step_impute_median {recipes} | R Documentation |
Impute numeric data using the median
Description
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.
Usage
step_impute_median(
recipe,
...,
role = NA,
trained = FALSE,
medians = NULL,
skip = FALSE,
id = rand_id("impute_median")
)
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 |
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. |
medians |
A named numeric vector of medians. This is |
skip |
A logical. Should the step be skipped when the recipe is baked by
|
id |
A character string that is unique to this step to identify it. |
Details
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()
.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
- terms
character, the selectors or variables selected
- value
numeric, the median value
- id
character, id of this step
Sparse data
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.
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_mode()
,
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))
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, ]
imputed_te[missing_examples, names(credit_te)]
tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)