step_impute_median() creates a specification of a recipe step that will
substitute missing values of numeric variables by the training set median of
those variables.
step_impute_median(
recipe,
...,
role = NA,
trained = FALSE,
medians = NULL,
skip = FALSE,
id = rand_id("impute_median")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A named numeric vector of medians. This is NULL until
computed by prep(). Note that, if the original data are integers, the
median will be converted to an integer to maintain the same data type.
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_impute_median() estimates the variable medians from the data used in
the training argument of prep(). bake() then applies the new values to
new data sets using these medians.
As of recipes 0.1.16, this function name changed from step_medianimpute()
to step_impute_median().
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
character, the selectors or variables selected
numeric, the median value
character, id of this step
This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.
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.
Other imputation steps:
step_impute_bag(),
step_impute_knn(),
step_impute_linear(),
step_impute_lower(),
step_impute_mean(),
step_impute_mode(),
step_impute_roll()
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 Marital
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 0.0002245173
#> Records Job Expenses Income Assets Debt
#> 0.0000000000 0.0004490346 0.0000000000 0.0855410867 0.0105523125 0.0040413112
#> Amount Price
#> 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_median(Income, Assets, Debt)
imp_models <- prep(impute_rec, training = credit_tr)
imputed_te <- bake(imp_models, new_data = credit_te)
credit_te[missing_examples, ]
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> 28 good 15 owner 36 43 married no fixed 75 251
#> 688 good 2 rent 60 32 married no partime 87 115
#> 1002 good 21 rent 60 39 married no fixed 124 191
#> Assets Debt Amount Price
#> 28 4000 0 1800 2557
#> 688 2000 0 1250 1517
#> 1002 2000 0 2000 2536
imputed_te[missing_examples, names(credit_te)]
#> # A tibble: 3 × 14
#> Status Seniority Home Time Age Marital Records Job Expenses Income
#> <fct> <int> <fct> <int> <int> <fct> <fct> <fct> <int> <int>
#> 1 good 15 owner 36 43 married no fixed 75 251
#> 2 good 2 rent 60 32 married no partime 87 115
#> 3 good 21 rent 60 39 married no fixed 124 191
#> # ℹ 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int>
tidy(impute_rec, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 Income NA impute_median_Hlm2y
#> 2 Assets NA impute_median_Hlm2y
#> 3 Debt NA impute_median_Hlm2y
tidy(imp_models, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 Income 125 impute_median_Hlm2y
#> 2 Assets 3000 impute_median_Hlm2y
#> 3 Debt 0 impute_median_Hlm2y