Box Cox Power Transformation. There s a wonderful article by osborne. The usual assumption of parameter lambda values varies between 5 and 5.
When some of the data are negative a shift parameter c needs to be added to all observations in the formulae above x is replaced with x c. This is useful for modeling issues related to heteroscedasticity non constant variance or other situations where normality is desired. X 位 log x when 位 0.
The method checks for the smallest standard deviation.
Box cox 1964. The box cox transformation has the following mathematical form 饾憣饾憣 饾憢饾憢 饾浛饾浛 饾渾饾渾 where 位 is the exponent power and 未 is a shift amount that is added when xis zero or negative. Applying the boxcox transformation to data without the need of any underlying model can be done currently using the package geor. Specifically you can use the function boxcoxfit for finding the best parameter and then predict the transformed variables using the function bctransform.
