car (version 2.1-6)

bcPower: Box-Cox, Box-Cox with Negatives Allowed, Yeo-Johnson and Basic Power Transformations

Description

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.

Usage

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

Arguments

U

A vector, matrix or data.frame of values to be transformed

lambda

Power transformation parameter with one element for eacul column of U, usuallly in the range from \(-2\) to \(2\), or if U

jacobian.adjusted

If TRUE, the transformation is normalized to have Jacobian equal to one. The default FALSE is almost always appropriate

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.

Value

Returns a vector or matrix of transformed values.

Details

The Box-Cox family of scaled power transformations equals \(((U + \gamma)^{\lambda}-1)/\lambda\) for \(\lambda \neq 0\), 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.

The Box-Cox family with negatives allowed was proposed by Hawkins and Weisberg (2017). It is the Box-Cox power transformation of \(z = .5 * (y + (y^2 + \gamma^2)^{1/2})\), where \(\gamma\) is strictly positive if \(y\) includes negative values and non-negative otherwise. The value of \(z\) is always positive. The bcnPower transformations behave very similarly to the bcPower transformations, including much less bias than is introduced by setting the parameter \(\gamma\) to be non-zero in the Box-Cox 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.

The basic power transformation returns \(U^{\lambda}\) if \(\lambda\) is not zero, 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.

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

References

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

Hawkins, D. and Weisberg, S. (2017) Combining the Box-Cox Power and Generalized Log Transformations to Accomodate Negative Responses In Linear and Mixed-Effects Linear Models, 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.

See Also

powerTransform, testTransform

Examples

# NOT RUN {
U <- c(NA, (-3:3))
# }
# NOT RUN {
bcPower(U, 0)
# }
# NOT RUN {
  # 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))
# }