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)
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. |
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).
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)]