step_distributed_lag: Distributed lag response
In jkennel/waterlevel: Well Water Level Analysis Tools

Description Usage Arguments Details Value See Also Examples

'step_distributed_lag' creates a *specification* of a recipe step that are distributed lag versions of a particular variable. Uses FFT for fast calculation with a large maximum lag and many observations

step_distributed_lag(recipe, ..., role = "distributed_lag",
  trained = FALSE, knots = 1, spline_fun = splines::ns,
  n_subset = 1, n_shift = 0, prefix = "distributed_lag_",
  default = NA, columns = NULL, skip = FALSE,
  id = rand_id("distributed_lag"))

`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`	Defaults to "distributed_lag"
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`knots`	specific knots for the lagging process
`spline_fun`	spline function to use i.e. splines::ns, splines::bs
`n_subset`	for distributed_lag parallel
`n_shift`	for distributed_lag parallel
`prefix`	A prefix for generated column names, default to "distributed_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.

'step_distributed_lag' calculates the earthtide response and then lags (leads) the terms.

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' which is the columns that will be affected and 'holiday'.

[step_lag_matrix()] [recipe()] [prep.recipe()] [bake.recipe()]

data(transducer)

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

with_et <- rec %>%
  step_distributed_lag(baro, knots = c(0, 10, 100)) %>%
  step_naomit(everything()) %>% 
  prep() %>%
  juice()