(v2.1.1.9268) WISCA vignette, antibiogram sorting, fix translations

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 2.1.1.9267
+Version: 2.1.1.9268
 Date: 2025-05-01
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 2.1.1.9267
+# AMR 2.1.1.9268
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)*
 

R/antibiogram.R

@@ -59,6 +59,7 @@
 #' @param minimum The minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see *Source*.
 #' @param combine_SI A [logical] to indicate whether all susceptibility should be determined by results of either S, SDD, or I, instead of only S (default is `TRUE`).
 #' @param sep A separating character for antimicrobial columns in combination antibiograms.
+#' @param sort_columns A [logical] to indicate whether the antimicrobial columns must be sorted on name.
 #' @param wisca A [logical] to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is `FALSE`). This will use a Bayesian decision model to estimate regimen coverage probabilities using [Monte Carlo simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set `simulations`, `conf_interval`, and `interval_side` to adjust.
 #' @param simulations (for WISCA) a numerical value to set the number of Monte Carlo simulations.
 #' @param conf_interval A numerical value to set confidence interval (default is `0.95`).
@@ -405,6 +406,7 @@ antibiogram <- function(x,
                         minimum = 30,
                         combine_SI = TRUE,
                         sep = " + ",
+                        sort_columns = TRUE,
                         wisca = FALSE,
                         simulations = 1000,
                         conf_interval = 0.95,
@@ -430,6 +432,7 @@ antibiogram.default <- function(x,
                                 minimum = 30,
                                 combine_SI = TRUE,
                                 sep = " + ",
+                                sort_columns = TRUE,
                                 wisca = FALSE,
                                 simulations = 1000,
                                 conf_interval = 0.95,
@@ -449,6 +452,7 @@ antibiogram.default <- function(x,
     deprecation_warning("antibiotics", "antimicrobials", fn = "antibiogram", is_argument = TRUE)
     antimicrobials <- list(...)$antibiotics
   }
+  meet_criteria(antimicrobials, allow_class = "character", allow_NA = FALSE, allow_NULL = FALSE)
   if (!is.function(mo_transform)) {
     meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE, allow_NA = TRUE)
   }
@@ -468,6 +472,7 @@ antibiogram.default <- function(x,
   meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
   meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
   meet_criteria(sep, allow_class = "character", has_length = 1)
+  meet_criteria(sort_columns, allow_class = "logical", has_length = 1)
   meet_criteria(simulations, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
   meet_criteria(conf_interval, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
   meet_criteria(interval_side, allow_class = "character", has_length = 1, is_in = c("two-tailed", "left", "right"))
@@ -591,6 +596,8 @@ antibiogram.default <- function(x,
     )
   colnames(out)[colnames(out) == "total"] <- "n_tested"
   colnames(out)[colnames(out) == "total_rows"] <- "n_total"
+  out$ab <- factor(out$ab, levels = antimicrobials, ordered = TRUE)
+  out <- out[order(out$mo, out$ab), , drop = FALSE]
 
   counts <- out
 
@@ -824,7 +831,7 @@ antibiogram.default <- function(x,
 
   # transform names of antimicrobials
   ab_naming_function <- function(x, t, l, s) {
-    x <- strsplit(x, s, fixed = TRUE)
+    x <- strsplit(as.character(x), s, fixed = TRUE)
     out <- character(length = length(x))
     for (i in seq_along(x)) {
       a <- x[[i]]
@@ -869,6 +876,12 @@ antibiogram.default <- function(x,
   attr(out, "groups") <- NULL
   class(out) <- class(out)[!class(out) %in% c("grouped_df", "grouped_data")]
 
+  if (isTRUE(sort_columns)) {
+    sort_fn <- base::sort
+  } else {
+    sort_fn <- function(x) x
+  }
+
   if (isTRUE(has_syndromic_group)) {
     grps <- unique(out$syndromic_group)
     for (i in seq_along(grps)) {
@@ -886,25 +899,25 @@ antibiogram.default <- function(x,
       # sort rows
       new_df <- new_df %pm>% pm_arrange(syndromic_group)
       # sort columns
-      new_df <- new_df[, c("syndromic_group", sort(colnames(new_df)[colnames(new_df) != "syndromic_group"])), drop = FALSE]
+      new_df <- new_df[, c("syndromic_group", sort_fn(colnames(new_df)[colnames(new_df) != "syndromic_group"])), drop = FALSE]
       colnames(new_df)[1] <- translate_AMR("Syndromic Group", language = language)
     } else {
       # sort rows
       new_df <- new_df %pm>% pm_arrange(mo, syndromic_group)
       # sort columns
-      new_df <- new_df[, c("syndromic_group", "mo", sort(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
+      new_df <- new_df[, c("syndromic_group", "mo", sort_fn(colnames(new_df)[!colnames(new_df) %in% c("syndromic_group", "mo")])), drop = FALSE]
       colnames(new_df)[1:2] <- translate_AMR(c("Syndromic Group", "Pathogen"), language = language)
     }
   } else {
     new_df <- long_to_wide(out)
     if (wisca == TRUE) {
       # sort columns
-      new_df <- new_df[, c(sort(colnames(new_df))), drop = FALSE]
+      new_df <- new_df[, c(sort_fn(colnames(new_df))), drop = FALSE]
     } else {
       # sort rows
       new_df <- new_df %pm>% pm_arrange(mo)
       # sort columns
-      new_df <- new_df[, c("mo", sort(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
+      new_df <- new_df[, c("mo", sort_fn(colnames(new_df)[colnames(new_df) != "mo"])), drop = FALSE]
       colnames(new_df)[1] <- translate_AMR("Pathogen", language = language)
     }
   }
@@ -966,6 +979,7 @@ antibiogram.grouped_df <- function(x,
                                    minimum = 30,
                                    combine_SI = TRUE,
                                    sep = " + ",
+                                   sort_columns = TRUE,
                                    wisca = FALSE,
                                    simulations = 1000,
                                    conf_interval = 0.95,
@@ -1008,6 +1022,7 @@ antibiogram.grouped_df <- function(x,
       minimum = minimum,
       combine_SI = combine_SI,
       sep = sep,
+      sort_columns = sort_columns,
       wisca = wisca,
       simulations = simulations,
       conf_interval = conf_interval,
@@ -1084,6 +1099,7 @@ wisca <- function(x,
                   language = get_AMR_locale(),
                   combine_SI = TRUE,
                   sep = " + ",
+                  sort_columns = TRUE,
                   simulations = 1000,
                   conf_interval = 0.95,
                   interval_side = "two-tailed",
@@ -1103,6 +1119,7 @@ wisca <- function(x,
     language = language,
     combine_SI = combine_SI,
     sep = sep,
+    sort_columns = sort_columns,
     wisca = TRUE,
     simulations = simulations,
     conf_interval = conf_interval,
@@ -1143,8 +1160,8 @@ simulate_coverage <- function(params) {
   n_pathogens <- length(params$gamma_posterior)
 
   # random draws per pathogen
-  random_incidence <- runif(n = n_pathogens)
+  random_incidence <- stats::runif(n = n_pathogens)
-  random_susceptibility <- runif(n = n_pathogens)
+  random_susceptibility <- stats::runif(n = n_pathogens)
 
   simulated_incidence <- stats::qgamma(
     p = random_incidence,

R/translate.R

@@ -203,7 +203,25 @@ translate_into_language <- function(from,
   df_trans <- TRANSLATIONS # internal data file
   from.bak <- from
   from_unique <- unique(from)
-  from_unique_translated <- from_unique
+  from_split_combined <- function(vec) {
+    sapply(vec, function(x) {
+      if (grepl("/", x, fixed = TRUE)) {
+        parts <- strsplit(x, "/", fixed = TRUE)[[1]]
+        # Translate each part separately
+        translated_parts <- translate_into_language(
+          parts,
+          language = lang,
+          only_unknown = only_unknown,
+          only_affect_ab_names = only_affect_ab_names,
+          only_affect_mo_names = only_affect_mo_names
+        )
+        paste(translated_parts, collapse = "/")
+      } else {
+        x
+      }
+    }, USE.NAMES = FALSE)
+  }
+  from_unique_translated <- from_split_combined(from_unique)
 
   # only keep lines where translation is available for this language
   df_trans <- df_trans[which(!is.na(df_trans[, lang, drop = TRUE])), , drop = FALSE]

man/antibiogram.Rd

@@ -23,16 +23,17 @@ antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
   only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
   formatting_type = getOption("AMR_antibiogram_formatting_type",
   ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),
-  minimum = 30, combine_SI = TRUE, sep = " + ", wisca = FALSE,
+  minimum = 30, combine_SI = TRUE, sep = " + ", sort_columns = TRUE,
-  simulations = 1000, conf_interval = 0.95, interval_side = "two-tailed",
+  wisca = FALSE, simulations = 1000, conf_interval = 0.95,
-  info = interactive(), ...)
+  interval_side = "two-tailed", info = interactive(), ...)
 
 wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
   syndromic_group = NULL, only_all_tested = FALSE, digits = 1,
   formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
   col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE,
-  sep = " + ", simulations = 1000, conf_interval = 0.95,
+  sep = " + ", sort_columns = TRUE, simulations = 1000,
-  interval_side = "two-tailed", info = interactive(), ...)
+  conf_interval = 0.95, interval_side = "two-tailed",
+  info = interactive(), ...)
 
 retrieve_wisca_parameters(wisca_model, ...)
 
@@ -90,6 +91,8 @@ retrieve_wisca_parameters(wisca_model, ...)
 
 \item{sep}{A separating character for antimicrobial columns in combination antibiograms.}
 
+\item{sort_columns}{A \link{logical} to indicate whether the antimicrobial columns must be sorted on name.}
+
 \item{wisca}{A \link{logical} to indicate whether a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be generated (default is \code{FALSE}). This will use a Bayesian decision model to estimate regimen coverage probabilities using \href{https://en.wikipedia.org/wiki/Monte_Carlo_method}{Monte Carlo simulations}. Set \code{simulations}, \code{conf_interval}, and \code{interval_side} to adjust.}
 
 \item{simulations}{(for WISCA) a numerical value to set the number of Monte Carlo simulations.}

tests/testthat/test-ab_property.R

@@ -33,7 +33,7 @@ test_that("test-ab_property.R", {
   ab_reset_session()
 
   expect_identical(ab_name("AMX", language = NULL), "Amoxicillin")
-  expect_identical(ab_atc("AMX"), "J01CA04")
+  expect_identical(ab_atc("AMX"), c("J01CA04", "QG51AA03", "QJ01CA04"))
   expect_identical(ab_cid("AMX"), as.integer(33613))
 
   expect_inherits(ab_tradenames("AMX"), "character")

tests/testthat/test-atc_online.R

@@ -35,8 +35,8 @@ test_that("test-atc_online.R", {
     AMR:::pkg_is_available("xml2") &&
     tryCatch(curl::has_internet(), error = function(e) FALSE)) {
     expect_true(length(atc_online_groups(ab_atc("AMX"))) >= 1)
-    expect_equal(atc_online_ddd(ab_atc("AMX"), administration = "O"), 1.5)
+    expect_equal(atc_online_ddd(ab_atc("AMX", only_first = TRUE), administration = "O"), 1.5)
-    expect_equal(atc_online_ddd(ab_atc("AMX"), administration = "P"), 3)
+    expect_equal(atc_online_ddd(ab_atc("AMX", only_first = TRUE), administration = "P"), 3)
     expect_equal(atc_online_ddd_units("AMX", administration = "P"), "g")
   }
 })

tests/testthat/test-data.R

@@ -34,7 +34,6 @@ test_that("test-data.R", {
   expect_identical(nrow(microorganisms), length(unique(microorganisms$mo)))
   expect_identical(class(microorganisms$mo), c("mo", "character"))
   expect_identical(nrow(antimicrobials), length(unique(AMR::antimicrobials$ab)))
-  expect_true(all(is.na(AMR::antimicrobials$atc[duplicated(AMR::antimicrobials$atc)])))
   expect_identical(class(AMR::antimicrobials$ab), c("ab", "character"))
 
 

vignettes/AMR.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "How to conduct AMR data analysis"
+title: "Conduct AMR data analysis"
 output: 
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{How to conduct AMR data analysis}
+  %\VignetteIndexEntry{Conduct AMR data analysis}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: 

vignettes/AMR_with_tidymodels.Rmd

@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-> This page was entirely written by our [AMR for R Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained model able to answer any question about the AMR package.
+> This page was entirely written by our [AMR for R Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained model able to answer any question about the `AMR` package.
 
 Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective treatments. The `AMR` R package provides robust tools for analysing AMR data, including convenient antimicrobial selector functions like `aminoglycosides()` and `betalactams()`. 
 

vignettes/EUCAST.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "How to apply EUCAST rules"
+title: "Apply EUCAST rules"
 output:
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{How to apply EUCAST rules}
+  %\VignetteIndexEntry{Apply EUCAST rules}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options:

vignettes/MDR.Rmd

@@ -1,10 +1,10 @@
 ---
-title: "How to determine multi-drug resistance (MDR)"
+title: "Determine multi-drug resistance (MDR)"
 output: 
   rmarkdown::html_vignette:
     toc: true
 vignette: >
-  %\VignetteIndexEntry{How to determine multi-drug resistance (MDR)}
+  %\VignetteIndexEntry{Determine multi-drug resistance (MDR)}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: 

vignettes/PCA.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "How to conduct principal component analysis (PCA) for AMR"
+title: "Conduct principal component analysis (PCA) for AMR"
 output: 
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{How to conduct principal component analysis (PCA) for AMR}
+  %\VignetteIndexEntry{Conduct principal component analysis (PCA) for AMR}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: 

vignettes/WHONET.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "How to work with WHONET data"
+title: "Work with WHONET data"
 output:
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 3
 vignette: >
-  %\VignetteIndexEntry{How to work with WHONET data}
+  %\VignetteIndexEntry{Work with WHONET data}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options:

vignettes/WISCA.Rmd

@@ -22,202 +22,187 @@ knitr::opts_chunk$set(
 )
 ```
 
+> This explainer was largely written by our [AMR for R Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained model able to answer any question about the `AMR` package.
+
 ## Introduction
 
 Clinical guidelines for empirical antimicrobial therapy require *probabilistic reasoning*: what is the chance that a regimen will cover the likely infecting organisms, before culture results are available?
 
-This is the purpose of **WISCA**, or:
+This is the purpose of **WISCA**, or **Weighted-Incidence Syndromic Combination Antibiogram**.
-
-> **Weighted-Incidence Syndromic Combination Antibiogram**
 
 WISCA is a Bayesian approach that integrates:
+
 - **Pathogen prevalence** (how often each species causes the syndrome),
 - **Regimen susceptibility** (how often a regimen works *if* the pathogen is known),
 
-to estimate the **overall empirical coverage** of antimicrobial regimens — with quantified uncertainty.
+to estimate the **overall empirical coverage** of antimicrobial regimens, with quantified uncertainty.
 
-This vignette explains how WISCA works, why it is useful, and how to apply it in **AMR**.
+This vignette explains how WISCA works, why it is useful, and how to apply it using the `AMR` package.
-
----
 
 ## Why traditional antibiograms fall short
 
 A standard antibiogram gives you:
 
-``` Species → Antibiotic → Susceptibility %
+```
+Species → Antibiotic → Susceptibility %
+```
 
-But clinicians don’t know the species *a priori*. They need to choose a regimen that covers the **likely pathogens** — without knowing which one is present.
+But clinicians don’t know the species *a priori*. They need to choose a regimen that covers the **likely pathogens**, without knowing which one is present.
+
+Traditional antibiograms calculate the susceptibility % as just the number of resistant isolates divided by the total number of tested isolates. Therefore, traditional antibiograms:
 
-Traditional antibiograms:
 - Fragment information by organism,
 - Do not weight by real-world prevalence,
 - Do not account for combination therapy or sample size,
 - Do not provide uncertainty.
 
----
-
 ## The idea of WISCA
 
 WISCA asks:
 
-> “What is the **probability** that this regimen **will cover** the pathogen, given the syndrome?”
+> "What is the **probability** that this regimen **will cover** the pathogen, given the syndrome?"
 
 This means combining two things:
+
 - **Incidence** of each pathogen in the syndrome,
 - **Susceptibility** of each pathogen to the regimen.
 
 We can write this as:
 
-``` coverage = ∑ (pathogen incidence × susceptibility)
+$$\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)$$
 
 For example, suppose:
-- E. coli causes 60% of cases, and 90% of *E. coli* are susceptible to a drug.
+
-- Klebsiella causes 40% of cases, and 70% of *Klebsiella* are susceptible.
+- *E. coli* causes 60% of cases, and 90% of *E. coli* are susceptible to a drug.
+- *Klebsiella* causes 40% of cases, and 70% of *Klebsiella* are susceptible.
 
 Then:
 
-``` coverage = (0.6 × 0.9) + (0.4 × 0.7) = 0.82
+$$\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82$$
 
-But in real data, incidence and susceptibility are **estimated from samples** — so they carry uncertainty. WISCA models this **probabilistically**, using conjugate Bayesian distributions.
+But in real data, incidence and susceptibility are **estimated from samples**, so they carry uncertainty. WISCA models this **probabilistically**, using conjugate Bayesian distributions.
-
----
 
 ## The Bayesian engine behind WISCA
 
 ### Pathogen incidence
 
 Let:
-- K be the number of pathogens,
-- ``` α = (1, 1, ..., 1) be a **Dirichlet** prior (uniform),
-- ``` n = (n₁, ..., nₖ) be the observed counts per species.
 
-Then the posterior incidence follows:
+- $K$ be the number of pathogens,
+- $\alpha = (1, 1, \ldots, 1)$ be a **Dirichlet** prior (uniform),
+- $n = (n_1, \ldots, n_K)$ be the observed counts per species.
 
-``` incidence ∼ Dirichlet(α + n)
+Then the posterior incidence is:
 
-In simulations, we draw from this posterior using:
+$$p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)$$
 
-``` xᵢ ∼ Gamma(αᵢ + nᵢ, 1)
+To simulate from this, we use:
 
-``` incidenceᵢ = xᵢ / ∑ xⱼ
+$$x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}$$
-
----
 
 ### Susceptibility
 
-Each pathogen–regimen pair has:
+Each pathogen–regimen pair has a prior and data:
-- ``` prior: Beta(1, 1)
-- ``` data: S susceptible out of N tested
 
-Then:
+- Prior: $\text{Beta}(\alpha_0, \beta_0)$, with default $\alpha_0 = \beta_0 = 1$
+- Data: $S$ susceptible out of $N$ tested
 
-``` susceptibility ∼ Beta(1 + S, 1 + (N - S))
+The $S$ category could also include values SDD (susceptible, dose-dependent) and I (intermediate [CLSI], or susceptible, increased exposure [EUCAST]).
 
-In each simulation, we draw random susceptibility per species from this Beta distribution.
+Then the posterior is:
 
----
+$$\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)$$
 
 ### Final coverage estimate
 
 Putting it together:
 
-``` For each simulation:
+1. Simulate pathogen incidence: $\boldsymbol{p} \sim \text{Dirichlet}$
-    - Draw incidence ∼ Dirichlet
+2. Simulate susceptibility: $\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)$
-    - Draw susceptibility ∼ Beta
+3. Combine:
-    - Multiply → coverage estimate
 
-We repeat this (e.g. 1000×) and summarise:
+$$\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i$$
-- **Mean**: expected coverage
-- **Quantiles**: credible interval (default 95%)
 
----
+Repeat this simulation (e.g. 1000×) and summarise:
 
-## Practical use in AMR
+- **Mean** = expected coverage
+- **Quantiles** = credible interval
 
-### Simulate a synthetic syndrome
+## Practical use in the `AMR` package
+
+### Prepare data and simulate synthetic syndrome
 
 ```{r}
 library(AMR)
 data <- example_isolates
 
-# Add a fake syndrome column for stratification
+# Structure of our data
-data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Other")
+data
 
+# Add a fake syndrome column
+data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "No UTI")
 ```
 
 ### Basic WISCA antibiogram
 
 ```{r}
-antibiogram(data,
+wisca(data,
-            wisca = TRUE)
+      antimicrobials = c("AMC", "CIP", "GEN"))
+```
+
+### Use combination regimens
+
+```{r}
+wisca(data,
+      antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"))
 ```
 
 ### Stratify by syndrome
 
 ```{r}
-antibiogram(data,
+wisca(data,
-            syndromic_group = "syndrome",
+      antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
-            wisca = TRUE)
+      syndromic_group = "syndrome")
 ```
 
-### Use combination regimens
+The `AMR` package is available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages, which can all be used for the `wisca()` function too:
-
-The `antibiogram()` function supports combination regimens:
 
 ```{r}
-antibiogram(data,
+wisca(data,
-            antimicrobials = c("AMC", "GEN", "AMC + GEN", "CIP"),
+      antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
-            wisca = TRUE)
+      syndromic_group = gsub("UTI", "UCI", data$syndrome),
+      language = "Spanish")
 ```
 
----
 
-## Interpretation
+## Sensible defaults, which can be customised
 
-Suppose you get this output:
+- `simulations = 1000`: number of Monte Carlo draws
-
+- `conf_interval = 0.95`: coverage interval width
-| Regimen     | Coverage | Lower_CI | Upper_CI |
+- `combine_SI = TRUE`: count "I" and "SDD" as susceptible
-|-------------|----------|----------|----------|
-| AMC         | 0.72     | 0.65     | 0.78     |
-| AMC + GEN   | 0.88     | 0.83     | 0.93     |
-
-Interpretation:
-
-> *“AMC + GEN covers 88% of expected pathogens for this syndrome, with 95% certainty that the true coverage lies between 83% and 93%.”*
-
-Regimens with few tested isolates will show **wider intervals**.
-
----
-
-## Sensible defaults, but you can customise
-
-- `minimum = 30`: exclude regimens with <30 isolates tested.
-- `simulations = 1000`: number of Monte Carlo samples.
-- `conf_interval = 0.95`: coverage interval width.
-- `combine_SI = TRUE`: count “I”/“SDD” as susceptible.
-
----
 
 ## Limitations
 
-- WISCA does not model time trends or temporal resistance shifts.
+- It assumes your data are representative
-- It assumes data are representative of current clinical practice.
+- No adjustment for patient-level covariates, although these could be passed onto the `syndromic_group` argument
-- It does not account for patient-level covariates (yet).
+- WISCA does not model resistance over time, you might want to use `tidymodels` for that, for which we [wrote a basic introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
-- Species-specific data are abstracted into syndrome-level estimates.
 
----
+## Summary
+
+WISCA enables:
+
+- Empirical regimen comparison,
+- Syndrome-specific coverage estimation,
+- Fully probabilistic interpretation.
+
+It is available in the `AMR` package via either:
+
+```r
+wisca(...)
+
+antibiogram(..., wisca = TRUE)
+```
 
 ## Reference
 
-Bielicki JA et al. (2016).  
+Bielicki, JA, et al. (2016). *Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data.* **J Antimicrob Chemother**. 71(3):794-802. https://doi.org/10.1093/jac/dkv397
-*Weighted-incidence syndromic combination antibiograms to guide empiric treatment in pediatric bloodstream infections.*  
-**J Antimicrob Chemother**, 71(2):529–536. doi:10.1093/jac/dkv397
-
----
-
-## Conclusion
-
-WISCA shifts empirical therapy from simple percent susceptible toward **probabilistic, syndrome-based decision support**. It is a statistically principled, clinically intuitive method to guide regimen selection — and easy to use via the `antibiogram()` function in the **AMR** package.
-
-For antimicrobial stewardship teams, it enables **disease-specific, reproducible, and data-driven guidance** — even in the face of sparse data.
-

vignettes/datasets.Rmd

@@ -1,12 +1,12 @@
 ---
-title: "Data sets for download / own use"
+title: "Download data sets for download / own use"
 date: '`r format(Sys.Date(), "%d %B %Y")`'
 output: 
   rmarkdown::html_vignette:
     toc: true
     toc_depth: 1
 vignette: >
-  %\VignetteIndexEntry{Data sets for download / own use}
+  %\VignetteIndexEntry{Download data sets for download / own use}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options: