step_impute_mode() creates a specification of a recipe step that will
substitute missing values of nominal variables by the training set mode of
those variables.
Usage
step_impute_mode(
recipe,
...,
role = NA,
trained = FALSE,
modes = NULL,
ptype = NULL,
skip = FALSE,
id = rand_id("impute_mode")
)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 variables for this step. See
selections()for more details.- 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.
- modes
A named character vector of modes. This is
NULLuntil computed byprep().- ptype
A data frame prototype to cast new data sets to. This is commonly a 0-row slice of the training set.
- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
Details
step_impute_mode estimates the variable modes
from the data used in the training argument of
prep.recipe. bake.recipe then applies the new
values to new data sets using these values. If the training set
data has more than one mode, one is selected at random.
As of recipes 0.1.16, this function name changed from step_modeimpute()
to step_impute_mode().
Tidying
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
- terms
character, the selectors or variables selected
- value
character, the mode value
- id
character, id of this step
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org.
See also
Other imputation steps:
step_impute_bag(),
step_impute_knn(),
step_impute_linear(),
step_impute_lower(),
step_impute_mean(),
step_impute_median(),
step_impute_roll()
Examples
data("credit_data", package = "modeldata")
## missing data per column
vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
#> Status Seniority Home Time Age
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000
#> Marital Records Job Expenses Income
#> 0.0002245173 0.0000000000 0.0004490346 0.0000000000 0.0855410867
#> Assets Debt Amount Price
#> 0.0105523125 0.0040413112 0.0000000000 0.0000000000
set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)
credit_tr <- credit_data[in_training, ]
credit_te <- credit_data[-in_training, ]
missing_examples <- c(14, 394, 565)
rec <- recipe(Price ~ ., data = credit_tr)
impute_rec <- rec %>%
step_impute_mode(Status, Home, Marital)
imp_models <- prep(impute_rec, training = credit_tr)
imputed_te <- bake(imp_models, new_data = credit_te)
table(credit_te$Home, imputed_te$Home, useNA = "always")
#>
#> ignore other owner parents priv rent <NA>
#> ignore 13 0 0 0 0 0 0
#> other 0 176 0 0 0 0 0
#> owner 0 0 1171 0 0 0 0
#> parents 0 0 0 436 0 0 0
#> priv 0 0 0 0 135 0 0
#> rent 0 0 0 0 0 519 0
#> <NA> 0 0 4 0 0 0 0
tidy(impute_rec, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 Status NA impute_mode_Hlm2y
#> 2 Home NA impute_mode_Hlm2y
#> 3 Marital NA impute_mode_Hlm2y
tidy(imp_models, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 Status good impute_mode_Hlm2y
#> 2 Home owner impute_mode_Hlm2y
#> 3 Marital married impute_mode_Hlm2y