step_lag {recipes} | R Documentation |
Create a lagged predictor
Description
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 default
argument.
Usage
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")
)
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 variables
for this step. See |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
lag |
A vector of positive integers. Each specified column will be lagged for each value in the vector. |
prefix |
A prefix for generated column names, default to "lag_". |
default |
Passed to |
columns |
A character string of the selected variable names. This field
is a placeholder and will be populated once |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
Details
The step assumes that the data are already in the proper sequential order for lagging.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
and id
:
- terms
character, the selectors or variables selected
- id
character, id of this step
Case weights
The underlying operation does not allow for case weights.
See Also
Other row operation steps:
step_arrange()
,
step_filter()
,
step_impute_roll()
,
step_naomit()
,
step_sample()
,
step_shuffle()
,
step_slice()
Examples
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)