R: Box-Cox Transformation for Non-Negative Data

step_BoxCox {recipes} | R Documentation

## Box-Cox Transformation for Non-Negative Data

### Description

`step_BoxCox` creates a specification of a recipe step that will transform data using a simple Box-Cox transformation.

### Usage

    step_BoxCox(recipe, ..., role = NA, trained = FALSE, lambdas = NULL,
      limits = c(-5, 5), nunique = 5, skip = FALSE)

    ## S3 method for class 'step_BoxCox'
    tidy(x, ...)

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | One or more selector functions to choose which variables are affected by the step. See `selections()` for more details. For the `tidy` method, these are not currently used.
`role` | Not used by this step since no new variables are created.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`lambdas` | A numeric vector of transformation values. This is `NULL` until computed by `prep.recipe()`.
`limits` | A length 2 numeric vector defining the range to compute the transformation parameter lambda.
`nunique` | An integer where data that have less possible values will not be evaluate for a transformation.
`skip` | A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations
`x` | A `step_BoxCox` object.

### Details

The Box-Cox transformation, which requires a strictly positive variable, can be used to rescale a variable to be more similar to a normal distribution. In this package, the partial log-likelihood function is directly optimized within a reasonable set of transformation values (which can be changed by the user).

This transformation is typically done on the outcome variable using the residuals for a statistical model (such as ordinary least squares). Here, a simple null model (intercept only) is used to apply the transformation to the predictor variables individually. This can have the effect of making the variable distributions more symmetric.

If the transformation parameters are estimated to be very closed to the bounds, or if the optimization fails, a value of `NA` is used and no transformation is applied.

### Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any). For the `tidy` method, a tibble with columns `terms` (the selectors or variables selected) and `value` (the lambda estimate).

### References

Sakia, R. M. (1992). The Box-Cox transformation technique: A review. The Statistician, 169-178..

### See Also

`step_YeoJohnson()` `recipe()` `prep.recipe()` `bake.recipe()`

### Examples

    rec <- recipe(~ ., data = as.data.frame(state.x77))

    bc_trans <- step_BoxCox(rec, all_numeric())

    bc_estimates <- prep(bc_trans, training = as.data.frame(state.x77))

    bc_data <- bake(bc_estimates, as.data.frame(state.x77))

    plot(density(state.x77[, "Illiteracy"]), main = "before")
    plot(density(bc_data$Illiteracy), main = "after")

    tidy(bc_trans, number = 1)
    tidy(bc_estimates, number = 1)

[Package recipes version 0.1.2 Index]