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Converting non-normal to normal distributions

Data which is not normal should be transformed so that it becomes normally distributed before applying parametric tests. Some of these transformations include: 

lognormal (or log base e or ln) (Y)
1 / Y
square (Y)
1 / square (Y)
square root (Y) 
1 / square root (Y)
Box-Cox transformation is a convenient method that detects the deviation and applies the appropriate transformation to data. The package fifer provides a convenient function for this: 

code: 

> library(fifer)
> normalized_vect = boxcoxR(vect)

Where vect is a number series (vector). 


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