1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/impute_linear.R
\name{step_impute_linear}
\alias{step_impute_linear}
\title{Impute numeric variables via a linear model}
\usage{
step_impute_linear(
recipe,
...,
role = NA,
trained = FALSE,
impute_with = imp_vars(all_predictors()),
models = NULL,
skip = FALSE,
id = rand_id("impute_linear")
)
}
\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 to be imputed;
these variables \strong{must} be of type \code{numeric}. When used with \code{imp_vars},
these dots indicate which variables are used to predict the missing data
in each variable. 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{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]{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{models}{The \code{\link[=lm]{lm()}} objects are stored here once the linear models
have been trained by \code{\link[=prep]{prep()}}.}
\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_linear} creates a \emph{specification} of a recipe step that will
create linear regression models to impute missing data.
}
\details{
For each variable requiring imputation, a linear model is fit
where the outcome is the variable of interest and the predictors are any
other variables listed in the \code{impute_with} formula. Note that if a variable
that is to be imputed is also in \code{impute_with}, this variable will be ignored.
The variable(s) to be imputed must be of type \code{numeric}. The imputed values
will keep the same type as their original data (i.e, model predictions are
coerced to integer as needed).
Since this is a linear regression, the imputation model only uses complete
cases for the training set predictors.
}
\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
bagged tree object) 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(c("modeldata", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
data(ames, package = "modeldata")
set.seed(393)
ames_missing <- ames
ames_missing$Longitude[sample(1:nrow(ames), 200)] <- NA
imputed_ames <-
recipe(Sale_Price ~ ., data = ames_missing) \%>\%
step_impute_linear(
Longitude,
impute_with = imp_vars(Latitude, Neighborhood, MS_Zoning, Alley)
) \%>\%
prep(ames_missing)
imputed <-
bake(imputed_ames, new_data = ames_missing) \%>\%
dplyr::rename(imputed = Longitude) \%>\%
bind_cols(ames \%>\% dplyr::select(original = Longitude)) \%>\%
bind_cols(ames_missing \%>\% dplyr::select(Longitude)) \%>\%
dplyr::filter(is.na(Longitude))
library(ggplot2)
ggplot(imputed, aes(x = original, y = imputed)) +
geom_abline(col = "green") +
geom_point(alpha = .3) +
coord_equal() +
labs(title = "Imputed Values")
\dontshow{\}) # examplesIf}
}
\references{
Kuhn, M. and Johnson, K. (2013).
\emph{Feature Engineering and Selection}
\url{https://bookdown.org/max/FES/handling-missing-data.html}
}
\seealso{
Other imputation steps:
\code{\link{step_impute_bag}()},
\code{\link{step_impute_knn}()},
\code{\link{step_impute_lower}()},
\code{\link{step_impute_mean}()},
\code{\link{step_impute_median}()},
\code{\link{step_impute_mode}()},
\code{\link{step_impute_roll}()}
}
\concept{imputation steps}
|
