sqrtshift: Square-root shift 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 Square-root shift transformation. The transformation parameter can either be estimated using different estimation methods or given.

Usage

sqrtshift(object, lambda = "estim", method = "ml",
  lambdarange = NULL, 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 NULL. In this case the lambdarange is set to the range of the data. In case the lowest value is negative the absolute value of the lowest value plus 1 is the lower bound for the range.
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.

Examples

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

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

# Transform dependent variable using a maximum likelihood approach
sqrtshift(object = lm_cars, plotit = TRUE)

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