boxcox_transform: Box-Cox Transformations for Linear Model

In rettopnivek/arfpam: Assorted R Functions for Processing, Analysis, and Modeling

Box-Cox Transformations for Linear Model

Description

Wrapper function to the boxcox function; estimates the optimal parameter for the Box-Cox power transformation and then applies the appropriate power transformation to the outcome variable.

Usage

boxcox_transform(
  x,
  outcome = NULL,
  parameter_grid = seq(-3, 3, 0.01),
  output = TRUE
)

Arguments

x	Either A numeric vector of positive values; A data frame with an outcome variable and set of predictors for a linear regression.
outcome	An optional character string with the column name for the outcome variable in x. Otherwise, the first column in x is assumed to be the outcome variable.
parameter_grid	Vector specifying the grid of parameters to explore for maximum likelihood estimation.
output	Logical; if TRUE, returns the outcome variable following the power transformation.

Details

The Box-Cox power transformation for a vector of positive values x and parameter \lambda is:

f(x) = \frac{x^{\lambda} - 1}{\lambda}.

For \lambda = 0, one simply uses the log transform.

Value

Either the maximum likelihood estimate for the power transformation parameter or a vector for the outcome variable following the transformation.

Examples

# Simulate 100 values from the normal distribution
set.seed(3)
z <- rnorm(100)
# Transform the simulated values
x <- (z * .5 + 1)^(1 / .5)
# Maximum likelihood estimate for
# transformation parameter
boxcox_transform(x, output = FALSE)

# Histogram of x, transform of x, and
# original simulated values
layout(rbind(1, 2, 3))
hist(x)
hist(boxcox_transform(x))
hist(z)

rettopnivek/arfpam documentation built on Oct. 20, 2024, 7:24 p.m.