`step_BoxCox`

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

step_BoxCox( recipe, ..., role = NA, trained = FALSE, lambdas = NULL, limits = c(-5, 5), num_unique = 5, skip = FALSE, id = rand_id("BoxCox") ) # S3 method for step_BoxCox tidy(x, ...)

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 |

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 |

limits | A length 2 numeric vector defining the range to compute the transformation parameter lambda. |

num_unique | An integer where data that have less possible values will not be evaluated for a transformation. |

skip | A logical. Should the step be skipped when the
recipe is baked by |

id | A character string that is unique to this step to identify it. |

x | A |

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).

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.

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

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")#> # A tibble: 1 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 all_numeric() NA BoxCox_gWfAl#> # A tibble: 7 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 Population 0.000966 BoxCox_gWfAl #> 2 Income 0.524 BoxCox_gWfAl #> 3 Illiteracy -0.379 BoxCox_gWfAl #> 4 Life Exp 4.59 BoxCox_gWfAl #> 5 Murder 0.606 BoxCox_gWfAl #> 6 HS Grad 1.92 BoxCox_gWfAl #> 7 Area 0.250 BoxCox_gWfAl