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#' Inverse Logit Transformation
#'
#' `step_invlogit` creates a *specification* of a recipe
#' step that will transform the data from real values to be between
#' zero and one.
#'
#' @inheritParams step_center
#' @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 Not used by this step since no new variables are
#' created.
#' @param columns A character string of variable names that will
#' be populated (eventually) by the `terms` argument.
#' @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.
#' @keywords datagen
#' @concept preprocessing
#' @concept transformation_methods
#' @export
#' @details The inverse logit transformation takes values on the
#' real line and translates them to be between zero and one using
#' the function `f(x) = 1/(1+exp(-x))`.
#' @examples
#' library(modeldata)
#' data(biomass)
#'
#' biomass_tr <- biomass[biomass$dataset == "Training",]
#' biomass_te <- biomass[biomass$dataset == "Testing",]
#'
#' rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr)
#'
#' ilogit_trans <- rec %>%
#' step_center(carbon, hydrogen) %>%
#' step_scale(carbon, hydrogen) %>%
#' step_invlogit(carbon, hydrogen)
#'
#' ilogit_obj <- prep(ilogit_trans, training = biomass_tr)
#'
#' transformed_te <- bake(ilogit_obj, biomass_te)
#' plot(biomass_te$carbon, transformed_te$carbon)
#' @seealso [step_logit()] [step_log()]
#' [step_sqrt()] [step_hyperbolic()]
#' [recipe()] [prep.recipe()]
#' [bake.recipe()]
step_invlogit <-
function(recipe, ..., role = NA, trained = FALSE, columns = NULL,
skip = FALSE, id = rand_id("invlogit")) {
add_step(recipe,
step_invlogit_new(
terms = ellipse_check(...),
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
))
}
step_invlogit_new <-
function(terms, role, trained, columns, skip, id) {
step(
subclass = "invlogit",
terms = terms,
role = role,
trained = trained,
columns = columns,
skip = skip,
id = id
)
}
#' @export
prep.step_invlogit <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
step_invlogit_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_invlogit <- function(object, new_data, ...) {
for (i in seq_along(object$columns))
new_data[, object$columns[i]] <-
binomial()$linkinv(unlist(getElement(new_data, object$columns[i]),
use.names = FALSE))
as_tibble(new_data)
}
print.step_invlogit <-
function(x, width = max(20, options()$width - 26), ...) {
cat("Inverse logit on ", sep = "")
printer(x$columns, x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_invlogit
#' @param x A `step_invlogit` object.
#' @export
tidy.step_invlogit <- function(x, ...) {
res <- simple_terms(x, ...)
res$id <- x$id
res
}
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