tests/testthat/_snaps/ApplyStatisticalTests.md

In FuzzyImputationTest: Imputation Procedures and Quality Tests for Fuzzy Data

Function returns correct values

Code
  set.seed(123456)
  ApplyStatisticalTests(trueData = testMatrix1, imputedData = testMatrix1Res,
    imputedMask = testMatrix1Mask, cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
                          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

Code
  set.seed(123456)
  ApplyStatisticalTests(trueData = testDF1, imputedData = testDF1Res,
    imputedMask = testDF1Mask, cutsNumber = 100, K = 10, trapezoidal = TRUE)
Output
                          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

Code
  set.seed(123456)
  ApplyStatisticalTests(trueData = testMatrix2, imputedData = testMatrix2Res,
    imputedMask = testMatrix2Mask, cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
                          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

Code
  set.seed(123456)
  ApplyStatisticalTests(trueData = testDF2, imputedData = testDF2Res,
    imputedMask = testDF2Mask, cutsNumber = 100, K = 10, trapezoidal = FALSE)
Output
                          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

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