CRAN Package Check Results for Package MCDA

Last updated on 2019-03-25 09:57:10 CET.

Check Details

Version: 0.0.19
Check: tests
Result: ERROR
     Running 'AHP.R' [0s/1s]
     Running 'LPDMRSort.R' [1s/1s]
     Running 'LPDMRSortIdentifyIncompatibleAssignments.R' [4s/4s]
     Running 'LPDMRSortIdentifyUsedDictatorProfiles.R' [0s/1s]
     Running 'LPDMRSortIdentifyUsedVetoProfiles.R' [0s/1s]
     Running 'LPDMRSortInferenceApprox.R' [4s/4s]
     Running 'LPDMRSortInferenceExact.R' [3s/3s]
     Running 'MARE.R' [0s/1s]
     Running 'MRSort.R' [0s/1s]
     Running 'MRSortIdentifyIncompatibleAssignments.R' [2s/3s]
     Running 'MRSortIdentifyUsedVetoProfiles.R' [0s/1s]
     Running 'MRSortInferenceApprox.R' [7s/8s]
     Running 'MRSortInferenceExact.R' [0s/1s]
     Running 'SRMP.R' [1s/1s]
     Running 'SRMPInference.R' [4s/5s]
     Running 'SRMPInferenceApprox.R' [49s/52s]
     Running 'SRMPInferenceApproxFixedLexicographicOrder.R' [15s/17s]
     Running 'SRMPInferenceApproxFixedProfilesNumber.R' [58s/62s]
     Running 'SRMPInferenceFixedLexicographicOrder.R' [1s/1s]
     Running 'SRMPInferenceFixedProfilesNumber.R' [4s/5s]
     Running 'SRMPInferenceNoInconsist.R' [1s/1s]
     Running 'SRMPInferenceNoInconsistFixedLexicographicOrder.R' [3s/3s]
     Running 'SRMPInferenceNoInconsistFixedProfilesNumber.R' [3s/3s]
     Running 'TOPSIS.R' [0s/1s]
     Running 'UTA.R' [0s/1s]
     Running 'UTADIS.R' [0s/1s]
     Running 'UTASTAR.R' [0s/1s]
     Running 'additiveValueFunctionElicitation.R' [0s/0s]
     Running 'applyPiecewiseLinearValueFunctionsOnPerformanceTable.R' [0s/1s]
     Running 'assignAlternativesToCategoriesByThresholds.R' [0s/1s]
     Running 'normalizePerformanceTable.R' [0s/1s]
     Running 'pairwiseConsistencyMeasures.R' [0s/1s]
     Running 'plotAlternativesValuesPreorder.R' [1s/2s]
     Running 'plotMRSortSortingProblem.R' [0s/1s]
     Running 'plotRadarPerformanceTable.R' [0s/1s]
     Running 'weightedSum.R' [0s/1s]
    Running the tests in 'tests/SRMPInferenceApproxFixedProfilesNumber.R' failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
     >
     > expectedValues <- expectedValues[altIDs]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Final model fitness: 77.78%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.0.19
Check: tests
Result: ERROR
     Running ‘AHP.R’ [0s/1s]
     Running ‘LPDMRSort.R’ [0s/1s]
     Running ‘LPDMRSortIdentifyIncompatibleAssignments.R’ [3s/5s]
     Running ‘LPDMRSortIdentifyUsedDictatorProfiles.R’ [0s/1s]
     Running ‘LPDMRSortIdentifyUsedVetoProfiles.R’ [0s/1s]
     Running ‘LPDMRSortInferenceApprox.R’ [3s/4s]
     Running ‘LPDMRSortInferenceExact.R’ [2s/4s]
     Running ‘MARE.R’ [0s/1s]
     Running ‘MRSort.R’ [0s/1s]
     Running ‘MRSortIdentifyIncompatibleAssignments.R’ [2s/3s]
     Running ‘MRSortIdentifyUsedVetoProfiles.R’ [0s/1s]
     Running ‘MRSortInferenceApprox.R’ [5s/8s]
     Running ‘MRSortInferenceExact.R’ [0s/1s]
     Running ‘SRMP.R’ [0s/1s]
     Running ‘SRMPInference.R’ [3s/4s]
     Running ‘SRMPInferenceApprox.R’ [36s/48s]
     Running ‘SRMPInferenceApproxFixedLexicographicOrder.R’ [12s/18s]
     Running ‘SRMPInferenceApproxFixedProfilesNumber.R’ [42s/62s]
     Running ‘SRMPInferenceFixedLexicographicOrder.R’ [0s/1s]
     Running ‘SRMPInferenceFixedProfilesNumber.R’ [3s/5s]
     Running ‘SRMPInferenceNoInconsist.R’ [1s/1s]
     Running ‘SRMPInferenceNoInconsistFixedLexicographicOrder.R’ [2s/4s]
     Running ‘SRMPInferenceNoInconsistFixedProfilesNumber.R’ [2s/3s]
     Running ‘TOPSIS.R’ [0s/1s]
     Running ‘UTA.R’ [0s/1s]
     Running ‘UTADIS.R’ [0s/1s]
     Running ‘UTASTAR.R’ [0s/1s]
     Running ‘additiveValueFunctionElicitation.R’ [0s/1s]
     Running ‘applyPiecewiseLinearValueFunctionsOnPerformanceTable.R’ [0s/1s]
     Running ‘assignAlternativesToCategoriesByThresholds.R’ [0s/1s]
     Running ‘normalizePerformanceTable.R’ [0s/1s]
     Running ‘pairwiseConsistencyMeasures.R’ [0s/1s]
     Running ‘plotAlternativesValuesPreorder.R’ [1s/2s]
     Running ‘plotMRSortSortingProblem.R’ [0s/1s]
     Running ‘plotRadarPerformanceTable.R’ [0s/1s]
     Running ‘weightedSum.R’ [0s/1s]
    Running the tests in ‘tests/SRMPInferenceApproxFixedProfilesNumber.R’ failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
     >
     > expectedValues <- expectedValues[altIDs]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Final model fitness: 77.78%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.0.19
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘cplexAPI’
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 0.0.19
Check: tests
Result: ERROR
     Running ‘AHP.R’
     Running ‘LPDMRSort.R’
     Running ‘LPDMRSortIdentifyIncompatibleAssignments.R’
     Running ‘LPDMRSortIdentifyUsedDictatorProfiles.R’
     Running ‘LPDMRSortIdentifyUsedVetoProfiles.R’
     Running ‘LPDMRSortInferenceApprox.R’
     Running ‘LPDMRSortInferenceExact.R’
     Running ‘MARE.R’
     Running ‘MRSort.R’
     Running ‘MRSortIdentifyIncompatibleAssignments.R’
     Running ‘MRSortIdentifyUsedVetoProfiles.R’
     Running ‘MRSortInferenceApprox.R’
     Running ‘MRSortInferenceExact.R’
     Running ‘SRMP.R’
     Running ‘SRMPInference.R’
     Running ‘SRMPInferenceApprox.R’ [55s/62s]
     Running ‘SRMPInferenceApproxFixedLexicographicOrder.R’ [17s/20s]
     Running ‘SRMPInferenceApproxFixedProfilesNumber.R’ [56s/61s]
     Running ‘SRMPInferenceFixedLexicographicOrder.R’
     Running ‘SRMPInferenceFixedProfilesNumber.R’
     Running ‘SRMPInferenceNoInconsist.R’
     Running ‘SRMPInferenceNoInconsistFixedLexicographicOrder.R’
     Running ‘SRMPInferenceNoInconsistFixedProfilesNumber.R’
     Running ‘TOPSIS.R’
     Running ‘UTA.R’
     Running ‘UTADIS.R’
     Running ‘UTASTAR.R’
     Running ‘additiveValueFunctionElicitation.R’
     Running ‘applyPiecewiseLinearValueFunctionsOnPerformanceTable.R’
     Running ‘assignAlternativesToCategoriesByThresholds.R’
     Running ‘normalizePerformanceTable.R’
     Running ‘pairwiseConsistencyMeasures.R’
     Running ‘plotAlternativesValuesPreorder.R’
     Running ‘plotMRSortSortingProblem.R’
     Running ‘plotRadarPerformanceTable.R’
     Running ‘weightedSum.R’
    Running the tests in ‘tests/SRMPInferenceApproxFixedProfilesNumber.R’ failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
     >
     > expectedValues <- expectedValues[altIDs]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Final model fitness: 77.78%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.0.19
Check: tests
Result: ERROR
     Running ‘AHP.R’
     Running ‘LPDMRSort.R’
     Running ‘LPDMRSortIdentifyIncompatibleAssignments.R’
     Running ‘LPDMRSortIdentifyUsedDictatorProfiles.R’
     Running ‘LPDMRSortIdentifyUsedVetoProfiles.R’
     Running ‘LPDMRSortInferenceApprox.R’
     Running ‘LPDMRSortInferenceExact.R’
     Running ‘MARE.R’
     Running ‘MRSort.R’
     Running ‘MRSortIdentifyIncompatibleAssignments.R’
     Running ‘MRSortIdentifyUsedVetoProfiles.R’
     Running ‘MRSortInferenceApprox.R’
     Running ‘MRSortInferenceExact.R’
     Running ‘SRMP.R’
     Running ‘SRMPInference.R’
     Running ‘SRMPInferenceApprox.R’ [51s/61s]
     Running ‘SRMPInferenceApproxFixedLexicographicOrder.R’ [17s/20s]
     Running ‘SRMPInferenceApproxFixedProfilesNumber.R’ [54s/62s]
     Running ‘SRMPInferenceFixedLexicographicOrder.R’
     Running ‘SRMPInferenceFixedProfilesNumber.R’
     Running ‘SRMPInferenceNoInconsist.R’
     Running ‘SRMPInferenceNoInconsistFixedLexicographicOrder.R’
     Running ‘SRMPInferenceNoInconsistFixedProfilesNumber.R’
     Running ‘TOPSIS.R’
     Running ‘UTA.R’
     Running ‘UTADIS.R’
     Running ‘UTASTAR.R’
     Running ‘additiveValueFunctionElicitation.R’
     Running ‘applyPiecewiseLinearValueFunctionsOnPerformanceTable.R’
     Running ‘assignAlternativesToCategoriesByThresholds.R’
     Running ‘normalizePerformanceTable.R’
     Running ‘pairwiseConsistencyMeasures.R’
     Running ‘plotAlternativesValuesPreorder.R’
     Running ‘plotMRSortSortingProblem.R’
     Running ‘plotRadarPerformanceTable.R’
     Running ‘weightedSum.R’
    Running the tests in ‘tests/SRMPInferenceApprox.R’ failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > expectedValues <- expectedValues[c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApprox(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18"))
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Best fitness so far: 88.89%"
     [1] "Final model fitness: 88.89%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18"))
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
    Running the tests in ‘tests/SRMPInferenceApproxFixedProfilesNumber.R’ failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
     >
     > expectedValues <- expectedValues[altIDs]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Final model fitness: 77.78%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.0.19
Check: tests
Result: ERROR
     Running 'AHP.R' [0s]
     Running 'LPDMRSort.R' [1s]
     Running 'LPDMRSortIdentifyIncompatibleAssignments.R' [6s]
     Running 'LPDMRSortIdentifyUsedDictatorProfiles.R' [1s]
     Running 'LPDMRSortIdentifyUsedVetoProfiles.R' [0s]
     Running 'LPDMRSortInferenceApprox.R' [4s]
     Running 'LPDMRSortInferenceExact.R' [4s]
     Running 'MARE.R' [0s]
     Running 'MRSort.R' [1s]
     Running 'MRSortIdentifyIncompatibleAssignments.R' [2s]
     Running 'MRSortIdentifyUsedVetoProfiles.R' [0s]
     Running 'MRSortInferenceApprox.R' [6s]
     Running 'MRSortInferenceExact.R' [0s]
     Running 'SRMP.R' [1s]
     Running 'SRMPInference.R' [3s]
     Running 'SRMPInferenceApprox.R' [48s]
     Running 'SRMPInferenceApproxFixedLexicographicOrder.R' [14s]
     Running 'SRMPInferenceApproxFixedProfilesNumber.R' [62s]
     Running 'SRMPInferenceFixedLexicographicOrder.R' [3s]
     Running 'SRMPInferenceFixedProfilesNumber.R' [1s]
     Running 'SRMPInferenceNoInconsist.R' [1s]
     Running 'SRMPInferenceNoInconsistFixedLexicographicOrder.R' [57s]
     Running 'SRMPInferenceNoInconsistFixedProfilesNumber.R' [10s]
     Running 'TOPSIS.R' [1s]
     Running 'UTA.R' [0s]
     Running 'UTADIS.R' [0s]
     Running 'UTASTAR.R' [0s]
     Running 'additiveValueFunctionElicitation.R' [1s]
     Running 'applyPiecewiseLinearValueFunctionsOnPerformanceTable.R' [0s]
     Running 'assignAlternativesToCategoriesByThresholds.R' [1s]
     Running 'normalizePerformanceTable.R' [0s]
     Running 'pairwiseConsistencyMeasures.R' [0s]
     Running 'plotAlternativesValuesPreorder.R' [1s]
     Running 'plotMRSortSortingProblem.R' [1s]
     Running 'plotRadarPerformanceTable.R' [1s]
     Running 'weightedSum.R' [0s]
    Running the tests in 'tests/SRMPInferenceApproxFixedProfilesNumber.R' failed.
    Complete output:
     > # ranking some students
     >
     > library(MCDA)
     >
     > # the performance table
     >
     > performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
     + c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
     + c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
     + c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))
     >
     > criteriaMinMax <- c("max","max","max")
     >
     > rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a16","a17","a18","a19","a20","a21","a22","a23","a24")
     >
     > colnames(performanceTable) <- c("c1","c2","c3")
     >
     > names(criteriaMinMax) <- colnames(performanceTable)
     >
     > # expected result for the tests below
     >
     > expectedValues <- c(10,7,13,3,5,1,10,7,13,4,6,2,14,12,8,15,11,9,4,6,2,6,2,4)
     >
     > names(expectedValues) <- rownames(performanceTable)
     >
     > altIDs <- c("a1","a3","a7","a9","a13","a14","a15","a16","a17","a18")
     >
     > expectedValues <- expectedValues[altIDs]
     >
     > expectedValues <- expectedValues - min(expectedValues) + 1
     >
     > # test - preferences and indifferences
     >
     > preferencePairs <- matrix(c("a16","a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12",
     + "a13","a3","a14","a17","a1","a18","a15","a2","a11","a5","a10","a4","a12","a6"),14,2)
     > indifferencePairs <- matrix(c("a3","a1","a2","a11","a11","a20","a10","a10","a19","a12","a12","a21",
     + "a9","a7","a8","a20","a22","a22","a19","a24","a24","a21","a23","a23"),12,2)
     >
     > set.seed(1)
     >
     > result<-SRMPInferenceApproxFixedProfilesNumber(performanceTable, criteriaMinMax, 3, preferencePairs, indifferencePairs, alternativesIDs = altIDs)
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Best fitness so far: 77.78%"
     [1] "Final model fitness: 77.78%"
     >
     > alternativesValues<-SRMP(performanceTable, result$referenceProfiles, result$lexicographicOrder, result$criteriaWeights, criteriaMinMax, alternativesIDs = altIDs)
     >
     > stopifnot(all(alternativesValues == expectedValues))
     Error: all(alternativesValues == expectedValues) is not TRUE
     Execution halted
Flavor: r-devel-windows-ix86+x86_64