step_rollimpute creates a specification of a
recipe step that will substitute missing values of numeric
variables by the measure of location (e.g. median) within a moving window.
step_rollimpute( recipe, ..., role = NA, trained = FALSE, columns = NULL, statistic = median, window = 5, skip = FALSE, id = rand_id("rollimpute") ) # S3 method for step_rollimpute tidy(x, ...)
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 columns. This is
A function with a single argument for the data to compute the imputed value. Only complete values will be passed to the function and it should return a double precision value.
The size of the window around a point to be imputed. Should be an odd integer greater than one. See Details below for a discussion of points at the ends of the series.
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). For the
tidy method, a tibble with columns
selectors or variables selected) and
window (the window size).
On the tails, the window is shifted towards the ends.
For example, for a 5-point window, the windows for the first
four points are
1:5, and then
When missing data are in the window, they are not passed to the function. If all of the data in the window are missing, a missing value is returned.
The statistics are calculated on the training set values before imputation. This means that if previous data within the window are missing, their imputed values are not included in the window data used for imputation. In other words, each imputation does not know anything about previous imputations in the series prior to the current point.
library(lubridate) set.seed(145) example_data <- data.frame( day = ymd("2012-06-07") + days(1:12), x1 = round(runif(12), 2), x2 = round(runif(12), 2), x3 = round(runif(12), 2) ) example_data$x1[c(1, 5, 6)] <- NA example_data$x2[c(1:4, 10)] <- NA library(recipes) seven_pt <- recipe(~ . , data = example_data) %>% update_role(day, new_role = "time_index") %>% step_rollimpute(all_predictors(), window = 7) %>% prep(training = example_data) # The training set: bake(seven_pt, new_data = NULL)#> # A tibble: 12 x 4 #> day x1 x2 x3 #> <date> <dbl> <dbl> <dbl> #> 1 2012-06-08 0.89 0.79 0.580 #> 2 2012-06-09 0.53 0.79 0.45 #> 3 2012-06-10 0.86 0.79 0.67 #> 4 2012-06-11 0.92 0.79 0.05 #> 5 2012-06-12 0.86 0.88 0.27 #> 6 2012-06-13 0.86 0.6 0.13 #> 7 2012-06-14 0.97 0.79 0.67 #> 8 2012-06-15 0.85 0.27 0.16 #> 9 2012-06-16 0.86 0.11 0.36 #> 10 2012-06-17 0.96 0.435 0.09 #> 11 2012-06-18 0.91 0.16 0.76 #> 12 2012-06-19 0.07 0.86 0.74