diff --git a/404.html b/404.html index 604a63221..f1d2bd2ce 100644 --- a/404.html +++ b/404.html @@ -31,7 +31,7 @@ AMR (for R) - 3.0.0.9021 + 3.0.0.9029 - - - - - -
-
-
-

AMR Extensions for Tidymodels

- Source: R/tidymodels.R -
amr-tidymodels.Rd
-
- -
-

This family of functions allows using AMR-specific data types such as <mic> and <sir> inside tidymodels pipelines.

-
- -
-

Usage

- 
all_mic()
-
-all_mic_predictors()
-
-all_sir()
-
-all_sir_predictors()
-
-step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
-  skip = FALSE, id = recipes::rand_id("mic_log2"))
-
-step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
-  skip = FALSE, id = recipes::rand_id("sir_numeric"))
-
- -
-

Arguments

- - -
recipe
-

A recipe object. The step will be added to the sequence of -operations for this recipe.

- - -
...
-

One or more selector functions to choose variables for this step. -See selections() for more details.

- - -
role
-

Not used by this step since no new variables are created.

- - -
trained
-

A logical to indicate if the quantities for preprocessing have -been estimated.

- - -
skip
-

A logical. Should the step be skipped when the recipe is baked by -bake()? While all operations are baked when prep() is run, some -operations may not be able to be conducted on new data (e.g. processing the -outcome variable(s)). Care should be taken when using skip = TRUE as it -may affect the computations for subsequent operations.

- - -
id
-

A character string that is unique to this step to identify it.

- -
-
-

Details

-

You can read more in our online AMR with tidymodels introduction.

-

Tidyselect helpers include:

  • all_mic() and all_mic_predictors() to select <mic> columns

    • -

  • all_sir() and all_sir_predictors() to select <sir> columns

    • -

Pre-processing pipeline steps include:

  • step_mic_log2() to convert MIC columns to numeric (via as.numeric()) and apply a log2 transform, to be used with all_mic_predictors()

    • -

  • step_sir_numeric() to convert SIR columns to numeric (via as.numeric()), to be used with all_sir_predictors(): "S" = 1, "I"/"SDD" = 2, "R" = 3. All other values are rendered NA. Keep this in mind for further processing, especially if the model does not allow for NA values.

    • -

These steps integrate with recipes::recipe() and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.

-
-
-

See also

-

recipes::recipe(), as.mic(), as.sir()

-
- -
-

Examples

- 
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        1.0.9     ✔ rsample      1.3.1
-#> ✔ dials        1.4.1     ✔ tibble       3.3.0
-#> ✔ infer        1.0.9     ✔ tidyr        1.3.1
-#> ✔ modeldata    1.5.1     ✔ tune         2.0.0
-#> ✔ parsnip      1.3.3     ✔ workflows    1.3.0
-#> ✔ purrr        1.1.0     ✔ workflowsets 1.1.1
-#> ✔ recipes      1.3.1     ✔ yardstick    1.3.2
-#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
-#> ✖ purrr::discard() masks scales::discard()
-#> ✖ dplyr::filter()  masks stats::filter()
-#> ✖ dplyr::lag()     masks stats::lag()
-#> ✖ recipes::step()  masks stats::step()
-#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
-#> # A tibble: 4 × 3
-#>   .metric  .estimator .estimate
-#>   <chr>    <chr>          <dbl>
-#> 1 accuracy binary         0.936
-#> 2 kap      binary         0.872
-#> 3 ppv      binary         0.925
-#> 4 npv      binary         0.948
-
-
-
- - -
- - - - - - - diff --git a/reference/antibiogram.html b/reference/antibiogram.html index f95599e98..d12c016b7 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) - 3.0.0.9021 + 3.0.0.9029 - - - - - -
-
-
-

Data Set with 500 ESBL Isolates

- Source: R/data.R -
esbl_isolates.Rd
-
- -
-

A data set containing 500 microbial isolates with MIC values of common antibiotics and a binary esbl column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with tidymodels.

-
- -
-

Usage

- 
esbl_isolates
-
- -
-

Format

-

A tibble with 500 observations and 19 variables:

  • esbl
    Logical indicator if the isolate is ESBL-producing

    • -

  • genus
    Genus of the microorganism

    • -

  • AMC:COL
    MIC values for 17 antimicrobial agents, transformed to class mic (see as.mic())

    • -
-
-

Details

-

See our tidymodels integration for an example using this data set.

-
- -
-

Examples

- 
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>
-
-
-
- - -
- - - - - - - diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index 97a39282a..621620dd8 100644 --- a/reference/eucast_rules.html +++ b/reference/eucast_rules.html @@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied AMR (for R) - 3.0.0.9021 + 3.0.0.9029