gpower: Gpower transformation for linear models

In trafo: Estimation, Comparison and Selection of Transformations

Description

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

Usage

gpower(object, lambda = "estim", method = "ml", lambdarange = c(-2,
  2), plotit = TRUE)

Arguments

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. Defaults to c(-2, 2).
plotit	logical. If TRUE, a plot that illustrates the optimal transformation parameter or the given transformation parameter is returned. Defaults to TRUE.

Value

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

References

Kelmansky DM, Martinez EJ, Leiva V (2013). A New Variance Stabilizing Transformation for Gene Expression Data Analysis. Statistical applications in genetics and molecular biology, 12(6), 653-666.

Examples

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

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

# Transform dependent variable using divergence minimization following 
# Kullback-Leibler
gpower(object = lm_cars, method = "div.kl", plotit = FALSE)

trafo documentation built on May 2, 2019, 2:13 p.m.