`step_hyperbolic`

creates a *specification* of a
recipe step that will transform data using a hyperbolic
function.

step_hyperbolic(
recipe,
...,
role = NA,
trained = FALSE,
func = "sin",
inverse = TRUE,
columns = NULL,
skip = FALSE,
id = rand_id("hyperbolic")
)
# S3 method for step_hyperbolic
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. |

func |
A character value for the function. Valid values
are "sin", "cos", or "tan". |

inverse |
A logical: should the inverse function be used? |

columns |
A character string of variable names that will
be populated (eventually) by the `terms` argument. |

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 |

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

x |
A `step_hyperbolic` object. |

## 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
columns that will be affected), `inverse`

, and `func`

.

## See also

## Examples

#> # A tibble: 1 x 4
#> terms inverse func id
#> <chr> <lgl> <chr> <chr>
#> 1 all_predictors() FALSE cos hyperbolic_IhS7o

#> # A tibble: 2 x 4
#> terms inverse func id
#> <chr> <lgl> <chr> <chr>
#> 1 V1 FALSE cos hyperbolic_IhS7o
#> 2 V2 FALSE cos hyperbolic_IhS7o