testDecisionTree: Decide what correlation test to perform and obtain statistics
In moosik/snorm: Supporting function for supervised exploration of data structure

Description Usage Arguments Value See Also

Given the names of two variable ind1 and ind2 in a data frame df decide which test should be performed to calculate the relationship between these variables. The decision is based on their respective classes. Class pairs and the corresponing tests: factor/factor - Chi-square (with or without a Monte-Carlo simulation), numeric/numeric and numeric/factor - anova F test

1	testDecisionTree(ind1, ind2, df)

`ind1`	a string to indicate the name of the first variable
`ind2`	a string to indicate the name of the second variable
`df`	a data frame with the variables
`chisq.p.val.sim`	boolean to indicate whether the simulation should be performed for the Chi-square test. Default is TRUE

p.value: P value for the test
statistic: Test statistic
test.type: Type of the test performed: anova F test or Chi-square test

variablesRelation, carefulChisq, carefulLM

moosik/snorm documentation built on May 23, 2019, 6:11 a.m.