step_interact {recipes} | R Documentation

## Create Interaction Variables

### Description

`step_interact` creates a specification of a recipe step that will create new columns that are interaction terms between two or more variables.

### Usage

    step_interact(recipe, terms, role = "predictor", trained = FALSE,
      objects = NULL, sep = "_x_", skip = FALSE,
      id = rand_id("interact"))

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

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`terms` | A traditional R formula that contains interaction terms. This can include `.` and selectors.
`role` | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created from the original variables will be used as predictors in a model.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`objects` | A list of `terms` objects for each individual interaction.
`sep` | A character value used to delineate variables in an interaction (e.g. `var1_x_var2` instead of the more traditional `var1:var2`).
`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_interact` object
`...` | 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.

### Details

`step_interact` can create interactions between variables. It is primarily intended for numeric data; categorical variables should probably be converted to dummy variables using `step_dummy()` prior to being used for interactions.

Unlike other step functions, the `terms` argument should be a traditional R model formula but should contain no inline functions (e.g. `log`). For example, for predictors `A`, `B`, and `C`, a formula such as `~A:B:C` can be used to make a three way interaction between the variables. If the formula contains terms other than interactions (e.g. `(A+B+C)^3`) only the interaction terms are retained for the design matrix.

The separator between the variables defaults to "`_x_`" so that the three way interaction shown previously would generate a column named `A_x_B_x_C`. This can be changed using the `sep` argument.

When dummy variables are created and are used in interactions, selectors can help specify the interactions succinctly. For example, suppose a factor column `X` gets converted to dummy variables `x_2`, `x_3`, ..., `x_6` using `step_dummy()`. If you wanted an interaction with numeric column `z`, you could create a set of specific interaction effects (e.g. `x_2:z + x_3:z` and so on) or you could use `starts_with("z_"):z`. When `prep()` evaluates this step, `starts_with("z_")` resolves to `(x_2 + x_3 + x_4 + x_5 + x6)` so that the formula is now `(x_2 + x_3 + x_4 + x_5 + x6):z` and all two-way interactions are created.

### 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` which is the interaction effects.

### Examples

    data(biomass)

    biomass_tr <- biomass[biomass$dataset == "Training",]
    biomass_te <- biomass[biomass$dataset == "Testing",]

    rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
                  data = biomass_tr)

    int_mod_1 <- rec %>%
      step_interact(terms = ~ carbon:hydrogen)

    int_mod_2 <- rec %>%
      step_interact(terms = ~ (matches("gen$") + sulfur)^2)

    int_mod_1 <- prep(int_mod_1, training = biomass_tr)
    int_mod_2 <- prep(int_mod_2, training = biomass_tr)

    dat_1 <- bake(int_mod_1, biomass_te)
    dat_2 <- bake(int_mod_2, biomass_te)

    names(dat_1)
    names(dat_2)

    tidy(int_mod_1, number = 1)
    tidy(int_mod_2, number = 1)

[Package recipes version 0.1.5 Index]