% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidymodels.R
\name{amr-tidymodels}
\alias{amr-tidymodels}
\alias{all_mic}
\alias{all_mic_predictors}
\alias{all_sir}
\alias{all_sir_predictors}
\alias{step_mic_log2}
\alias{step_sir_numeric}
\title{AMR Extensions for Tidymodels}
\usage{
all_mic()

all_mic_predictors()

all_sir()

all_sir_predictors()

step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
  skip = FALSE, id = recipes::rand_id("mic_log2"))

step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
  skip = FALSE, id = recipes::rand_id("sir_numeric"))
}
\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[recipes: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{skip}{A logical. Should the step be skipped when the recipe is baked by
\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes: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.}
}
\description{
This family of functions allows using AMR-specific data types such as \verb{<mic>} and \verb{<sir>} inside \code{tidymodels} pipelines.
}
\details{
You can read more in our online \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{AMR with tidymodels introduction}.

Tidyselect helpers include:
\itemize{
\item \code{\link[=all_mic]{all_mic()}} and \code{\link[=all_mic_predictors]{all_mic_predictors()}} to select \verb{<mic>} columns
\item \code{\link[=all_sir]{all_sir()}} and \code{\link[=all_sir_predictors]{all_sir_predictors()}} to select \verb{<sir>} columns
}

Pre-processing pipeline steps include:
\itemize{
\item \code{\link[=step_mic_log2]{step_mic_log2()}} to convert MIC columns to numeric (via \code{as.numeric()}) and apply a log2 transform, to be used with \code{\link[=all_mic_predictors]{all_mic_predictors()}}
\item \code{\link[=step_sir_numeric]{step_sir_numeric()}} to convert SIR columns to numeric (via \code{as.numeric()}), to be used with \code{\link[=all_sir_predictors]{all_sir_predictors()}}: \code{"S"} = 1, \code{"I"}/\code{"SDD"} = 2, \code{"R"} = 3. All other values are rendered \code{NA}. Keep this in mind for further processing, especially if the model does not allow for \code{NA} values.
}

These steps integrate with \code{recipes::recipe()} and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
}
\examples{
library(tidymodels)

# The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
# Presence of ESBL genes was predicted based on raw MIC values.


# example data set in the AMR package
esbl_isolates

# Prepare a binary outcome and convert to ordered factor
data <- esbl_isolates \%>\%
  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))

# Split into training and testing sets
split <- initial_split(data)
training_data <- training(split)
testing_data <- testing(split)

# Create and prep a recipe with MIC log2 transformation
mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
  # Optionally remove non-predictive variables
  remove_role(genus, old_role = "predictor") \%>\%
  # Apply the log2 transformation to all MIC predictors
  step_mic_log2(all_mic_predictors()) \%>\%
  prep()

# View prepped recipe
mic_recipe

# Apply the recipe to training and testing data
out_training <- bake(mic_recipe, new_data = NULL)
out_testing <- bake(mic_recipe, new_data = testing_data)

# Fit a logistic regression model
fitted <- logistic_reg(mode = "classification") \%>\%
  set_engine("glm") \%>\%
  fit(esbl ~ ., data = out_training)

# Generate predictions on the test set
predictions <- predict(fitted, out_testing) \%>\%
  bind_cols(out_testing)

# Evaluate predictions using standard classification metrics
our_metrics <- metric_set(accuracy, kap, ppv, npv)
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)

# Show performance:
# - negative predictive value (NPV) of ~98\%
# - positive predictive value (PPV) of ~94\%
metrics
}
\seealso{
\code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.sir]{as.sir()}}
}
\keyword{internal}