step_meanimpute {recipes} | R Documentation |
step_meanimpute
creates a specification of a
recipe step that will substitute missing values of numeric
variables by the training set mean of those variables.
step_meanimpute(recipe, ..., role = NA, trained = FALSE, means = NULL, trim = 0) ## S3 method for class 'step_meanimpute' tidy(x, ...)
recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose which
variables are affected by the 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. |
means |
A named numeric vector of means. This is
|
trim |
The fraction (0 to 0.5) of observations to be trimmed from each end of the variables before the mean is computed. Values of trim outside that range are taken as the nearest endpoint. |
x |
A |
step_meanimpute
estimates the variable means
from the data used in the training
argument of
prep.recipe
. bake.recipe
then applies the new
values to new data sets using these averages.
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
(the
selectors or variables selected) and model
(the mean
value).
data("credit_data") ## 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_meanimpute(Income, Assets, Debt) imp_models <- prep(impute_rec, training = credit_tr) imputed_te <- bake(imp_models, newdata = credit_te, everything()) credit_te[missing_examples,] imputed_te[missing_examples, names(credit_te)] tidy(impute_rec, number = 1) tidy(imp_models, number = 1)