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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/meanimpute.R
\name{step_meanimpute}
\alias{step_meanimpute}
\alias{tidy.step_meanimpute}
\title{Impute Numeric Data Using the Mean}
\usage{
step_meanimpute(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
trim = 0,
skip = FALSE,
id = rand_id("meanimpute")
)
\method{tidy}{step_meanimpute}(x, ...)
}
\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 which variables are
affected by the step. See \code{\link[=selections]{selections()}} for more details. For the \code{tidy}
method, these are not currently used.}
\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{means}{A named numeric vector of means. This is \code{NULL} until computed
by \code{\link[=prep.recipe]{prep.recipe()}}. Note that, if the original data are integers, the mean
will be converted to an integer to maintain the same data type.}
\item{trim}{The fraction (0 to 0.5) of observations to be trimmed from each
end of the variables before the mean is computed. Values of trim outside
that range are taken as the nearest endpoint.}
\item{skip}{A logical. Should the step be skipped when the
recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked
when \code{\link[=prep.recipe]{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 \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.}
\item{x}{A \code{step_meanimpute} object.}
}
\value{
An updated version of \code{recipe} with the new step added to the
sequence of existing steps (if any). For the \code{tidy} method, a tibble with
columns \code{terms} (the selectors or variables selected) and \code{model} (the mean
value).
}
\description{
\code{step_meanimpute} creates a \emph{specification} of a recipe step that will
substitute missing values of numeric variables by the training set mean of
those variables.
}
\details{
\code{step_meanimpute} estimates the variable means 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 averages.
}
\examples{
library(modeldata)
data("credit_data")
## 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_meanimpute(Income, Assets, Debt)
imp_models <- prep(impute_rec, training = credit_tr)
imputed_te <- bake(imp_models, new_data = credit_te, everything())
credit_te[missing_examples,]
imputed_te[missing_examples, names(credit_te)]
tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)
}
\concept{imputation}
\concept{preprocessing}
\keyword{datagen}
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