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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ordinalscore.R
\name{step_ordinalscore}
\alias{step_ordinalscore}
\alias{tidy.step_ordinalscore}
\title{Convert Ordinal Factors to Numeric Scores}
\usage{
step_ordinalscore(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
convert = as.numeric,
skip = FALSE,
id = rand_id("ordinalscore")
)
\method{tidy}{step_ordinalscore}(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{columns}{A character string of variables that will be
converted. This is \code{NULL} until computed by
\code{\link[=prep.recipe]{prep.recipe()}}.}
\item{convert}{A function that takes an ordinal factor vector
as an input and outputs a single numeric variable.}
\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_ordinalscore} 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
columns that will be affected).
}
\description{
\code{step_ordinalscore} creates a \emph{specification} of a
recipe step that will convert ordinal factor variables into
numeric scores.
}
\details{
Dummy variables from ordered factors with \code{C}
levels will create polynomial basis functions with \code{C-1}
terms. As an alternative, this step can be used to translate the
ordered levels into a single numeric vector of values that
represent (subjective) scores. By default, the translation uses
a linear scale (1, 2, 3, ... \code{C}) but custom score
functions can also be used (see the example below).
}
\examples{
fail_lvls <- c("meh", "annoying", "really_bad")
ord_data <-
data.frame(item = c("paperclip", "twitter", "airbag"),
fail_severity = factor(fail_lvls,
levels = fail_lvls,
ordered = TRUE))
model.matrix(~fail_severity, data = ord_data)
linear_values <- recipe(~ item + fail_severity, data = ord_data) \%>\%
step_dummy(item) \%>\%
step_ordinalscore(fail_severity)
linear_values <- prep(linear_values, training = ord_data)
bake(linear_values, new_data = NULL, everything())
custom <- function(x) {
new_values <- c(1, 3, 7)
new_values[as.numeric(x)]
}
nonlin_scores <- recipe(~ item + fail_severity, data = ord_data) \%>\%
step_dummy(item) \%>\%
step_ordinalscore(fail_severity, convert = custom)
tidy(nonlin_scores, number = 2)
nonlin_scores <- prep(nonlin_scores, training = ord_data)
bake(nonlin_scores, new_data = NULL, everything())
tidy(nonlin_scores, number = 2)
}
\concept{ordinal_data}
\concept{preprocessing}
\keyword{datagen}
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