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(v3.0.0.9011) allow names for age_groups()

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dr. M.S. (Matthijs) Berends
2025-07-17 19:32:46 +02:00
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commit 39ea5f6597
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man/amr-tidymodels.Rd
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@ -65,56 +65,59 @@ Pre-processing pipeline steps include:
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)
if (require("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.
# 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
# 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))
# 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)
# 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()
# Create and prep a recipe with MIC log2 transformation
mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
# View prepped recipe
mic_recipe
# Optionally remove non-predictive variables
remove_role(genus, old_role = "predictor") \%>\%
# 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)
# Apply the log2 transformation to all MIC predictors
step_mic_log2(all_mic_predictors()) \%>\%
# Fit a logistic regression model
fitted <- logistic_reg(mode = "classification") \%>\%
set_engine("glm") \%>\%
fit(esbl ~ ., data = out_training)
# And apply the preparation steps
prep()
# Generate predictions on the test set
predictions <- predict(fitted, out_testing) \%>\%
bind_cols(out_testing)
# View prepped recipe
mic_recipe
# Evaluate predictions using standard classification metrics
our_metrics <- metric_set(accuracy, kap, ppv, npv)
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
# 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)
# Show performance:
# - negative predictive value (NPV) of ~98\%
# - positive predictive value (PPV) of ~94\%
metrics
# 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
metrics
}
}
\seealso{
\code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.sir]{as.sir()}}