% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/knn_imp.R
\name{step_knnimpute}
\alias{step_knnimpute}
\title{Imputation via K-Nearest Neighbors}
\usage{
step_knnimpute(recipe, ..., role = NA, trained = FALSE, K = 5,
  impute_with = imp_vars(all_predictors()), ref_data = NULL,
  columns = NULL)
}
\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
\code{step_knnimpute}, this indicates the variables to be imputed. When
used with \code{imp_vars}, the dots indicates which variables are used to
predict the missing data in each variable. See \code{\link{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{K}{The number of neighbors.}

\item{impute_with}{A call to \code{imp_vars} to specify which variables are
used to impute the variables that can include specific variable names
separated by commas or different selectors (see
\code{\link{selections}}).  If a column is included in both lists to be
imputed and to be an imputation predictor, it will be removed from the
latter and not used to impute itself.}

\item{ref_data}{A tibble of data that will reflect the data preprocessing
done up to the point of this imputation step. This is
\code{NULL} until the step is trained by \code{\link{prep.recipe}}.}

\item{columns}{The column names that will be imputed and used for
imputation. This is  \code{NULL} until the step is trained by
\code{\link{prep.recipe}}.}
}
\value{
An updated version of \code{recipe} with the
  new step added to the sequence of existing steps (if any).
}
\description{
\code{step_knnimpute} creates a \emph{specification} of a recipe step that
  will impute missing data using nearest neighbors.
}
\details{
The step uses the training set to impute any other data sets. The
  only distance function available is Gower's distance which can be used for
  mixtures of nominal and numeric data.

Once the nearest neighbors are determined, the mode is used to predictor
  nominal variables and the mean is used for numeric data.

Note that if a variable that is to be imputed is also in \code{impute_with},
  this variable will be ignored.

It is possible that missing values will still occur after imputation if a
  large majority (or all) of the imputing variables are also missing.
}
\examples{
library(recipes)
data(biomass)

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
biomass_te_whole <- biomass_te

# induce some missing data at random
set.seed(9039)
carb_missing <- sample(1:nrow(biomass_te), 3)
nitro_missing <- sample(1:nrow(biomass_te), 3)

biomass_te$carbon[carb_missing] <- NA
biomass_te$nitrogen[nitro_missing] <- NA

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
              data = biomass_tr)

ratio_recipe <- rec \%>\%
  step_knnimpute(all_predictors(), K = 3)
ratio_recipe2 <- prep(ratio_recipe, training = biomass_tr)
imputed <- bake(ratio_recipe2, biomass_te)

# how well did it work?
summary(biomass_te_whole$carbon)
cbind(before = biomass_te_whole$carbon[carb_missing],
      after = imputed$carbon[carb_missing])

summary(biomass_te_whole$nitrogen)
cbind(before = biomass_te_whole$nitrogen[nitro_missing],
      after = imputed$nitrogen[nitro_missing])
}
\references{
Gower, C. (1971) "A general coefficient of similarity and some
  of its properties," Biometrics, 857-871.
}
\concept{
preprocessing imputation
}
\keyword{datagen}
