# Deciding on power to use for box-cox transformation

In R, I am using the bc function to do a box-cox transformation. What factors do I need to consider when setting p (the power argument)?

• The answer varies according to your reason for considering the transformation: whether it is for exploratory or confirmatory purposes, whether it is a dependent or independent variable in a regression, and so on. Could you perhaps share some of that relevant information with us so we can give you appropriate, focused answers?
– whuber
Commented May 30, 2012 at 1:15
• It's for exploratory purposes. I'd like to transform certain measures of an event into a comparable space for clustering (euclidean distance perhaps?). In order to get a roughly normal distribution in order to use scale to get standard scores, I'm transforming these measures Commented May 30, 2012 at 8:32

library(geoR)
bc(x=vecToTransform, p=boxcoxfit(y)$lambda)  If this is on a single variable, a likelihood profile wrt p is typically used, as in Wikipedia example. Note that you need to use the right scale with geometric means of your variable and such for it to make sense. • Thanks. What do you mean by "Note that you need to use the right scale with geometric means of your variable and such for it to make sense." How can I implement this? Commented May 29, 2012 at 23:16 • Box-Cox transformation is not just$x^\lambda$, it is$\lambda\frac{x^\lambda - 1}{\dot x^{\lambda-1}}$where$\dot x\$ is the geometric mean. The reason for introducing this geometric mean is to provide correct likelihood ratio tests. Commented May 30, 2012 at 1:27