power.transform (version 1.0.1)

power_transform: Transform values

Description

power_transform transforms numeric values to normality.

Usage

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

Value

A vector of transformed values of x.

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.

See Also

find_transformation_parameters

Examples

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