In this example, we demonstrated how to build a machine learning
pipeline with the tidymodels framework and the
AMR package. By combining selector functions like
aminoglycosides() and betalactams() with
@@ -521,10 +521,9 @@ predictors can easily and agnostically selected using the new
# Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
- step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors
- # prep()
+ step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
-mic_recipe
+prep(mic_recipe)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
@@ -534,8 +533,11 @@ predictors can easily and agnostically selected using the new
#> predictor: 17
#> undeclared role: 1
#>
+#> ── Training information
+#> Training data contained 375 data points and no incomplete rows.
+#>
#> ── Operations
-#> • Log2 transformation of MIC columns: all_mic_predictors()
+#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
It appears we can predict ESBL gene presence with a positive
predictive value (PPV) of 92.1% and a negative predictive value (NPV) of
-91.9 using a simplistic logistic regression model.
+91.9% using a simplistic logistic regression model.
-
-Conclusion
+
+Conclusion
In this example, we demonstrated how to analyze AMR trends over time
using tidymodels. By aggregating resistance rates by year
diff --git a/articles/AMR_with_tidymodels.md b/articles/AMR_with_tidymodels.md
index 747801c2f..bf99ea546 100644
--- a/articles/AMR_with_tidymodels.md
+++ b/articles/AMR_with_tidymodels.md
@@ -332,9 +332,9 @@ predictions %>%
### **Conclusion**
-In this post, we demonstrated how to build a machine learning pipeline
-with the `tidymodels` framework and the `AMR` package. By combining
-selector functions like
+In this example, we demonstrated how to build a machine learning
+pipeline with the `tidymodels` framework and the `AMR` package. By
+combining selector functions like
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
@@ -431,10 +431,9 @@ testing_data <- testing(split)
# Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
- step_mic_log2(all_mic_predictors()) #%>% # Log2 transform all MIC predictors
- # prep()
+ step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
-mic_recipe
+prep(mic_recipe)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
@@ -444,8 +443,11 @@ mic_recipe
#> predictor: 17
#> undeclared role: 1
#>
+#> ── Training information
+#> Training data contained 375 data points and no incomplete rows.
+#>
#> ── Operations
-#> • Log2 transformation of MIC columns: all_mic_predictors()
+#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
```
**Explanation:**
@@ -540,8 +542,8 @@ metrics
check the predictions with.
It appears we can predict ESBL gene presence with a positive predictive
-value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9 using
-a simplistic logistic regression model.
+value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
+using a simplistic logistic regression model.
### **Visualising Predictions**
@@ -574,20 +576,22 @@ predictions %>%
colour = correct)) +
scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
name = "Correct?") +
- geom_point() +
+ geom_point() +
scale_y_continuous(labels = function(x) paste0(x * 100, "%"),
limits = c(0.5, 1)) +
theme_minimal()
```
- \###
-**Conclusion**
+
+
+### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps
simplify working with `` columns in `tidymodels`. The
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
-transformation converts ordered MICs to log2-transformed numerics,
-improving compatibility with classification models.
+transformation converts MICs (with or without operators) to
+log2-transformed numerics, improving compatibility with classification
+models.
This pipeline enables realistic, reproducible, and interpretable
modelling of antimicrobial resistance data.
@@ -762,7 +766,7 @@ fitted_workflow_time <- resistance_workflow_time %>%
# Make predictions
predictions_time <- fitted_workflow_time %>%
predict(test_time) %>%
- bind_cols(test_time)
+ bind_cols(test_time)
# Evaluate model
metrics_time <- predictions_time %>%
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 9e045a3b4..63eebca50 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9007
+ 3.0.1.9009