NormalCheckThenBoxCoxTransform.Rd
Cannot use boxcox if data has zeroes. If data has zeros, then do this: Add 1 if most values are greater than 1, else if most of values <1, multiply 10 or 100, then add 1. Then do boxcox.
NormalCheckThenBoxCoxTransform(input.data, alpha.for.shapiro)
input.data | A numerical data vector. |
---|---|
alpha.for.shapiro | Numerical value from 0 to 1. Threshold for what is considered not normal. If p-value is less than this threshold, then the data is considered not normal. |
A List with 4 elements:
If data is non-normal, then a vector of the transformed data is outputted. If data is normal, then this is NULL.
If data is non-normal, then a number specifying the lambda used for boxcox is outputted. If data is normal, then this is NULL.
P-value from the Shapiro test.
Boolean indicating if boxcox transformation was performed.
Other Preprocessing functions:
AddColBinnedToBinary()
,
AddColBinnedToQuartiles()
,
AddPCsToEnd()
,
ConvertDataToPercentiles()
,
CorAssoTestMultipleWithErrorHandling()
,
DownSampleDataframe()
,
GenerateElbowPlotPCA()
,
GeneratePC1andPC2PlotsWithAndWithoutOutliers()
,
Log2TargetDensityPlotComparison()
,
LookAtPCFeatureLoadings()
,
MultipleColumnsNormalCheckThenBoxCox()
,
RanomlySelectOneRowForEach()
,
RecodeIdentifier()
,
RemoveColWithAllZeros()
,
RemoveRowsBasedOnCol()
,
RemoveSamplesWithInstability()
,
SplitIntoTrainTest()
,
StabilityTestingAcrossVisits()
,
SubsetDataByContinuousCol()
,
TwoSampleTTest()
,
ZScoreChallengeOutliers()
,
captureSessionInfo()
,
correlation.association.test()
,
describeNumericalColumnsWithLevels()
,
describeNumericalColumns()
,
generate.descriptive.plots.save.pdf()
,
generate.descriptive.plots()