View source: R/powerTransform.R
bcPower | R Documentation |
Transform the elements of a vector or columns of a matrix using, the Box-Cox, Box-Cox with negatives allowed, Yeo-Johnson, or simple power transformations.
bcPower(U, lambda, jacobian.adjusted=FALSE, gamma=NULL)
bcnPower(U, lambda, jacobian.adjusted = FALSE, gamma)
bcnPowerInverse(z, lambda, gamma)
yjPower(U, lambda, jacobian.adjusted = FALSE)
basicPower(U,lambda, gamma=NULL)
U |
A vector, matrix or data.frame of values to be transformed |
lambda |
Power transformation parameter with one element for each
column of U, usuallly in the range from |
jacobian.adjusted |
If |
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. For the bcnPower, Box-cox power with negatives allowed, see the details below. |
z |
a numeric vector the result of a call to |
.
The Box-Cox
family of scaled power transformations
equals (x^{\lambda}-1)/\lambda
for \lambda \neq 0
, and
\log(x)
if \lambda =0
. The bcPower
function computes the scaled power transformation of
x = U + \gamma
, where \gamma
is set by the user so U+\gamma
is strictly positive for these
transformations to make sense.
The Box-Cox family with negatives allowed was proposed by Hawkins and Weisberg (2017). It is the Box-Cox power transformation of
z = .5 (U + \sqrt{U^2 + \gamma^2)})
where for this family \gamma
is either user selected or is estimated. gamma
must be positive if U
includes negative values and non-negative otherwise, ensuring that z
is always positive. The bcnPower transformations behave similarly to the bcPower transformations, and introduce less bias than is introduced by setting the parameter \gamma
to be non-zero in the Box-Cox family.
The function bcnPowerInverse
computes the inverse of the bcnPower
function, so U = bcnPowerInverse(bcnPower(U, lambda=lam, jacobian.adjusted=FALSE, gamma=gam), lambda=lam, gamma=gam)
is true for any permitted value of gam
and lam
.
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.
The basic power transformation returns U^{\lambda}
if
\lambda
is not 0, and \log(\lambda)
otherwise for U
strictly positive.
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. Jacobian adjustment facilitates computing the Box-Cox estimates of the transformation parameters.
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. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Hawkins, D. and Weisberg, S. (2017) Combining the Box-Cox Power and Generalized Log Transformations to Accomodate Nonpositive Responses In Linear and Mixed-Effects Linear Models South African Statistics Journal, 51, 317-328.
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
, testTransform
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)
bcnPower(U, 0, gamma=2)
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))
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