Create a lagged (or lead) predictor

`step_lag_matrix` 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 `default` argument. This method is faster than [step_lag()] and allows for negative values.

step_lag_matrix(recipe, ..., role = "lag_matrix", trained = FALSE,
  lag = 1, n_subset = 1, n_shift = 0, prefix = "lag_matrix_",
  default = NA, columns = NULL, skip = FALSE,
  id = rand_id("lag_matrix"))

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.
role

Defaults to "predictor"
trained

A logical to indicate if the quantities for preprocessing have been estimated.
lag

A vector of integers. They can be positive, negative or zero. Each specified column will be lagged for each value in the vector.
n_subset

subset every n_subset values
n_shift

shift the data n_shift values
prefix

A prefix for generated column names, default to "lag_".
default

Passed to dplyr::lag, determines what fills empty rows left by lagging (defaults to NA).
columns

A character string of variable names that will be populated (eventually) by the `terms` argument.
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.

Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any).

Details

The step assumes that the data are already _in the proper sequential order_ for lagging.

See also

[recipe()] [step_lag()] [prep.recipe()] [bake.recipe()] [step_naomit()]

Examples

data(transducer)

rec <- recipe(wl ~ .,
              data = transducer[1:1000, list(datetime, wl, baro)])

with_et <- rec %>%
  step_lag_matrix(baro, lag = -1:1) %>%
  step_naomit(everything()) %>%
  prep() %>%
  juice()