yeo.johnson {VGAM} | R Documentation |
Yeo-Johnson Transformation
Description
Computes the Yeo-Johnson transformation, which is a normalizing transformation.
Usage
yeo.johnson(y, lambda, derivative = 0,
epsilon = sqrt(.Machine$double.eps), inverse = FALSE)
Arguments
y |
Numeric, a vector or matrix. |
lambda |
Numeric. It is recycled to the same length as
|
derivative |
Non-negative integer. The default is
the ordinary function evaluation, otherwise the derivative
with respect to |
epsilon |
Numeric and positive value. The tolerance given
to values of |
inverse |
Logical. Return the inverse transformation? |
Details
The Yeo-Johnson transformation can be thought of as an extension of the Box-Cox transformation. It handles both positive and negative values, whereas the Box-Cox transformation only handles positive values. Both can be used to transform the data so as to improve normality. They can be used to perform LMS quantile regression.
Value
The Yeo-Johnson transformation or its inverse, or its
derivatives with respect to lambda
, of y
.
Note
If inverse = TRUE
then the
argument derivative = 0
is required.
Author(s)
Thomas W. Yee
References
Yeo, I.-K. and Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954–959.
Yee, T. W. (2004). Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.
See Also
Examples
y <- seq(-4, 4, len = (nn <- 200))
ltry <- c(0, 0.5, 1, 1.5, 2) # Try these values of lambda
lltry <- length(ltry)
psi <- matrix(NA_real_, nn, lltry)
for (ii in 1:lltry)
psi[, ii] <- yeo.johnson(y, lambda = ltry[ii])
## Not run:
matplot(y, psi, type = "l", ylim = c(-4, 4), lwd = 2,
lty = 1:lltry, col = 1:lltry, las = 1,
ylab = "Yeo-Johnson transformation",
main = "Yeo-Johnson transformation with some lambda values")
abline(v = 0, h = 0)
legend(x = 1, y = -0.5, lty = 1:lltry, legend = as.character(ltry),
lwd = 2, col = 1:lltry)
## End(Not run)