#' Distributed lag response
#'
#' `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
#'
#' @inheritParams recipes::step_lag
#' @inheritParams distributed_lag
#' @param ... 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.
#' @param role Defaults to "distributed_lag"
#' @param prefix A prefix for generated column names, default to
#' "distributed_lag_".
#' @return 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`.
#' @keywords datagen
#' @concept generate a distributed lag response
#' @export
#' @details `step_distributed_lag` calculates the earthtide response and then
#' lags (leads) the terms.
#' @examples
#' 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()
#'
#' @seealso [step_lag_matrix()] [recipe()]
#' [prep.recipe()] [bake.recipe()]
#' @importFrom recipes add_step step terms_select ellipse_check rand_id
step_distributed_lag <-
function(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")) {
add_step(
recipe,
step_distributed_lag_new(
terms = ellipse_check(...),
role = role,
trained = trained,
knots = knots,
spline_fun = spline_fun,
n_subset = n_subset,
n_shift = n_shift,
default = default,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
)
}
step_distributed_lag_new <-
function(terms, role, trained, knots, spline_fun, n_subset, n_shift, default, prefix, columns, skip, id) {
step(
subclass = "distributed_lag",
terms = terms,
role = role,
trained = trained,
knots = knots,
spline_fun = spline_fun,
n_subset = n_subset,
n_shift = n_shift,
default = default,
prefix = prefix,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_distributed_lag <- function(x, training, info = NULL, ...) {
step_distributed_lag_new(
terms = x$terms,
role = x$role,
trained = TRUE,
knots = x$knots,
spline_fun = x$spline_fun,
n_subset = x$n_subset,
n_shift = x$n_shift,
default = x$default,
prefix = x$prefix,
columns = terms_select(x$terms, info = info),
skip = x$skip,
id = x$id
)
}
#' @importFrom dplyr bind_cols
#' @importFrom tibble as_tibble
#' @importFrom recipes bake prep
#' @export
bake.step_distributed_lag <- function(object, new_data, ...) {
bind_cols(new_data[seq(object$n_shift+1, nrow(new_data), by = object$n_subset),],
as_tibble(distributed_lag(new_data[[object$columns]],
object$knots,
object$spline_fun,
object$columns,
object$n_subset,
object$n_shift)))
}
#' @importFrom recipes printer
print.step_distributed_lag <-
function(x, width = max(20, options()$width - 30), ...) {
cat("distributed_lag ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
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