step_impute_mean creates a specification of a recipe step that will
substitute missing values of numeric variables by the training set mean of
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
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 means. This is
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.
A logical. Should the step be skipped when the
recipe is baked by
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 existing steps (if any).
step_impute_mean estimates the variable means from the data used
training argument of
bake.recipe then applies the
new values to new data sets using these averages.
tidy() this step, a tibble with
terms (the selectors or variables selected) and
model (the mean
value) is returned.
recipes 0.1.16, this function name changed from
library(modeldata) data("credit_data") ## 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_mean(Income, Assets, Debt) imp_models <- prep(impute_rec, training = credit_tr) imputed_te <- bake(imp_models, new_data = credit_te, everything()) 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 x 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 #> # … with 4 more variables: Assets <int>, Debt <int>, Amount <int>, Price <int> tidy(impute_rec, number = 1) #> # A tibble: 3 x 3 #> terms model id #> <chr> <dbl> <chr> #> 1 Income NA impute_mean_Hlm2y #> 2 Assets NA impute_mean_Hlm2y #> 3 Debt NA impute_mean_Hlm2y tidy(imp_models, number = 1) #> # A tibble: 3 x 3 #> terms model id #> <chr> <int> <chr> #> 1 Income 142 impute_mean_Hlm2y #> 2 Assets 5378 impute_mean_Hlm2y #> 3 Debt 364 impute_mean_Hlm2y