ZScoreChallengeOutliers.Rd
If the Z-score of a sample for the selected column corresponds with a p-value less than 0.05, then the sample is considered an outlier and removed.
ZScoreChallengeOutliers(inputted.data, column.to.perform.outlier.analysis)
inputted.data | A dataframe |
---|---|
column.to.perform.outlier.analysis | Name of column in dataframe to evaluate for outliers. The column should contain continuous data. |
A dataframe with outlier rows removed.
Other Preprocessing functions:
AddColBinnedToBinary()
,
AddColBinnedToQuartiles()
,
AddPCsToEnd()
,
ConvertDataToPercentiles()
,
CorAssoTestMultipleWithErrorHandling()
,
DownSampleDataframe()
,
GenerateElbowPlotPCA()
,
GeneratePC1andPC2PlotsWithAndWithoutOutliers()
,
Log2TargetDensityPlotComparison()
,
LookAtPCFeatureLoadings()
,
MultipleColumnsNormalCheckThenBoxCox()
,
NormalCheckThenBoxCoxTransform()
,
RanomlySelectOneRowForEach()
,
RecodeIdentifier()
,
RemoveColWithAllZeros()
,
RemoveRowsBasedOnCol()
,
RemoveSamplesWithInstability()
,
SplitIntoTrainTest()
,
StabilityTestingAcrossVisits()
,
SubsetDataByContinuousCol()
,
TwoSampleTTest()
,
captureSessionInfo()
,
correlation.association.test()
,
describeNumericalColumnsWithLevels()
,
describeNumericalColumns()
,
generate.descriptive.plots.save.pdf()
,
generate.descriptive.plots()
identifier.col <- c("a", "a", "a", "b", "b", "b", "c") value.col <- c(1, 2, 3, 1, 1, 1, 100) input.data.frame <- as.data.frame(cbind(identifier.col, value.col)) results <- ZScoreChallengeOutliers(input.data.frame, "value.col") results#> identifier.col value.col #> 1 a 1 #> 2 a 2 #> 3 a 3 #> 4 b 1 #> 5 b 1 #> 6 b 1