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#' Impute Numeric Data Below the Threshold of Measurement
#'
#' `step_lowerimpute` creates a *specification* of a recipe step
#' designed for cases where the non-negative numeric data cannot be
#' measured below a known value. In these cases, one method for
#' imputing the data is to substitute the truncated value by a
#' random uniform number between zero and the truncation point.
#'
#' @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 threshold A named numeric vector of lower bounds. This is
#' `NULL` until computed by [prep.recipe()].
#' @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` (the
#' selectors or variables selected) and `value` for the estimated
#' threshold.
#' @keywords datagen
#' @concept preprocessing
#' @concept imputation
#' @export
#' @details `step_lowerimpute` estimates the variable minimums
#' from the data used in the `training` argument of `prep.recipe`.
#' `bake.recipe` then simulates a value for any data at the minimum
#' with a random uniform value between zero and the minimum.
#' @examples
#' library(recipes)
#' library(modeldata)
#' data(biomass)
#'
#' ## Truncate some values to emulate what a lower limit of
#' ## the measurement system might look like
#'
#' biomass$carbon <- ifelse(biomass$carbon > 40, biomass$carbon, 40)
#' biomass$hydrogen <- ifelse(biomass$hydrogen > 5, biomass$carbon, 5)
#'
#' biomass_tr <- biomass[biomass$dataset == "Training",]
#' biomass_te <- biomass[biomass$dataset == "Testing",]
#'
#' rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
#' data = biomass_tr)
#'
#' impute_rec <- rec %>%
#' step_lowerimpute(carbon, hydrogen)
#'
#' tidy(impute_rec, number = 1)
#'
#' impute_rec <- prep(impute_rec, training = biomass_tr)
#'
#' tidy(impute_rec, number = 1)
#'
#' transformed_te <- bake(impute_rec, biomass_te)
#'
#' plot(transformed_te$carbon, biomass_te$carbon,
#' ylab = "pre-imputation", xlab = "imputed")
step_lowerimpute <-
function(recipe,
...,
role = NA,
trained = FALSE,
threshold = NULL,
skip = FALSE,
id = rand_id("lowerimpute")) {
add_step(
recipe,
step_lowerimpute_new(
terms = ellipse_check(...),
role = role,
trained = trained,
threshold = threshold,
skip = skip,
id = id
)
)
}
step_lowerimpute_new <-
function(terms, role, trained, threshold, skip, id) {
step(
subclass = "lowerimpute",
terms = terms,
role = role,
trained = trained,
threshold = threshold,
skip = skip,
id = id
)
}
#' @export
prep.step_lowerimpute <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
threshold <-
vapply(training[, col_names],
min,
numeric(1),
na.rm = TRUE)
if (any(threshold < 0))
rlang::abort(
paste0(
"Some columns have negative values. Lower bound ",
"imputation is intended for data bounded at zero."
)
)
step_lowerimpute_new(
terms = x$terms,
role = x$role,
trained = TRUE,
threshold = threshold,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_lowerimpute <- function(object, new_data, ...) {
for (i in names(object$threshold)) {
affected <- which(new_data[[i]] <= object$threshold[[i]])
if (length(affected) > 0)
new_data[[i]][affected] <- runif(length(affected),
max = object$threshold[[i]])
}
as_tibble(new_data)
}
print.step_lowerimpute <-
function(x, width = max(20, options()$width - 30), ...) {
cat("Lower Bound Imputation for ", sep = "")
printer(names(x$threshold), x$terms, x$trained, width = width)
invisible(x)
}
#' @rdname step_lowerimpute
#' @param x A `step_lowerimpute` object.
#' @export
tidy.step_lowerimpute <- function(x, ...) {
if (is_trained(x)) {
res <- tibble(terms = names(x$threshold),
value = unname(x$threshold))
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, value = na_dbl)
}
res$id <- x$id
res
}
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