Takes multiple vectors and do correlation/association testing with all of them

Uses correlation.association.test() for multiple pairs of data vectors and then outputs results into a text file.

CorAssoTestMultipleWithErrorHandling(
  data.vectors1,
  variable.types1,
  names1,
  data.vectors2,
  variable.types2,
  names2,
  table.name
)

Arguments

data.vectors1	List of numerical vectors.
variable.types1	A vector of strings where each element can be: "continuous", "categorical", or "ordinal". Specifies data type of vectors in data.vectors1.
names1	A vector strings that specify the names that should go with each numerical vector in data.vectors1.
data.vectors2	List of numerical vectors.
variable.types2	A vector of strings where each element can be: "continuous", "categorical", or "ordinal". Specifies data type of vectors in data.vectors2.
names2	A vector strings that specify the names that should go with each numerical vector in data.vectors2.
table.name	Name of the outputted text file.

Value

No object is returned, but function creates a text file with the p-values from the testings.

Details

This function can be used for batch effect testing. Example: We can ask if the dependent variable significantly differs based on gender or race. If it does, then it could impact the relationship of the independent variable with the dependent variable. See ancovall for functions that can adjust for covariates like gender and race.

See also

Other Preprocessing functions: AddColBinnedToBinary(), AddColBinnedToQuartiles(), AddPCsToEnd(), ConvertDataToPercentiles(), DownSampleDataframe(), GenerateElbowPlotPCA(), GeneratePC1andPC2PlotsWithAndWithoutOutliers(), Log2TargetDensityPlotComparison(), LookAtPCFeatureLoadings(), MultipleColumnsNormalCheckThenBoxCox(), NormalCheckThenBoxCoxTransform(), RanomlySelectOneRowForEach(), RecodeIdentifier(), RemoveColWithAllZeros(), RemoveRowsBasedOnCol(), RemoveSamplesWithInstability(), SplitIntoTrainTest(), StabilityTestingAcrossVisits(), SubsetDataByContinuousCol(), TwoSampleTTest(), ZScoreChallengeOutliers(), captureSessionInfo(), correlation.association.test(), describeNumericalColumnsWithLevels(), describeNumericalColumns(), generate.descriptive.plots.save.pdf(), generate.descriptive.plots()