| bcPower {car} | R Documentation |
Transform the elements of a vector using, the Box-Cox, Yeo-Johnson, or simple power transformations.
bcPower(U, lambda, jacobian.adjusted = FALSE) yjPower(U, lambda, jacobian.adjusted = FALSE) basicPower(U,lambda)
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 |
jacobian.adjusted |
If |
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 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.
If jacobian.adjusted is TRUE, then the scaled transformations are divided by the
Jacobian, which is a function of the geometric mean of U.
The basic power transformation returns U^{λ} if λ is not zero, and \log(λ) otherwise.
Missing values are permitted, and return NA where ever Uis equal to NA.
Returns a vector or matrix of transformed values.
Sanford Weisberg, <sandy@stat.umn.edu>
Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.
Weisberg, S. (2005) Applied Linear Regression, Third Edition. 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.
U <- c(NA, (-3:3)) ## Not run: bcPower(U, 0) # produces an error as U has negative values bcPower(U+4,0) bcPower(U+4, .5, jacobian.adjusted=TRUE) yjPower(U, 0) yjPower(U+3, .5, jacobian.adjusted=TRUE) V <- matrix(1:10, ncol=2) bcPower(V, c(0,1)) #basicPower(V, c(0,1))