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,
  skip = FALSE,
  id = rand_id("meanimpute")
)

# S3 method for step_meanimpute
tidy(x, ...)

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 which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.

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 NULL until computed by prep.recipe(). Note that, if the original data are integers, the mean will be converted to an integer to maintain the same data type.

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.

skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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

id

A character string that is unique to this step to identify it.

x

A step_meanimpute object.

Value

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).

Details

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.

Examples

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_meanimpute(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 part… 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 meanimpute_Hlm2y #> 2 Assets NA meanimpute_Hlm2y #> 3 Debt NA meanimpute_Hlm2y
tidy(imp_models, number = 1)
#> # A tibble: 3 x 3 #> terms model id #> <chr> <int> <chr> #> 1 Income 142 meanimpute_Hlm2y #> 2 Assets 5378 meanimpute_Hlm2y #> 3 Debt 364 meanimpute_Hlm2y