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(v3.0.0.9029) fix for vignette and envir data

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dr. M.S. (Matthijs) Berends
2025-09-10 16:19:30 +02:00
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vignettes/AMR_with_tidymodels.Rmd
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@@ -225,148 +225,12 @@ This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.
>
> The new AMR package version will contain new tidymodels selectors such as `step_mic_log2()`.
<!--
### **Objective**
Our goal is to:
1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
3. Train a classification model and evaluate its predictive performance.
### **Data Preparation**
We use the `esbl_isolates` dataset that comes with the AMR package.
```{r}
# Load required libraries
library(AMR)
library(tidymodels)
# View the esbl_isolates data set
esbl_isolates
# Prepare a binary outcome and convert to ordered factor
data <- esbl_isolates %>%
mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
```
**Explanation:**
- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
### **Defining the Workflow**
#### 1. Preprocessing with a Recipe
We use our `step_mic_log2()` function to log2-transform MIC values, ensuring that MICs are numeric and properly scaled. All MIC predictors can easily and agnostically selected using the new `all_mic_predictors()`:
```{r}
# Split into training and testing sets
set.seed(123)
split <- initial_split(data)
training_data <- training(split)
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()
mic_recipe
```
**Explanation:**
- `remove_role()`: Removes irrelevant variables like genus.
- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
- `prep()`: Finalises the recipe based on training data.
#### 2. Specifying the Model
We use a simple logistic regression to model ESBL presence, though recent models such as xgboost ([link to `parsnip` manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html)) could be much more precise.
```{r}
# Define the model
model <- logistic_reg(mode = "classification") %>%
set_engine("glm")
model
```
**Explanation:**
- `logistic_reg()`: Specifies a binary classification model.
- `set_engine("glm")`: Uses the base R GLM engine.
#### 3. Building the Workflow
```{r}
# Create workflow
workflow_model <- workflow() %>%
add_recipe(mic_recipe) %>%
add_model(model)
workflow_model
```
### **Training and Evaluating the Model**
```{r}
# Fit the model
fitted <- fit(workflow_model, training_data)
# Generate predictions
predictions <- predict(fitted, testing_data) %>%
bind_cols(testing_data)
# Evaluate model performance
our_metrics <- metric_set(accuracy, kap, ppv, npv)
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
metrics
```
**Explanation:**
- `fit()`: Trains the model on the processed training data.
- `predict()`: Produces predictions for unseen test data.
- `metric_set()`: Allows evaluating multiple classification metrics.
It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics$.estimate[3], 3) * 100`% and a negative predictive value (NPV) of `r round(metrics$.estimate[4], 3) * 100` using a simplistic logistic regression model.
### **Visualising Predictions**
We can visualise predictions by comparing predicted and actual ESBL status.
```{r}
library(ggplot2)
ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
geom_bar(position = "stack") +
labs(title = "Predicted vs Actual ESBL Status",
x = "Actual ESBL",
y = "Count") +
theme_minimal()
```
<!--
### **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.
This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
-->
<!-- TODO for AMR v3.1.0: add info from here: https://github.com/msberends/AMR/blob/2461631bcefa78ebdb37bdfad359be74cdd9165a/vignettes/AMR_with_tidymodels.Rmd#L212-L291 -->
---
## Example 3: Predicting AMR Over Time
## Example 2: Predicting AMR Over Time
In this third example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.