Create a lagged predictorSource:
step_lag() creates a specification of a recipe step that will add new
columns of lagged data. Lagged data will by default include NA values where
the lag was induced. These can be removed with
step_naomit(), or you may
specify an alternative filler value with the
step_lag( recipe, ..., role = "predictor", trained = FALSE, lag = 1, prefix = "lag_", default = NA, columns = NULL, keep_original_cols = TRUE, skip = FALSE, id = rand_id("lag") )
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step. See
selections()for more details.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A vector of positive integers. Each specified column will be lagged for each value in the vector.
A prefix for generated column names, default to "lag_".
dplyr::lag, determines what fills empty rows left by lagging (defaults to NA).
A character string of the selected variable names. This field is a placeholder and will be populated once
A logical to keep the original variables in the output. Defaults to
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()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 = TRUEas it may affect the computations for subsequent operations.
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 any existing operations.
tidy() this step, a tibble with column
terms (the columns that will be affected) is returned.
n <- 10 start <- as.Date("1999/01/01") end <- as.Date("1999/01/10") df <- data.frame( x = runif(n), index = 1:n, day = seq(start, end, by = "day") ) recipe(~., data = df) %>% step_lag(index, day, lag = 2:3) %>% prep(df) %>% bake(df) #> # A tibble: 10 × 7 #> x index day lag_2_index lag_3_index lag_2_day lag_3_day #> <dbl> <int> <date> <int> <int> <date> <date> #> 1 0.189 1 1999-01-01 NA NA NA NA #> 2 0.0579 2 1999-01-02 NA NA NA NA #> 3 0.0469 3 1999-01-03 1 NA 1999-01-01 NA #> 4 0.356 4 1999-01-04 2 1 1999-01-02 1999-01-01 #> 5 0.563 5 1999-01-05 3 2 1999-01-03 1999-01-02 #> 6 0.757 6 1999-01-06 4 3 1999-01-04 1999-01-03 #> 7 0.687 7 1999-01-07 5 4 1999-01-05 1999-01-04 #> 8 0.966 8 1999-01-08 6 5 1999-01-06 1999-01-05 #> 9 0.977 9 1999-01-09 7 6 1999-01-07 1999-01-06 #> 10 0.0903 10 1999-01-10 8 7 1999-01-08 1999-01-07