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.
+
+
+ # 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()) %>%
+
+ # And apply the preparation steps
+ 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
+ metrics
+}
+#> Loading required package: tidymodels
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ──
#> ✔ broom 1.0.8 ✔ rsample 1.3.0
#> ✔ dials 1.4.0 ✔ tibble 3.3.0
@@ -135,86 +188,7 @@ may affect the computations for subsequent operations.
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
-
-# 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
-#> # A tibble: 500 × 19
-#> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT
-#> <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
-#> 1 FALSE Esch… 32 32 4 64 64 8.00 8.00 1 1 16.0 20
-#> 2 FALSE Esch… 32 32 4 64 64 4.00 8.00 1 1 16.0 320
-#> 3 FALSE Esch… 4 2 64 8 4 8.00 0.12 16 16 0.5 20
-#> 4 FALSE Kleb… 32 32 16 64 64 8.00 8.00 1 1 0.5 20
-#> 5 FALSE Esch… 32 32 4 4 4 0.25 2.00 1 1 16.0 320
-#> 6 FALSE Citr… 32 32 16 64 64 64.00 32.00 1 1 0.5 20
-#> 7 FALSE Morg… 32 32 4 64 64 16.00 2.00 1 1 0.5 20
-#> 8 FALSE Prot… 16 32 4 1 4 8.00 0.12 1 1 16.0 320
-#> 9 FALSE Ente… 32 32 8 64 64 32.00 4.00 1 1 0.5 20
-#> 10 FALSE Citr… 32 32 32 64 64 8.00 64.00 1 1 16.0 320
-#> # ℹ 490 more rows
-#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
-#> # COL <mic>
-
-# 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
-#>
-#> ── Recipe ──────────────────────────────────────────────────────────────────────
-#>
-#> ── Inputs
-#> Number of variables by role
-#> outcome: 1
-#> predictor: 17
-#> undeclared role: 1
-#>
-#> ── Training information
-#> Training data contained 375 data points and no incomplete rows.
-#>
-#> ── Operations
-#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
-
-# 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)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-
-# 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
#> # A tibble: 4 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 7540effa9..88d095557 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
AMR (for R)
- 3.0.0.9010
+ 3.0.0.9011