0.60024263 0.28938551 0.565166226 -0.15822473
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.013659429 0.01801936
TrueSD 1.549888285 0.51582321 0.72660560 0.343366567 0.78392091
ImpSD 1.651290877 0.56980539 0.29546576 0.290234559 0.70169914
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.053132008 0.15991415
GenSD 1.232048554 1.22736828 1.02518086 1.020631251 1.12630723
GenImpSD 1.254258014 1.24364860 0.98907990 1.025644293 1.12815770
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.005013043 0.01990095
$statisticalTests
V1 mean
true+avs-anti 0.4319143 0.4319143
true+ms-anti 0.4494832 0.4494832
true+res-anti 0.4020562 0.4020562
imputed+avs-anti 0.3443240 0.3443240
imputed+ms-anti 0.4212245 0.4212245
imputed+res-anti 0.3710733 0.3710733
parts+avs-anti 0.8978057 0.8978057
parts+ms-anti 0.8711881 0.8711881
parts+res-anti 0.9074074 0.9074074
$fuzzyMeasures
V1 mean
Euclidean 0.01597974 0.01597974
AHD 0.20373806 0.20373806
HSD 0.14043065 0.14043065
Bertoluzza 0.07935232 0.07935232
DiffVal 0.06332650 0.06332650
DiffAmb 0.07076335 0.07076335
DiffEV 0.05773562 0.05773562
DiffWidth 0.06330741 0.06330741
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testMatrix2, imputedData = testMatrix2Res,
imputedMask = testMatrix2Mask, cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
$trueValues
[,1] [,2] [,3]
[1,] 0.1199240 0.188146026 0.31481645
[2,] -0.4088952 -0.009244923 0.44777067
[3,] 0.4277991 0.603188626 1.92344212
[4,] -2.2328662 -2.042898771 0.35867035
[5,] -2.5630808 -2.064273777 -1.23140885
[6,] 0.2323925 0.341050237 0.89701427
[7,] -0.9907339 -0.617642400 -0.06840412
[8,] -0.4229014 0.007461673 0.41849777
[9,] -1.3006321 -1.275517766 -0.46704551
[10,] -3.6169809 -3.278239026 -1.14842928
[11,] -0.5573558 -0.515324252 -0.42162779
[12,] -1.6530331 -1.113884835 -0.22295531
[13,] -1.7779261 -1.698957584 0.58819769
[14,] -1.6492244 -1.310178700 -0.08413849
[15,] -0.4535884 -0.375611086 1.18356358
[16,] -0.8892359 -0.816145242 -0.47619296
[17,] 1.5656538 2.115238384 3.00592957
[18,] 0.9068369 1.160665144 1.54206699
[19,] -0.8969491 -0.608124123 -0.30055712
[20,] -1.6964713 -1.350383708 -0.07207102
$mask
[,1] [,2] [,3]
[1,] FALSE FALSE FALSE
[2,] FALSE TRUE FALSE
[3,] FALSE FALSE FALSE
[4,] FALSE FALSE FALSE
[5,] FALSE FALSE FALSE
[6,] FALSE TRUE TRUE
[7,] FALSE FALSE FALSE
[8,] TRUE FALSE FALSE
[9,] FALSE FALSE FALSE
[10,] FALSE FALSE FALSE
[11,] FALSE TRUE FALSE
[12,] FALSE FALSE FALSE
[13,] TRUE FALSE FALSE
[14,] TRUE FALSE TRUE
[15,] FALSE FALSE TRUE
[16,] FALSE TRUE FALSE
[17,] TRUE FALSE FALSE
[18,] FALSE FALSE FALSE
[19,] FALSE FALSE TRUE
[20,] FALSE FALSE FALSE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
X1 X2 X3 mean
MAE 0.15816363 0.18044286 0.3764461 0.2383509
WMAE 0.25232132 9.12089408 0.7923513 3.3885222
MSE 0.04968434 0.04419471 0.1953087 0.0963959
WMSE 0.18867129 319.26814069 0.8957060 106.7841727
NRMSE 0.09791167 0.09052128 0.2146912 0.1343747
$statisticalMeasures
X1 X2 X3 mean
TrueMean -0.571099525 -0.24991605 0.42397056 -0.13234834
ImpMean -0.542822592 -0.08596016 0.32411334 -0.10155647
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.09736335
GenMean -0.892863415 -0.63303381 0.30935695 -0.40551342
GenImpMean -0.887208030 -0.60024263 0.28938551 -0.39935505
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.01947267
TrueSD 1.549888285 0.51582321 0.72660560 0.93077236
ImpSD 1.651290877 0.56980539 0.29546576 0.83885401
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.19550820
GenSD 1.232048554 1.22736828 1.02518086 1.16153256
GenImpSD 1.254258014 1.24364860 0.98907990 1.16232883
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.02486358
$statisticalTests
V1 mean
true+avs-anti 0.1678213 0.1678213
true+ms-anti 0.3094203 0.3094203
true+res-anti 0.3046429 0.3046429
imputed+avs-anti 0.1678213 0.1678213
imputed+ms-anti 0.2548216 0.2548216
imputed+res-anti 0.2427320 0.2427320
parts+avs-anti 0.9944576 0.9944576
parts+ms-anti 0.9913046 0.9913046
parts+res-anti 0.9944576 0.9944576
$fuzzyMeasures
V1 mean
Euclidean 0.02153806 0.02153806
AHD 0.21252430 0.21252430
HSD 0.15907851 0.15907851
Bertoluzza 0.10464542 0.10464542
DiffVal 0.07428797 0.07428797
DiffAmb 0.03563052 0.03563052
DiffEV 0.07534339 0.07534339
DiffWidth 0.05344578 0.05344578
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testDF1, imputedData = testDF1Res, imputedMask = testDF1Mask,
cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
$trueValues
V1 V2 V3 V4
[1,] 0.1199240 0.188146026 0.31481645 0.454346750
[2,] -0.4088952 -0.009244923 0.44777067 1.001830760
[3,] 0.4277991 0.603188626 1.92344212 2.062777662
[4,] -2.2328662 -2.042898771 0.35867035 0.907465245
[5,] -2.5630808 -2.064273777 -1.23140885 -0.927024725
[6,] 0.2323925 0.341050237 0.89701427 1.084108858
[7,] -0.9907339 -0.617642400 -0.06840412 0.117407837
[8,] -0.4229014 0.007461673 0.41849777 0.937398067
[9,] -1.3006321 -1.275517766 -0.46704551 -0.436057542
[10,] -3.6169809 -3.278239026 -1.14842928 -0.917869269
[11,] -0.5573558 -0.515324252 -0.42162779 -0.303922994
[12,] -1.6530331 -1.113884835 -0.22295531 -0.142508395
[13,] -1.7779261 -1.698957584 0.58819769 0.609471724
[14,] -1.6492244 -1.310178700 -0.08413849 0.008898696
[15,] -0.4535884 -0.375611086 1.18356358 1.753746539
[16,] -0.8892359 -0.816145242 -0.47619296 -0.029626635
[17,] 1.5656538 2.115238384 3.00592957 3.082389607
[18,] 0.9068369 1.160665144 1.54206699 1.736698607
[19,] -0.8969491 -0.608124123 -0.30055712 0.287914788
[20,] -1.6964713 -1.350383708 -0.07207102 0.289067511
$mask
V1 V2 V3 V4
[1,] FALSE FALSE FALSE FALSE
[2,] FALSE TRUE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE
[6,] FALSE TRUE TRUE FALSE
[7,] FALSE FALSE FALSE TRUE
[8,] TRUE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE TRUE
[10,] FALSE FALSE FALSE FALSE
[11,] FALSE TRUE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE
[13,] TRUE FALSE FALSE FALSE
[14,] TRUE FALSE TRUE FALSE
[15,] FALSE FALSE TRUE FALSE
[16,] FALSE TRUE FALSE FALSE
[17,] TRUE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE
[19,] FALSE FALSE TRUE TRUE
[20,] FALSE FALSE FALSE TRUE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
V1 V2 V3 V4 mean
MAE 0.15816363 0.18044286 0.3764461 0.10809568 0.20578707
WMAE 0.25232132 9.12089408 0.7923513 0.49790500 2.66586793
MSE 0.04968434 0.04419471 0.1953087 0.01864308 0.07695769
WMSE 0.18867129 319.26814069 0.8957060 0.40012277 80.18816020
NRMSE 0.09791167 0.09052128 0.2146912 0.06818963 0.11782844
$statisticalMeasures
V1 V2 V3 V4 mean
TrueMean -0.571099525 -0.24991605 0.42397056 0.064583148 -0.08311547
ImpMean -0.542822592 -0.08596016 0.32411334 -0.003713996 -0.07709585
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.068297145 0.09009680
GenMean -0.892863415 -0.63303381 0.30935695 0.578825655 -0.15942865
GenImpMean -0.887208030 -0.60024263 0.28938551 0.565166226 -0.15822473
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.013659429 0.01801936
TrueSD 1.549888285 0.51582321 0.72660560 0.343366567 0.78392091
ImpSD 1.651290877 0.56980539 0.29546576 0.290234559 0.70169914
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.053132008 0.15991415
GenSD 1.232048554 1.22736828 1.02518086 1.020631251 1.12630723
GenImpSD 1.254258014 1.24364860 0.98907990 1.025644293 1.12815770
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.005013043 0.01990095
$statisticalTests
V1 mean
true+avs-anti 0.4319143 0.4319143
true+ms-anti 0.4494832 0.4494832
true+res-anti 0.4020562 0.4020562
imputed+avs-anti 0.3443240 0.3443240
imputed+ms-anti 0.4212245 0.4212245
imputed+res-anti 0.3710733 0.3710733
parts+avs-anti 0.8978057 0.8978057
parts+ms-anti 0.8711881 0.8711881
parts+res-anti 0.9074074 0.9074074
$fuzzyMeasures
V1 mean
Euclidean 0.01597974 0.01597974
AHD 0.20373806 0.20373806
HSD 0.14043065 0.14043065
Bertoluzza 0.07935232 0.07935232
DiffVal 0.06332650 0.06332650
DiffAmb 0.07076335 0.07076335
DiffEV 0.05773562 0.05773562
DiffWidth 0.06330741 0.06330741
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testDF2, imputedData = testDF2Res, imputedMask = testDF2Mask,
cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
$trueValues
V1 V2 V3
[1,] 0.1199240 0.188146026 0.31481645
[2,] -0.4088952 -0.009244923 0.44777067
[3,] 0.4277991 0.603188626 1.92344212
[4,] -2.2328662 -2.042898771 0.35867035
[5,] -2.5630808 -2.064273777 -1.23140885
[6,] 0.2323925 0.341050237 0.89701427
[7,] -0.9907339 -0.617642400 -0.06840412
[8,] -0.4229014 0.007461673 0.41849777
[9,] -1.3006321 -1.275517766 -0.46704551
[10,] -3.6169809 -3.278239026 -1.14842928
[11,] -0.5573558 -0.515324252 -0.42162779
[12,] -1.6530331 -1.113884835 -0.22295531
[13,] -1.7779261 -1.698957584 0.58819769
[14,] -1.6492244 -1.310178700 -0.08413849
[15,] -0.4535884 -0.375611086 1.18356358
[16,] -0.8892359 -0.816145242 -0.47619296
[17,] 1.5656538 2.115238384 3.00592957
[18,] 0.9068369 1.160665144 1.54206699
[19,] -0.8969491 -0.608124123 -0.30055712
[20,] -1.6964713 -1.350383708 -0.07207102
$mask
V1 V2 V3
[1,] FALSE FALSE FALSE
[2,] FALSE TRUE FALSE
[3,] FALSE FALSE FALSE
[4,] FALSE FALSE FALSE
[5,] FALSE FALSE FALSE
[6,] FALSE TRUE TRUE
[7,] FALSE FALSE FALSE
[8,] TRUE FALSE FALSE
[9,] FALSE FALSE FALSE
[10,] FALSE FALSE FALSE
[11,] FALSE TRUE FALSE
[12,] FALSE FALSE FALSE
[13,] TRUE FALSE FALSE
[14,] TRUE FALSE TRUE
[15,] FALSE FALSE TRUE
[16,] FALSE TRUE FALSE
[17,] TRUE FALSE FALSE
[18,] FALSE FALSE FALSE
[19,] FALSE FALSE TRUE
[20,] FALSE FALSE FALSE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
V1 V2 V3 mean
MAE 0.15816363 0.18044286 0.3764461 0.2383509
WMAE 0.25232132 9.12089408 0.7923513 3.3885222
MSE 0.04968434 0.04419471 0.1953087 0.0963959
WMSE 0.18867129 319.26814069 0.8957060 106.7841727
NRMSE 0.09791167 0.09052128 0.2146912 0.1343747
$statisticalMeasures
V1 V2 V3 mean
TrueMean -0.571099525 -0.24991605 0.42397056 -0.13234834
ImpMean -0.542822592 -0.08596016 0.32411334 -0.10155647
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.09736335
GenMean -0.892863415 -0.63303381 0.30935695 -0.40551342
GenImpMean -0.887208030 -0.60024263 0.28938551 -0.39935505
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.01947267
TrueSD 1.549888285 0.51582321 0.72660560 0.93077236
ImpSD 1.651290877 0.56980539 0.29546576 0.83885401
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.19550820
GenSD 1.232048554 1.22736828 1.02518086 1.16153256
GenImpSD 1.254258014 1.24364860 0.98907990 1.16232883
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.02486358
$statisticalTests
V1 mean
true+avs-anti 0.1678213 0.1678213
true+ms-anti 0.3094203 0.3094203
true+res-anti 0.3046429 0.3046429
imputed+avs-anti 0.1678213 0.1678213
imputed+ms-anti 0.2548216 0.2548216
imputed+res-anti 0.2427320 0.2427320
parts+avs-anti 0.9944576 0.9944576
parts+ms-anti 0.9913046 0.9913046
parts+res-anti 0.9944576 0.9944576
$fuzzyMeasures
V1 mean
Euclidean 0.02153806 0.02153806
AHD 0.21252430 0.21252430
HSD 0.15907851 0.15907851
Bertoluzza 0.10464542 0.10464542
DiffVal 0.07428797 0.07428797
DiffAmb 0.03563052 0.03563052
DiffEV 0.07534339 0.07534339
DiffWidth 0.05344578 0.05344578
attr(,"class")
[1] "impTest"
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FuzzyImputationTest documentation built on April 3, 2025, 9:23 p.m.
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Function returns correct values
Code
set.seed(123456)
ImputationTests(trueData = testMatrix1, imputedData = testMatrix1Res,
imputedMask = testMatrix1Mask, cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
$trueValues
[,1] [,2] [,3] [,4]
[1,] 0.1199240 0.188146026 0.31481645 0.454346750
[2,] -0.4088952 -0.009244923 0.44777067 1.001830760
[3,] 0.4277991 0.603188626 1.92344212 2.062777662
[4,] -2.2328662 -2.042898771 0.35867035 0.907465245
[5,] -2.5630808 -2.064273777 -1.23140885 -0.927024725
[6,] 0.2323925 0.341050237 0.89701427 1.084108858
[7,] -0.9907339 -0.617642400 -0.06840412 0.117407837
[8,] -0.4229014 0.007461673 0.41849777 0.937398067
[9,] -1.3006321 -1.275517766 -0.46704551 -0.436057542
[10,] -3.6169809 -3.278239026 -1.14842928 -0.917869269
[11,] -0.5573558 -0.515324252 -0.42162779 -0.303922994
[12,] -1.6530331 -1.113884835 -0.22295531 -0.142508395
[13,] -1.7779261 -1.698957584 0.58819769 0.609471724
[14,] -1.6492244 -1.310178700 -0.08413849 0.008898696
[15,] -0.4535884 -0.375611086 1.18356358 1.753746539
[16,] -0.8892359 -0.816145242 -0.47619296 -0.029626635
[17,] 1.5656538 2.115238384 3.00592957 3.082389607
[18,] 0.9068369 1.160665144 1.54206699 1.736698607
[19,] -0.8969491 -0.608124123 -0.30055712 0.287914788
[20,] -1.6964713 -1.350383708 -0.07207102 0.289067511
$mask
[,1] [,2] [,3] [,4]
[1,] FALSE FALSE FALSE FALSE
[2,] FALSE TRUE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE
[6,] FALSE TRUE TRUE FALSE
[7,] FALSE FALSE FALSE TRUE
[8,] TRUE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE TRUE
[10,] FALSE FALSE FALSE FALSE
[11,] FALSE TRUE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE
[13,] TRUE FALSE FALSE FALSE
[14,] TRUE FALSE TRUE FALSE
[15,] FALSE FALSE TRUE FALSE
[16,] FALSE TRUE FALSE FALSE
[17,] TRUE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE
[19,] FALSE FALSE TRUE TRUE
[20,] FALSE FALSE FALSE TRUE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
X1 X2 X3 X4 mean
MAE 0.15816363 0.18044286 0.3764461 0.10809568 0.20578707
WMAE 0.25232132 9.12089408 0.7923513 0.49790500 2.66586793
MSE 0.04968434 0.04419471 0.1953087 0.01864308 0.07695769
WMSE 0.18867129 319.26814069 0.8957060 0.40012277 80.18816020
NRMSE 0.09791167 0.09052128 0.2146912 0.06818963 0.11782844
$statisticalMeasures
X1 X2 X3 X4 mean
TrueMean -0.571099525 -0.24991605 0.42397056 0.064583148 -0.08311547
ImpMean -0.542822592 -0.08596016 0.32411334 -0.003713996 -0.07709585
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.068297145 0.09009680
GenMean -0.892863415 -0.63303381 0.30935695 0.578825655 -0.15942865
GenImpMean -0.887208030 -0.60024263 0.28938551 0.565166226 -0.15822473
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.013659429 0.01801936
TrueSD 1.549888285 0.51582321 0.72660560 0.343366567 0.78392091
ImpSD 1.651290877 0.56980539 0.29546576 0.290234559 0.70169914
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.053132008 0.15991415
GenSD 1.232048554 1.22736828 1.02518086 1.020631251 1.12630723
GenImpSD 1.254258014 1.24364860 0.98907990 1.025644293 1.12815770
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.005013043 0.01990095
$statisticalTests
V1 mean
true+avs-anti 0.4319143 0.4319143
true+ms-anti 0.4494832 0.4494832
true+res-anti 0.4020562 0.4020562
imputed+avs-anti 0.3443240 0.3443240
imputed+ms-anti 0.4212245 0.4212245
imputed+res-anti 0.3710733 0.3710733
parts+avs-anti 0.8978057 0.8978057
parts+ms-anti 0.8711881 0.8711881
parts+res-anti 0.9074074 0.9074074
$fuzzyMeasures
V1 mean
Euclidean 0.01597974 0.01597974
AHD 0.20373806 0.20373806
HSD 0.14043065 0.14043065
Bertoluzza 0.07935232 0.07935232
DiffVal 0.06332650 0.06332650
DiffAmb 0.07076335 0.07076335
DiffEV 0.05773562 0.05773562
DiffWidth 0.06330741 0.06330741
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testMatrix2, imputedData = testMatrix2Res,
imputedMask = testMatrix2Mask, cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
$trueValues
[,1] [,2] [,3]
[1,] 0.1199240 0.188146026 0.31481645
[2,] -0.4088952 -0.009244923 0.44777067
[3,] 0.4277991 0.603188626 1.92344212
[4,] -2.2328662 -2.042898771 0.35867035
[5,] -2.5630808 -2.064273777 -1.23140885
[6,] 0.2323925 0.341050237 0.89701427
[7,] -0.9907339 -0.617642400 -0.06840412
[8,] -0.4229014 0.007461673 0.41849777
[9,] -1.3006321 -1.275517766 -0.46704551
[10,] -3.6169809 -3.278239026 -1.14842928
[11,] -0.5573558 -0.515324252 -0.42162779
[12,] -1.6530331 -1.113884835 -0.22295531
[13,] -1.7779261 -1.698957584 0.58819769
[14,] -1.6492244 -1.310178700 -0.08413849
[15,] -0.4535884 -0.375611086 1.18356358
[16,] -0.8892359 -0.816145242 -0.47619296
[17,] 1.5656538 2.115238384 3.00592957
[18,] 0.9068369 1.160665144 1.54206699
[19,] -0.8969491 -0.608124123 -0.30055712
[20,] -1.6964713 -1.350383708 -0.07207102
$mask
[,1] [,2] [,3]
[1,] FALSE FALSE FALSE
[2,] FALSE TRUE FALSE
[3,] FALSE FALSE FALSE
[4,] FALSE FALSE FALSE
[5,] FALSE FALSE FALSE
[6,] FALSE TRUE TRUE
[7,] FALSE FALSE FALSE
[8,] TRUE FALSE FALSE
[9,] FALSE FALSE FALSE
[10,] FALSE FALSE FALSE
[11,] FALSE TRUE FALSE
[12,] FALSE FALSE FALSE
[13,] TRUE FALSE FALSE
[14,] TRUE FALSE TRUE
[15,] FALSE FALSE TRUE
[16,] FALSE TRUE FALSE
[17,] TRUE FALSE FALSE
[18,] FALSE FALSE FALSE
[19,] FALSE FALSE TRUE
[20,] FALSE FALSE FALSE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
X1 X2 X3 mean
MAE 0.15816363 0.18044286 0.3764461 0.2383509
WMAE 0.25232132 9.12089408 0.7923513 3.3885222
MSE 0.04968434 0.04419471 0.1953087 0.0963959
WMSE 0.18867129 319.26814069 0.8957060 106.7841727
NRMSE 0.09791167 0.09052128 0.2146912 0.1343747
$statisticalMeasures
X1 X2 X3 mean
TrueMean -0.571099525 -0.24991605 0.42397056 -0.13234834
ImpMean -0.542822592 -0.08596016 0.32411334 -0.10155647
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.09736335
GenMean -0.892863415 -0.63303381 0.30935695 -0.40551342
GenImpMean -0.887208030 -0.60024263 0.28938551 -0.39935505
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.01947267
TrueSD 1.549888285 0.51582321 0.72660560 0.93077236
ImpSD 1.651290877 0.56980539 0.29546576 0.83885401
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.19550820
GenSD 1.232048554 1.22736828 1.02518086 1.16153256
GenImpSD 1.254258014 1.24364860 0.98907990 1.16232883
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.02486358
$statisticalTests
V1 mean
true+avs-anti 0.1678213 0.1678213
true+ms-anti 0.3094203 0.3094203
true+res-anti 0.3046429 0.3046429
imputed+avs-anti 0.1678213 0.1678213
imputed+ms-anti 0.2548216 0.2548216
imputed+res-anti 0.2427320 0.2427320
parts+avs-anti 0.9944576 0.9944576
parts+ms-anti 0.9913046 0.9913046
parts+res-anti 0.9944576 0.9944576
$fuzzyMeasures
V1 mean
Euclidean 0.02153806 0.02153806
AHD 0.21252430 0.21252430
HSD 0.15907851 0.15907851
Bertoluzza 0.10464542 0.10464542
DiffVal 0.07428797 0.07428797
DiffAmb 0.03563052 0.03563052
DiffEV 0.07534339 0.07534339
DiffWidth 0.05344578 0.05344578
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testDF1, imputedData = testDF1Res, imputedMask = testDF1Mask,
cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
$trueValues
V1 V2 V3 V4
[1,] 0.1199240 0.188146026 0.31481645 0.454346750
[2,] -0.4088952 -0.009244923 0.44777067 1.001830760
[3,] 0.4277991 0.603188626 1.92344212 2.062777662
[4,] -2.2328662 -2.042898771 0.35867035 0.907465245
[5,] -2.5630808 -2.064273777 -1.23140885 -0.927024725
[6,] 0.2323925 0.341050237 0.89701427 1.084108858
[7,] -0.9907339 -0.617642400 -0.06840412 0.117407837
[8,] -0.4229014 0.007461673 0.41849777 0.937398067
[9,] -1.3006321 -1.275517766 -0.46704551 -0.436057542
[10,] -3.6169809 -3.278239026 -1.14842928 -0.917869269
[11,] -0.5573558 -0.515324252 -0.42162779 -0.303922994
[12,] -1.6530331 -1.113884835 -0.22295531 -0.142508395
[13,] -1.7779261 -1.698957584 0.58819769 0.609471724
[14,] -1.6492244 -1.310178700 -0.08413849 0.008898696
[15,] -0.4535884 -0.375611086 1.18356358 1.753746539
[16,] -0.8892359 -0.816145242 -0.47619296 -0.029626635
[17,] 1.5656538 2.115238384 3.00592957 3.082389607
[18,] 0.9068369 1.160665144 1.54206699 1.736698607
[19,] -0.8969491 -0.608124123 -0.30055712 0.287914788
[20,] -1.6964713 -1.350383708 -0.07207102 0.289067511
$mask
V1 V2 V3 V4
[1,] FALSE FALSE FALSE FALSE
[2,] FALSE TRUE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE
[6,] FALSE TRUE TRUE FALSE
[7,] FALSE FALSE FALSE TRUE
[8,] TRUE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE TRUE
[10,] FALSE FALSE FALSE FALSE
[11,] FALSE TRUE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE
[13,] TRUE FALSE FALSE FALSE
[14,] TRUE FALSE TRUE FALSE
[15,] FALSE FALSE TRUE FALSE
[16,] FALSE TRUE FALSE FALSE
[17,] TRUE FALSE FALSE FALSE
[18,] FALSE FALSE FALSE FALSE
[19,] FALSE FALSE TRUE TRUE
[20,] FALSE FALSE FALSE TRUE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
V1 V2 V3 V4 mean
MAE 0.15816363 0.18044286 0.3764461 0.10809568 0.20578707
WMAE 0.25232132 9.12089408 0.7923513 0.49790500 2.66586793
MSE 0.04968434 0.04419471 0.1953087 0.01864308 0.07695769
WMSE 0.18867129 319.26814069 0.8957060 0.40012277 80.18816020
NRMSE 0.09791167 0.09052128 0.2146912 0.06818963 0.11782844
$statisticalMeasures
V1 V2 V3 V4 mean
TrueMean -0.571099525 -0.24991605 0.42397056 0.064583148 -0.08311547
ImpMean -0.542822592 -0.08596016 0.32411334 -0.003713996 -0.07709585
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.068297145 0.09009680
GenMean -0.892863415 -0.63303381 0.30935695 0.578825655 -0.15942865
GenImpMean -0.887208030 -0.60024263 0.28938551 0.565166226 -0.15822473
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.013659429 0.01801936
TrueSD 1.549888285 0.51582321 0.72660560 0.343366567 0.78392091
ImpSD 1.651290877 0.56980539 0.29546576 0.290234559 0.70169914
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.053132008 0.15991415
GenSD 1.232048554 1.22736828 1.02518086 1.020631251 1.12630723
GenImpSD 1.254258014 1.24364860 0.98907990 1.025644293 1.12815770
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.005013043 0.01990095
$statisticalTests
V1 mean
true+avs-anti 0.4319143 0.4319143
true+ms-anti 0.4494832 0.4494832
true+res-anti 0.4020562 0.4020562
imputed+avs-anti 0.3443240 0.3443240
imputed+ms-anti 0.4212245 0.4212245
imputed+res-anti 0.3710733 0.3710733
parts+avs-anti 0.8978057 0.8978057
parts+ms-anti 0.8711881 0.8711881
parts+res-anti 0.9074074 0.9074074
$fuzzyMeasures
V1 mean
Euclidean 0.01597974 0.01597974
AHD 0.20373806 0.20373806
HSD 0.14043065 0.14043065
Bertoluzza 0.07935232 0.07935232
DiffVal 0.06332650 0.06332650
DiffAmb 0.07076335 0.07076335
DiffEV 0.05773562 0.05773562
DiffWidth 0.06330741 0.06330741
attr(,"class")
[1] "impTest"
Code
set.seed(123456)
ImputationTests(trueData = testDF2, imputedData = testDF2Res, imputedMask = testDF2Mask,
cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
$trueValues
V1 V2 V3
[1,] 0.1199240 0.188146026 0.31481645
[2,] -0.4088952 -0.009244923 0.44777067
[3,] 0.4277991 0.603188626 1.92344212
[4,] -2.2328662 -2.042898771 0.35867035
[5,] -2.5630808 -2.064273777 -1.23140885
[6,] 0.2323925 0.341050237 0.89701427
[7,] -0.9907339 -0.617642400 -0.06840412
[8,] -0.4229014 0.007461673 0.41849777
[9,] -1.3006321 -1.275517766 -0.46704551
[10,] -3.6169809 -3.278239026 -1.14842928
[11,] -0.5573558 -0.515324252 -0.42162779
[12,] -1.6530331 -1.113884835 -0.22295531
[13,] -1.7779261 -1.698957584 0.58819769
[14,] -1.6492244 -1.310178700 -0.08413849
[15,] -0.4535884 -0.375611086 1.18356358
[16,] -0.8892359 -0.816145242 -0.47619296
[17,] 1.5656538 2.115238384 3.00592957
[18,] 0.9068369 1.160665144 1.54206699
[19,] -0.8969491 -0.608124123 -0.30055712
[20,] -1.6964713 -1.350383708 -0.07207102
$mask
V1 V2 V3
[1,] FALSE FALSE FALSE
[2,] FALSE TRUE FALSE
[3,] FALSE FALSE FALSE
[4,] FALSE FALSE FALSE
[5,] FALSE FALSE FALSE
[6,] FALSE TRUE TRUE
[7,] FALSE FALSE FALSE
[8,] TRUE FALSE FALSE
[9,] FALSE FALSE FALSE
[10,] FALSE FALSE FALSE
[11,] FALSE TRUE FALSE
[12,] FALSE FALSE FALSE
[13,] TRUE FALSE FALSE
[14,] TRUE FALSE TRUE
[15,] FALSE FALSE TRUE
[16,] FALSE TRUE FALSE
[17,] TRUE FALSE FALSE
[18,] FALSE FALSE FALSE
[19,] FALSE FALSE TRUE
[20,] FALSE FALSE FALSE
$nonFNNumbers
V1 mean
0 0
$errorMatrix
V1 V2 V3 mean
MAE 0.15816363 0.18044286 0.3764461 0.2383509
WMAE 0.25232132 9.12089408 0.7923513 3.3885222
MSE 0.04968434 0.04419471 0.1953087 0.0963959
WMSE 0.18867129 319.26814069 0.8957060 106.7841727
NRMSE 0.09791167 0.09052128 0.2146912 0.1343747
$statisticalMeasures
V1 V2 V3 mean
TrueMean -0.571099525 -0.24991605 0.42397056 -0.13234834
ImpMean -0.542822592 -0.08596016 0.32411334 -0.10155647
AbsDiffTrueImpMean 0.028276932 0.16395589 0.09985722 0.09736335
GenMean -0.892863415 -0.63303381 0.30935695 -0.40551342
GenImpMean -0.887208030 -0.60024263 0.28938551 -0.39935505
AbsDiffGenImpMean 0.005655385 0.03279118 0.01997144 0.01947267
TrueSD 1.549888285 0.51582321 0.72660560 0.93077236
ImpSD 1.651290877 0.56980539 0.29546576 0.83885401
AbsDiffTrueImpSD 0.101402593 0.05398218 0.43113984 0.19550820
GenSD 1.232048554 1.22736828 1.02518086 1.16153256
GenImpSD 1.254258014 1.24364860 0.98907990 1.16232883
AbsDiffGenImpSD 0.022209460 0.01628032 0.03610096 0.02486358
$statisticalTests
V1 mean
true+avs-anti 0.1678213 0.1678213
true+ms-anti 0.3094203 0.3094203
true+res-anti 0.3046429 0.3046429
imputed+avs-anti 0.1678213 0.1678213
imputed+ms-anti 0.2548216 0.2548216
imputed+res-anti 0.2427320 0.2427320
parts+avs-anti 0.9944576 0.9944576
parts+ms-anti 0.9913046 0.9913046
parts+res-anti 0.9944576 0.9944576
$fuzzyMeasures
V1 mean
Euclidean 0.02153806 0.02153806
AHD 0.21252430 0.21252430
HSD 0.15907851 0.15907851
Bertoluzza 0.10464542 0.10464542
DiffVal 0.07428797 0.07428797
DiffAmb 0.03563052 0.03563052
DiffEV 0.07534339 0.07534339
DiffWidth 0.05344578 0.05344578
attr(,"class")
[1] "impTest"
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