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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/impute_mode.R
\name{step_impute_mode}
\alias{step_impute_mode}
\alias{step_modeimpute}
\title{Impute nominal data using the most common value}
\usage{
step_impute_mode(
recipe,
...,
role = NA,
trained = FALSE,
modes = NULL,
ptype = NULL,
skip = FALSE,
id = rand_id("impute_mode")
)
step_modeimpute(
recipe,
...,
role = NA,
trained = FALSE,
modes = NULL,
ptype = NULL,
skip = FALSE,
id = rand_id("impute_mode")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the
sequence of operations for this recipe.}
\item{...}{One or more selector functions to choose variables
for this step. See \code{\link[=selections]{selections()}} for more details.}
\item{role}{Not used by this step since no new variables are
created.}
\item{trained}{A logical to indicate if the quantities for
preprocessing have been estimated.}
\item{modes}{A named character vector of modes. This is
\code{NULL} until computed by \code{\link[=prep]{prep()}}.}
\item{ptype}{A data frame prototype to cast new data sets to. This is
commonly a 0-row slice of the training set.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake]{bake()}}? While all operations are baked
when \code{\link[=prep]{prep()}} 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 \code{skip = TRUE} as it may affect
the computations for subsequent operations.}
\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of any existing operations.
}
\description{
\code{step_impute_mode} creates a \emph{specification} of a
recipe step that will substitute missing values of nominal
variables by the training set mode of those variables.
}
\details{
\code{step_impute_mode} estimates the variable modes
from the data used in the \code{training} argument of
\code{prep.recipe}. \code{bake.recipe} then applies the new
values to new data sets using these values. If the training set
data has more than one mode, one is selected at random.
As of \code{recipes} 0.1.16, this function name changed from \code{step_modeimpute()}
to \code{step_impute_mode()}.
}
\section{Tidying}{
When you \code{\link[=tidy.recipe]{tidy()}} this step, a tibble with columns
\code{terms} (the selectors or variables selected) and \code{model} (the mode
value) is returned.
}
\section{Case weights}{
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in \link{case_weights} and the examples on
\code{tidymodels.org}.
}
\examples{
\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
data("credit_data", package = "modeldata")
## missing data per column
vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)
credit_tr <- credit_data[in_training, ]
credit_te <- credit_data[-in_training, ]
missing_examples <- c(14, 394, 565)
rec <- recipe(Price ~ ., data = credit_tr)
impute_rec <- rec \%>\%
step_impute_mode(Status, Home, Marital)
imp_models <- prep(impute_rec, training = credit_tr)
imputed_te <- bake(imp_models, new_data = credit_te, everything())
table(credit_te$Home, imputed_te$Home, useNA = "always")
tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)
\dontshow{\}) # examplesIf}
}
\seealso{
Other imputation steps:
\code{\link{step_impute_bag}()},
\code{\link{step_impute_knn}()},
\code{\link{step_impute_linear}()},
\code{\link{step_impute_lower}()},
\code{\link{step_impute_mean}()},
\code{\link{step_impute_median}()},
\code{\link{step_impute_roll}()}
}
\concept{imputation steps}
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