bcPower: Box-Cox, Yeo-Johnson and Basic Power Transformations
In jonathon-love/car: Companion to Applied Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

Transform the elements of a vector using, the Box-Cox, Yeo-Johnson, or simple power transformations.

bcPower(U, lambda, jacobian.adjusted=FALSE, gamma=NULL)

yjPower(U, lambda, jacobian.adjusted = FALSE)

basicPower(U,lambda, gamma=NULL)

`U`	A vector, matrix or data.frame of values to be transformed
`lambda`	The one-dimensional transformation parameter, usually in the range from -2 to 2, or if `U` is a matrix or data frame, a vector of length `ncol(U)` of transformation parameters
`jacobian.adjusted`	If `TRUE`, the transformation is normalized to have Jacobian equal to one. The default is `FALSE`.
`gamma`	For bcPower or basicPower, the transformation is of U + gamma, where gamma is a positive number called a start that must be large enough so that U + gamma is strictly positive.

The Box-Cox family of scaled power transformations equals (U^(lambda)-1)/lambda for lambda not equal to zero, and log(U) if lambda = 0. If gamma is not specified, it is set equal to zero. U + gamma must be strictly positive to use this family.

If family="yeo.johnson" then the Yeo-Johnson transformations are used. This is the Box-Cox transformation of U+1 for nonnegative values, and of |U|+1 with parameter 2-lambda for U negative. An alternative family to the Yeo-Johnson family is the skewPower family that requires estimating both a power and an second parameter.

The basic power transformation returns U^{λ} if λ is not zero, and \log(λ) otherwise.

If jacobian.adjusted is TRUE, then the scaled transformations are divided by the Jacobian, which is a function of the geometric mean of U for skewPower and yjpower and of U + gamma for bcPower. With this adjustment, the Jacobian of the transformation is always equal to 1.

Missing values are permitted, and return NA where ever U is equal to NA.

Returns a vector or matrix of transformed values.

Sanford Weisberg, <sandy@umn.edu>

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Hawkins, D. and Weisberg, S. (2015) Combining the Box-Cox Power and Genralized Log Transformations to Accomodate Negative Responses, submitted for publication.

Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley Wiley, Chapter 7.

Yeo, In-Kwon and Johnson, Richard (2000) A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954-959.

powerTransform, skewPower

U <- c(NA, (-3:3))
## Not run: bcPower(U, 0)  # produces an error as U has negative values
bcPower(U, 0, gamma=4)
bcPower(U, .5, jacobian.adjusted=TRUE, gamma=4)
basicPower(U, lambda = 0, gamma=4)
yjPower(U, 0)
V <- matrix(1:10, ncol=2)
bcPower(V, c(0, 2))
basicPower(V, c(0,1))