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(v3.0.1.9009) tidymodels vignette

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dr. M.S. (Matthijs) Berends
2025-12-23 11:02:26 +01:00
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DESCRIPTION
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Package: AMR Package: AMR
Version: 3.0.1.9008 Version: 3.0.1.9009
Date: 2025-12-22 Date: 2025-12-23
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

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NEWS.md
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# AMR 3.0.1.9008 # AMR 3.0.1.9009
### New ### New
* Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes` * Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`

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vignettes/AMR_with_tidymodels.Rmd
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# Define the recipe # Define the recipe
mic_recipe <- recipe(esbl ~ ., data = training_data) %>% mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
step_mic_log2(all_mic_predictors()) %>% # Log2 transform all MIC predictors step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
prep()
mic_recipe prep(mic_recipe)
``` ```
**Explanation:** **Explanation:**
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- `predict()`: Produces predictions for unseen test data. - `predict()`: Produces predictions for unseen test data.
- `metric_set()`: Allows evaluating multiple classification metrics. This will make `our_metrics` to become a function that we can use to check the predictions with. - `metric_set()`: Allows evaluating multiple classification metrics. This will make `our_metrics` to become a function that we can use to check the predictions with.
It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100` using a simplistic logistic regression model. It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100`% using a simplistic logistic regression model.
### **Visualising Predictions** ### **Visualising Predictions**
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### **Conclusion** ### **Conclusion**
In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models. In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` 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. This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.