step_interact() creates a specification of a recipe step that will create
new columns that are interaction terms between two or more variables.
step_interact(
recipe,
terms,
role = "predictor",
trained = FALSE,
objects = NULL,
sep = "_x_",
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("interact")
)A recipe object. The step will be added to the sequence of operations for this recipe.
A traditional R formula that contains interaction terms. This
can include . and selectors. See selections() for more details, and
consider using tidyselect::starts_with() when dummy variables have been
created.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A list of terms objects for each individual interaction.
A character value used to delineate variables in an interaction
(e.g. var1_x_var2 instead of the more traditional var1:var2).
A logical to keep the original variables in the
output. Defaults to TRUE.
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when prep() 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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
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("x_"):z. When prep() evaluates this
step, starts_with("x_") resolves to (x_2 + x_3 + x_4 + x_5 + x_6) so that
the formula is now (x_2 + x_3 + x_4 + x_5 + x_6):z and all two-way
interactions are created.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
The underlying operation does not allow for case weights.
data(penguins, package = "modeldata")
penguins <- penguins |> na.omit()
rec <- recipe(flipper_length_mm ~ ., data = penguins)
int_mod_1 <- rec |>
step_interact(terms = ~ bill_depth_mm:bill_length_mm)
# specify all dummy variables succinctly with `starts_with()`
int_mod_2 <- rec |>
step_dummy(sex, species, island) |>
step_interact(terms = ~ body_mass_g:starts_with("species"))
int_mod_1 <- prep(int_mod_1, training = penguins)
int_mod_2 <- prep(int_mod_2, training = penguins)
dat_1 <- bake(int_mod_1, penguins)
dat_2 <- bake(int_mod_2, penguins)
names(dat_1)
#> [1] "species" "island"
#> [3] "bill_length_mm" "bill_depth_mm"
#> [5] "body_mass_g" "sex"
#> [7] "flipper_length_mm" "bill_depth_mm_x_bill_length_mm"
names(dat_2)
#> [1] "bill_length_mm" "bill_depth_mm"
#> [3] "body_mass_g" "flipper_length_mm"
#> [5] "sex_male" "species_Chinstrap"
#> [7] "species_Gentoo" "island_Dream"
#> [9] "island_Torgersen" "body_mass_g_x_species_Chinstrap"
#> [11] "body_mass_g_x_species_Gentoo"
tidy(int_mod_1, number = 1)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 bill_depth_mm:bill_length_mm interact_IUB7W
tidy(int_mod_2, number = 2)
#> # A tibble: 2 × 2
#> terms id
#> <chr> <chr>
#> 1 body_mass_g:species_Chinstrap interact_kM5w7
#> 2 body_mass_g:species_Gentoo interact_kM5w7