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 |
jacobian.adjusted |
If |
The Box-Cox family of scaled power transformations
equals (U^{\lambda}-1)/\lambda
for \lambda \neq 0
, 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^{\lambda}
if \lambda
is not zero, and \log(\lambda)
otherwise.
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.
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.
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))