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(v3.0.1.9047) fix #272

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dr. M.S. (Matthijs) Berends
2026-04-21 22:11:40 +02:00
parent 8ff5d4472a
commit e0f8cf0882
7 changed files with 19 additions and 20 deletions
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2
DESCRIPTION
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@@ -1,5 +1,5 @@
Package: AMR Package: AMR
Version: 3.0.1.9045 Version: 3.0.1.9047
Date: 2026-04-21 Date: 2026-04-21
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)

3
NEWS.md
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@@ -1,4 +1,4 @@
# AMR 3.0.1.9045 # AMR 3.0.1.9047
### New ### New
* Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations. * Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations.
@@ -32,6 +32,7 @@
* Fixed a bug to disregard `NI` for susceptibility proportion functions * Fixed a bug to disregard `NI` for susceptibility proportion functions
* Fixed Italian translation of CoNS to Stafilococco coagulasi-negativo and CoPS to Stafilococco coagulasi-positivo (#256) * Fixed Italian translation of CoNS to Stafilococco coagulasi-negativo and CoPS to Stafilococco coagulasi-positivo (#256)
* Fixed SIR and MIC coercion of combined values, e.g. `as.sir("<= 0.002; S") ` or `as.mic("S; 0.002")` (#252) * Fixed SIR and MIC coercion of combined values, e.g. `as.sir("<= 0.002; S") ` or `as.mic("S; 0.002")` (#252)
* Fixed translation of foreign languages in `sir_df()` (#272)
### Updates ### Updates
* Extensive `cli` integration for better message handling and clickable links in messages and warnings (#191, #265) * Extensive `cli` integration for better message handling and clickable links in messages and warnings (#191, #265)

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R/ab_property.R
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@@ -361,7 +361,7 @@ ab_property <- function(x, property = "name", language = get_AMR_locale(), ...)
meet_criteria(x, allow_NA = TRUE) meet_criteria(x, allow_NA = TRUE)
meet_criteria(property, is_in = colnames(AMR::antimicrobials), has_length = 1) meet_criteria(property, is_in = colnames(AMR::antimicrobials), has_length = 1)
language <- validate_language(language) language <- validate_language(language)
translate_into_language(ab_validate(x = x, property = property, ...), language = language) translate_into_language(ab_validate(x = x, property = property, ...), language = language, only_affect_ab_names = TRUE)
} }
#' @rdname ab_property #' @rdname ab_property

10
man/amr-tidymodels.Rd
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@@ -73,7 +73,6 @@ These steps integrate with \code{recipes::recipe()} and work like standard prepr
} }
\examples{ \examples{
if (require("tidymodels")) { if (require("tidymodels")) {
# The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703 # 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. # Presence of ESBL genes was predicted based on raw MIC values.
@@ -92,13 +91,10 @@ if (require("tidymodels")) {
# Create and prep a recipe with MIC log2 transformation # Create and prep a recipe with MIC log2 transformation
mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\% mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
# Optionally remove non-predictive variables # Optionally remove non-predictive variables
remove_role(genus, old_role = "predictor") \%>\% remove_role(genus, old_role = "predictor") \%>\%
# Apply the log2 transformation to all MIC predictors # Apply the log2 transformation to all MIC predictors
step_mic_log2(all_mic_predictors()) \%>\% step_mic_log2(all_mic_predictors()) \%>\%
# And apply the preparation steps # And apply the preparation steps
prep() prep()
@@ -119,13 +115,15 @@ if (require("tidymodels")) {
bind_cols(out_testing) bind_cols(out_testing)
# Evaluate predictions using standard classification metrics # Evaluate predictions using standard classification metrics
our_metrics <- metric_set(accuracy, our_metrics <- metric_set(
accuracy,
recall, recall,
precision, precision,
sensitivity, sensitivity,
specificity, specificity,
ppv, ppv,
npv) npv
)
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class) metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
# Show performance # Show performance