dual: Dual transformation for linear models
In trafo: Estimation, Comparison and Selection of Transformations

Description Usage Arguments Value References Examples

The function transforms the dependent variable of a linear model using the Dual transformation. The transformation parameter can either be estimated using different estimation methods or given.

1 2	dual(object, lambda = "estim", method = "ml", lambdarange = c(0, 2), plotit = TRUE)

`object`	an object of type lm.
`lambda`	either a character named "estim" if the optimal transformation parameter should be estimated or a numeric value determining a given value for the transformation parameter. Defaults to "estim".
`method`	a character string. Different estimation methods can be used for the estimation of the optimal transformation parameter: (i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"), (iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by Kullback-Leibler ("div.kl"). Defaults to "ml".
`lambdarange`	a numeric vector with two elements defining an interval that is used for the estimation of the optimal transformation parameter. The Dual transformation is not defined for negative values of lambda. Defaults to `c(0, 2)`.
`plotit`	logical. If `TRUE`, a plot that illustrates the optimal transformation parameter or the given transformation parameter is returned. Defaults to `TRUE`.

An object of class trafo. Methods such as as.data.frame.trafo and print.trafo can be used for this class.

Yang Z (2006). A modified family of power transformations. Economics Letters, 92(1), 14-19.

# Load data
data("cars", package = "datasets")

# Fit linear model
lm_cars <- lm(dist ~ speed, data = cars)

# Transform dependent variable using divergence minimization following
# Cramer-von-Mises
dual(object = lm_cars, method = "div.cvm", plotit = TRUE)