power_transform: Transform values

In power.transform: Location and Scale Invariant Power Transformations

Transform values

Description

power_transform transforms numeric values to normality.

Usage

power_transform(x, transformer = NULL, oob_action = "na", ...)

Arguments

x	A vector with numeric values that should be transformed to normality.
transformer	A transformer object created using find_transformation_parameters. If NULL, a transformer is generated internally.
oob_action	Action that should be taken when out-of-bounds values are encountered in x. This can for example be 0 or negative values for Box-Cox transformations. na (default): replaces out-of-bounds values by NA values. valid: replaces out-of-bounds values by the closest valid boundary values. This argument has no effect for Yeo-Johnson transformations.
...	Arguments passed on to find_transformation_parameters method One of the following methods for power transformation: box_cox: Transformation using the Box-Cox transformation (Box and Cox, 1964). The Box-Cox transformation requires that all data are strictly positive. Features that contain zero or negative values cannot be transformed using this transformation. In their work, Box and Cox define a shifted variant. We use this variant to shift values to a strictly positive range, when negative values are present. The Box-Cox transformation relies on a single parameter lambda, which is estimated through maximisation of the log-likelihood function corresponding to a normal distribution. yeo_johnson:Transformation using the Yeo-Johnson transformation (Yeo and Johnson, 2000). Unlike the Box-Cox transformation, the Yeo-Johnson transformation allows for negative and positive values. Like the Box-Cox transformation, this transformation relies on a single parameter lambda, which is estimated through maximisation of the log-likelihood function corresponding to a normal distribution. none: A fall-back method that will not transform values. robust Flag for using a robust version of Box-Cox or Yeo-Johnson transformation, as defined by Raymaekers and Rousseeuw (2021). This version is less sensitive in the presence outliers. invariant Flag for using a version of Box-Cox or Yeo-Johnson transformation that simultaneously optimises location and scale in addition to the lambda parameter. lambda Single lambda value, or range of lambda values that should be considered. Default: c(4.0, 6.0). Can be NULL to force optimisation without a constraint in lambda values. empirical_gof_normality_p_value Significance value for the empirical goodness-of-fit test for central normality. The p-value is computed through the assess_transformation function. By setting this parameter to a numeric value other than NULL, the transformation will be rejected when the p-value of the test is below the significance value.

Value

A vector of transformed values of x.

See Also

find_transformation_parameters

Examples

x <- exp(stats::rnorm(1000))
y <- power_transform(
  x = x,
  method = "box_cox")

power.transform documentation built on April 12, 2025, 5:08 p.m.