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_", skip = FALSE, id = rand_id("interact") ) # S3 method for step_interact tidy(x, ...)
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
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.
A logical to indicate if the quantities for preprocessing have been estimated.
A list of
A character value used to delineate variables in an
A logical. Should the step be skipped when the
recipe is baked by
A character string that is unique to this step to identify it.
One or more selector functions to choose which
variables are affected by the step. See
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.
step_interact can create interactions between
variables. It is primarily intended for numeric data;
categorical variables should probably be converted to dummy
step_dummy() prior to being used for
Unlike other step functions, the
terms argument should
be a traditional R model formula but should contain no inline
log). For example, for predictors
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
(A+B+C)^3) only the interaction terms
are retained for the design matrix.
The separator between the variables defaults to "
that the three way interaction shown previously would generate a
A_x_B_x_C. This can be changed using the
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
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
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.
library(modeldata) 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)#>  "carbon" "hydrogen" "oxygen" #>  "nitrogen" "sulfur" "HHV" #>  "carbon_x_hydrogen"names(dat_2)#>  "carbon" "hydrogen" "oxygen" #>  "nitrogen" "sulfur" "HHV" #>  "hydrogen_x_oxygen" "hydrogen_x_nitrogen" "hydrogen_x_sulfur" #>  "oxygen_x_nitrogen" "oxygen_x_sulfur" "nitrogen_x_sulfur"tidy(int_mod_1, number = 1)#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 carbon:hydrogen interact_q3sI4tidy(int_mod_2, number = 1)#> # A tibble: 6 x 2 #> terms id #> <chr> <chr> #> 1 hydrogen:oxygen interact_xjtSG #> 2 hydrogen:nitrogen interact_xjtSG #> 3 hydrogen:sulfur interact_xjtSG #> 4 oxygen:nitrogen interact_xjtSG #> 5 oxygen:sulfur interact_xjtSG #> 6 nitrogen:sulfur interact_xjtSG