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Add parallel computing support to antibiogram() and wisca() (#281) (#282)

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* Add parallel computing support to antibiogram() and wisca() (#281)

For WISCA: simulations are distributed across (group, chunk) job pairs
via future.apply::future_lapply(), keeping all workers active even when
the regimen count is smaller than nbrOfWorkers(). Sequential fallback
with progress ticker is preserved when parallel = FALSE or workers = 1.

For grouped antibiograms: each group is processed by a separate worker,
mirroring the row-batch approach in as.sir().

Same gate pattern as as.sir() (PR #280): requires a non-sequential
future::plan() to be active; auto-upgrades to parallel = TRUE when a
parallel plan is detected; throws an informative error otherwise.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix version to 3.0.1.9055 and update CLAUDE.md version formula

Uses origin/${defaultbranch} (with a fetch) instead of the local
branch ref so the commit count is never stale after a merge.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix non-ASCII characters in antibiogram.R

Replace en/em dashes and non-breaking spaces with ASCII equivalents
to satisfy R CMD check portability requirement.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Update auto-generated Rd files after documentation rebuild

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Move parallel gate to top of antibiogram.default() like sir.R

The gate was inside the wisca==TRUE block, so parallel=TRUE with a
sequential plan was silently ignored for non-WISCA antibiograms.
Now the gate runs unconditionally at the top of the function,
identical to the as.sir() pattern: error on explicit parallel=TRUE
with sequential plan, auto-upgrade when a non-sequential plan is
already active.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix parallel WISCA returning all NA; strengthen tests; add sequential hint

Bug: lapply() over a factor yields length-1 factor elements (integer
codes), while for() over a factor yields character strings.  The job
list stored j\$group as a factor integer, but the reassembly loop
compared it with identical(j\$group, g) where g was character -- always
FALSE, so no simulation chunks were ever assembled and coverage stayed
NA throughout.

Fix: convert unique_groups to character before building jobs so both
the job list and the reassembly loop use the same type.

Tests: replaced na.rm = TRUE guards with explicit anyNA() checks so the
test suite would have caught the all-NA result immediately.

Also adds a sequential-mode performance hint (analogous to sir.R
lines 1116-1127) when simulations >= 500 and >= 3 regimens.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
2026-04-30 18:41:56 +01:00
committed by GitHub
parent 23beebc6c3
commit f7e9294bea
8 changed files with 279 additions and 79 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
CLAUDE.md
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@@ -167,7 +167,8 @@ Then run the following from the repo root to determine the version string to use
currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
currenttagfull=$(git describe --tags --abbrev=0)
defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
currentcommit=$(git rev-list --count ${currenttagfull}..${defaultbranch})
git fetch origin ${defaultbranch} --quiet
currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
echo "$currentversion"
```

4
DESCRIPTION
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@@ -1,6 +1,6 @@
Package: AMR
Version: 3.0.1.9053
Date: 2026-04-27
Version: 3.0.1.9055
Date: 2026-04-30
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by

3
NEWS.md
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@@ -1,4 +1,4 @@
# AMR 3.0.1.9053
# AMR 3.0.1.9055
This will become release v3.1.0, intended for launch end of May.
@@ -7,6 +7,7 @@ This will become release v3.1.0, intended for launch end of May.
* Support for the [`future`](https://future.futureverse.org) package and its framework, as the previous implementation of parallel computing was slow
- **Breaking change**: `as.sir()` with `parallel = TRUE` now requires a non-sequential `future::plan()` to be active before the call — e.g., `future::plan(future::multisession)` — and throws an informative error if none is set.
- New all-core usage setup: when the number of AB columns is smaller than the number of available cores, rows are now split into batches so all cores stay active (row-batch mode). Previously, a 6-column dataset on a 16-core machine would only use 6 cores; now all 16 are used, with each worker processing a smaller row slice (lower per-worker memory pressure and processing time)
- `antibiogram()` and `wisca()` gained a `parallel` argument using the same `future`/`future.apply` pattern: for WISCA, Monte Carlo simulations are split into `(group, chunk)` job pairs distributed across workers; for grouped antibiograms, each group is processed by a separate worker (#281)
* Integration with the *tidymodels* framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`
- `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
- New `tidyselect` helpers:

241
R/antibiogram.R
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@@ -54,7 +54,7 @@
#' @param add_total_n *(deprecated in favour of `formatting_type`)* A [logical] to indicate whether `n_tested` available numbers per pathogen should be added to the table (default is `TRUE`). This will add the lowest and highest number of available isolates per antimicrobial (e.g, if for *E. coli* 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200"). This option is unavailable when `wisca = TRUE`; in that case, use [retrieve_wisca_parameters()] to get the parameters used for WISCA.
#' @param only_all_tested (for combination antibiograms): a [logical] to indicate that isolates must be tested for all antimicrobials, see *Details*.
#' @param digits Number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise.
#' @param formatting_type Numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See *Details* > *Formatting Type* for a list of options.
#' @param formatting_type Numeric value (1-22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See *Details* > *Formatting Type* for a list of options.
#' @param col_mo Column name of the names or codes of the microorganisms (see [as.mo()]) - the default is the first column of class [`mo`]. Values will be coerced using [as.mo()].
#' @param language Language to translate text, which defaults to the system language (see [get_AMR_locale()]).
#' @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*.
@@ -65,6 +65,7 @@
#' @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`).
#' @param interval_side The side of the confidence interval, either `"two-tailed"` (default), `"left"` or `"right"`.
#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`. Requires the [`future.apply`][future.apply::future_lapply()] package. For WISCA, Monte Carlo simulations are distributed across workers; for grouped antibiograms, each group is processed by a separate worker. **A non-sequential [future::plan()] must already be active before setting `parallel = TRUE`** -- for example, `future::plan(future::multisession)`. An error is thrown if `parallel = TRUE` is used without a plan set by the user.
#' @param info A [logical] to indicate info should be printed - the default is `TRUE` only in interactive mode.
#' @param object An [antibiogram()] object.
#' @param ... When used in [R Markdown or Quarto][knitr::kable()]: arguments passed on to [knitr::kable()] (otherwise, has no use).
@@ -413,6 +414,7 @@ antibiogram <- function(x,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...) {
UseMethod("antibiogram")
}
@@ -439,6 +441,7 @@ antibiogram.default <- function(x,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...) {
meet_criteria(x, allow_class = "data.frame")
x <- ascertain_sir_classes(x, "x")
@@ -478,6 +481,35 @@ antibiogram.default <- function(x,
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"))
meet_criteria(info, allow_class = "logical", has_length = 1)
meet_criteria(parallel, allow_class = "logical", has_length = 1)
# parallel gate - identical pattern to as.sir()
if (requireNamespace("future.apply", quietly = TRUE) && !inherits(future::plan(), "sequential")) {
if (isFALSE(parallel)) {
message_("Assuming {.code parallel = TRUE} since parallel computing has been set up using the {.pkg future} package before. Set {.help [{.fun plan}](future::plan)} to sequential to prevent this.")
}
parallel <- TRUE
}
if (isTRUE(parallel)) {
stop_ifnot(
requireNamespace("future.apply", quietly = TRUE),
"Setting {.code parallel = TRUE} requires the {.pkg future.apply} package.\n",
"Install it with {.code install.packages(\"future.apply\")}."
)
stop_if(inherits(future::plan(), "sequential"),
"Setting {.code parallel = TRUE} requires a non-sequential {.help [{.fun future::plan}](future::plan)} to be active.\n",
"For your system, you could first run: {.code library(future); ",
ifelse(.Platform$OS.type == "windows" || in_rstudio(),
"plan(multisession)",
"plan(multicore)"
),
"}",
call = FALSE
)
n_workers <- future::nbrOfWorkers()
} else {
n_workers <- 1L
}
# try to find columns based on type
if (is.null(col_mo)) {
@@ -705,25 +737,71 @@ antibiogram.default <- function(x,
wisca_parameters <- out
progress <- progress_ticker(
n = length(unique(wisca_parameters$group)) * simulations,
n_min = 25,
print = info,
title = paste("Calculating WISCA for", length(unique(wisca_parameters$group)), "regimens")
)
on.exit(close(progress))
# run WISCA per group
for (group in unique(wisca_parameters$group)) {
params_current <- wisca_parameters[wisca_parameters$group == group, , drop = FALSE]
if (sum(params_current$n_tested, na.rm = TRUE) == 0) {
next
# quantile probabilities are constant across all groups
probs <- if (interval_side == "two-tailed") {
c((1 - conf_interval) / 2, 1 - (1 - conf_interval) / 2)
} else if (interval_side == "left") {
c(0, conf_interval)
} else {
c(1 - conf_interval, 1)
}
# prepare priors
priors_current <- create_wisca_priors(params_current)
unique_groups <- as.character(unique(wisca_parameters$group))
# Monte Carlo simulations
use_parallel_wisca <- isTRUE(parallel) && n_workers > 1L && length(unique_groups) > 0L
if (use_parallel_wisca) {
if (isTRUE(info)) {
message_("Running WISCA in parallel mode using ", n_workers, " workers...", as_note = FALSE, appendLF = FALSE)
}
# chunks_per_group gives ~n_workers total jobs so all workers stay busy
# even when the number of regimens is smaller than n_workers
chunks_per_group <- max(1L, ceiling(n_workers / length(unique_groups)))
chunk_sizes <- diff(c(0L, round(seq_len(chunks_per_group) * simulations / chunks_per_group)))
# precompute priors per group and build (group, chunk) job list
jobs <- unlist(lapply(unique_groups, function(g) {
params_g <- wisca_parameters[wisca_parameters$group == g, , drop = FALSE]
if (sum(params_g$n_tested, na.rm = TRUE) == 0L) return(NULL)
priors_g <- create_wisca_priors(params_g)
lapply(seq_along(chunk_sizes), function(ch) {
list(group = g, priors = priors_g, n_sims = chunk_sizes[ch])
})
}), recursive = FALSE)
jobs <- Filter(Negate(is.null), jobs)
flat <- future.apply::future_lapply(jobs, function(job) {
vapply(FUN.VALUE = double(1), seq_len(job$n_sims), function(i) {
simulate_coverage(job$priors)
})
}, future.seed = TRUE)
# reassemble per group: concatenate chunks, then summarise
for (g in unique_groups) {
g_idx <- vapply(jobs, function(j) identical(j$group, g), logical(1))
if (!any(g_idx)) next
sims <- unlist(flat[g_idx], use.names = FALSE)
out_wisca$coverage[out_wisca$group == g] <- mean(sims)
ci_vals <- unname(stats::quantile(sims, probs = probs))
out_wisca$lower_ci[out_wisca$group == g] <- ci_vals[1]
out_wisca$upper_ci[out_wisca$group == g] <- ci_vals[2]
}
if (isTRUE(info)) message_(font_green_bg(" DONE "), as_note = FALSE)
} else {
progress <- progress_ticker(
n = length(unique_groups) * simulations,
n_min = 25,
print = info,
title = paste("Calculating WISCA for", length(unique_groups), "regimens")
)
on.exit(close(progress), add = TRUE)
for (group in unique_groups) {
params_current <- wisca_parameters[wisca_parameters$group == group, , drop = FALSE]
if (sum(params_current$n_tested, na.rm = TRUE) == 0) next
priors_current <- create_wisca_priors(params_current)
coverage_simulations <- vapply(
FUN.VALUE = double(1),
seq_len(simulations), function(i) {
@@ -731,26 +809,24 @@ antibiogram.default <- function(x,
simulate_coverage(priors_current)
}
)
# summarise results
coverage_mean <- mean(coverage_simulations)
if (interval_side == "two-tailed") {
probs <- c((1 - conf_interval) / 2, 1 - (1 - conf_interval) / 2)
} else if (interval_side == "left") {
probs <- c(0, conf_interval)
} else if (interval_side == "right") {
probs <- c(1 - conf_interval, 1)
out_wisca$coverage[out_wisca$group == group] <- mean(coverage_simulations)
ci_vals <- unname(stats::quantile(coverage_simulations, probs = probs))
out_wisca$lower_ci[out_wisca$group == group] <- ci_vals[1]
out_wisca$upper_ci[out_wisca$group == group] <- ci_vals[2]
}
coverage_ci <- unname(stats::quantile(coverage_simulations, probs = probs))
out_wisca$coverage[out_wisca$group == group] <- coverage_mean
out_wisca$lower_ci[out_wisca$group == group] <- coverage_ci[1]
out_wisca$upper_ci[out_wisca$group == group] <- coverage_ci[2]
}
close(progress)
if (isTRUE(info) && simulations >= 500 && length(unique_groups) >= 3) {
suggest <- ifelse(.Platform$OS.type == "windows" || in_rstudio(),
"plan(multisession)",
"plan(multicore)"
)
if (requireNamespace("future.apply", quietly = TRUE)) {
message_("Running in sequential mode. To speed up WISCA, set a parallel {.help [{.fun future::plan}](future::plan)} such as {.code ", suggest, "} and use {.code parallel = TRUE}.")
} else {
message_("Running in sequential mode. To speed up WISCA, install the {.pkg future.apply} package and then set {.code parallel = TRUE}.")
}
}
}
# final output preparation
out <- out_wisca
@@ -997,30 +1073,50 @@ antibiogram.grouped_df <- function(x,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...) {
stop_ifnot(is.null(mo_transform), "{.arg mo_transform} must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, which could include the pathogen information (though not necessary). Nonetheless, this makes {.arg mo_transform} redundant.", call = FALSE)
stop_ifnot(is.null(syndromic_group), "{.arg syndromic_group} must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making {.arg syndromic_group} redundant.", call = FALSE)
meet_criteria(parallel, allow_class = "logical", has_length = 1)
groups <- attributes(x)$groups
n_groups <- NROW(groups)
progress <- progress_ticker(
n = n_groups,
n_min = 5,
print = info,
title = paste("Calculating AMR for", n_groups, "groups")
)
on.exit(close(progress))
out <- NULL
wisca_parameters <- NULL
long_numeric <- NULL
for (i in seq_len(n_groups)) {
progress$tick()
rows <- unlist(groups[i, ]$.rows)
if (length(rows) == 0) {
next
# parallel gate - identical pattern to as.sir()
if (requireNamespace("future.apply", quietly = TRUE) && !inherits(future::plan(), "sequential")) {
if (isFALSE(parallel)) {
message_("Assuming {.code parallel = TRUE} since parallel computing has been set up using the {.pkg future} package before. Set {.help [{.fun plan}](future::plan)} to sequential to prevent this.")
}
new_out <- antibiogram(as.data.frame(x)[rows, , drop = FALSE],
parallel <- TRUE
}
if (isTRUE(parallel)) {
stop_ifnot(
requireNamespace("future.apply", quietly = TRUE),
"Setting {.code parallel = TRUE} requires the {.pkg future.apply} package.\n",
"Install it with {.code install.packages(\"future.apply\")}."
)
stop_if(inherits(future::plan(), "sequential"),
"Setting {.code parallel = TRUE} requires a non-sequential {.help [{.fun future::plan}](future::plan)} to be active.\n",
"For your system, you could first run: {.code library(future); ",
ifelse(.Platform$OS.type == "windows" || in_rstudio(),
"plan(multisession)",
"plan(multicore)"
),
"}",
call = FALSE
)
n_workers <- future::nbrOfWorkers()
} else {
n_workers <- 1L
}
use_parallel <- isTRUE(parallel) && n_workers > 1L && n_groups > 1L
x_df <- as.data.frame(x)
run_group <- function(i) {
rows <- unlist(groups[i, ]$.rows)
if (length(rows) == 0L) return(NULL)
antibiogram(x_df[rows, , drop = FALSE],
antimicrobials = antimicrobials,
mo_transform = NULL,
ab_transform = ab_transform,
@@ -1040,12 +1136,42 @@ antibiogram.grouped_df <- function(x,
conf_interval = conf_interval,
interval_side = interval_side,
info = FALSE,
...
parallel = FALSE # never nest parallelism in workers
)
}
if (use_parallel) {
if (isTRUE(info)) {
message_("Running antibiogram for ", n_groups, " groups in parallel using ", n_workers, " workers...", as_note = FALSE, appendLF = FALSE)
}
results_raw <- future.apply::future_lapply(seq_len(n_groups), run_group, future.seed = TRUE)
if (isTRUE(info)) message_(font_green_bg(" DONE "), as_note = FALSE)
} else {
progress <- progress_ticker(
n = n_groups,
n_min = 5,
print = info,
title = paste("Calculating AMR for", n_groups, "groups")
)
on.exit(close(progress), add = TRUE)
results_raw <- vector("list", n_groups)
for (i in seq_len(n_groups)) {
progress$tick()
results_raw[[i]] <- run_group(i)
}
close(progress)
}
out <- NULL
wisca_parameters <- NULL
long_numeric <- NULL
for (i in seq_len(n_groups)) {
new_out <- results_raw[[i]]
new_wisca_parameters <- attributes(new_out)$wisca_parameters
new_long_numeric <- attributes(new_out)$long_numeric
if (NROW(new_out) == 0) {
if (is.null(new_out) || NROW(new_out) == 0) {
next
}
@@ -1071,8 +1197,7 @@ antibiogram.grouped_df <- function(x,
new_long_numeric <- new_long_numeric[, c(col_name, setdiff(names(new_long_numeric), col_name))] # set place to 1st col
}
if (i == 1) {
# the first go
if (is.null(out)) {
out <- new_out
wisca_parameters <- new_wisca_parameters
long_numeric <- new_long_numeric
@@ -1083,8 +1208,6 @@ antibiogram.grouped_df <- function(x,
}
}
close(progress)
out <- structure(as_original_data_class(out, class(x), extra_class = "antibiogram"),
has_syndromic_group = FALSE,
combine_SI = isTRUE(combine_SI),
@@ -1116,6 +1239,7 @@ wisca <- function(x,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...) {
antibiogram(
x = x,
@@ -1137,6 +1261,7 @@ wisca <- function(x,
conf_interval = conf_interval,
interval_side = interval_side,
info = info,
parallel = parallel,
...
)
}

9
man/antibiogram.Rd
View File

@@ -25,7 +25,8 @@ antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),
minimum = 30, combine_SI = TRUE, sep = " + ", sort_columns = TRUE,
wisca = FALSE, simulations = 1000, conf_interval = 0.95,
interval_side = "two-tailed", info = interactive(), ...)
interval_side = "two-tailed", info = interactive(), parallel = FALSE,
...)
wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
syndromic_group = NULL, only_all_tested = FALSE, digits = 1,
@@ -33,7 +34,7 @@ wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE,
sep = " + ", sort_columns = TRUE, simulations = 1000,
conf_interval = 0.95, interval_side = "two-tailed",
info = interactive(), ...)
info = interactive(), parallel = FALSE, ...)
retrieve_wisca_parameters(wisca_model, ...)
@@ -80,7 +81,7 @@ retrieve_wisca_parameters(wisca_model, ...)
\item{digits}{Number of digits to use for rounding the antimicrobial coverage, defaults to 1 for WISCA and 0 otherwise.}
\item{formatting_type}{Numeric value (1–22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
\item{formatting_type}{Numeric value (1-22 for WISCA, 1-12 for non-WISCA) indicating how the 'cells' of the antibiogram table should be formatted. See \emph{Details} > \emph{Formatting Type} for a list of options.}
\item{col_mo}{Column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
@@ -104,6 +105,8 @@ retrieve_wisca_parameters(wisca_model, ...)
\item{info}{A \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode.}
\item{parallel}{A \link{logical} to indicate if parallel computing must be used, defaults to \code{FALSE}. Requires the \code{\link[future.apply:future_lapply]{future.apply}} package. For WISCA, Monte Carlo simulations are distributed across workers; for grouped antibiograms, each group is processed by a separate worker. \strong{A non-sequential \code{\link[future:plan]{future::plan()}} must already be active before setting \code{parallel = TRUE}} -- for example, \code{future::plan(future::multisession)}. An error is thrown if \code{parallel = TRUE} is used without a plan set by the user.}
\item{...}{When used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use).}
\item{wisca_model}{The outcome of \code{\link[=wisca]{wisca()}} or \code{\link[=antibiogram]{antibiogram(..., wisca = TRUE)}}.}

5
man/g.test.Rd
View File

@@ -45,9 +45,8 @@ A list with class \code{"htest"} containing the following
\item{residuals}{the Pearson residuals,
\code{(observed - expected) / sqrt(expected)}.}
\item{stdres}{standardized residuals,
\code{(observed - expected) / sqrt(V)}, where \code{V} is the
residual cell variance (Agresti, 2007, section 2.4.5
for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
\code{(observed - expected) / sqrt(V)}, where \code{V} is the residual cell variance (Agresti, 2007,
section 2.4.5 for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
}
\description{
\code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable (1). A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).

2
man/pca.Rd
View File

@@ -32,7 +32,7 @@ pca(x, ..., retx = TRUE, center = TRUE, scale. = TRUE, tol = NULL,
standard deviations are less than or equal to \code{tol} times the
standard deviation of the first component.) With the default null
setting, no components are omitted (unless \code{rank.} is specified
less than \code{min(dim(x))}.). Other settings for \code{tol} could be
less than \code{min(dim(x))}.). Other settings for tol could be
\code{tol = 0} or \code{tol = sqrt(.Machine$double.eps)}, which
would omit essentially constant components.}

71
tests/testthat/test-antibiogram.R
View File

@@ -130,6 +130,77 @@ test_that("test-antibiogram.R", {
expect_equal(colnames(ab9), c("ward", "gender", "Piperacillin/tazobactam", "Piperacillin/tazobactam + Gentamicin", "Piperacillin/tazobactam + Tobramycin"))
}
# Parallel computing ----------------------------------------------------
# Tests must pass even when only 1 core is available; parallel = TRUE then
# silently falls back to sequential, but results must still be correct.
if (AMR:::pkg_is_available("future.apply")) {
set.seed(42)
# sequential reference for WISCA
wisca_seq <- suppressWarnings(suppressMessages(
wisca(example_isolates, antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), simulations = 100, info = FALSE)
))
future::plan(future::multicore)
# 1. parallel = TRUE produces the same antibiogram structure as sequential
wisca_par <- suppressWarnings(suppressMessages(
wisca(example_isolates, antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), simulations = 100, parallel = TRUE, info = FALSE)
))
expect_inherits(wisca_par, "antibiogram")
expect_equal(colnames(wisca_par), colnames(wisca_seq))
expect_true(isTRUE(attributes(wisca_par)$wisca))
# 2. coverage values are non-NA and fall within [0, 1]
ln <- attributes(wisca_par)$long_numeric
expect_false(anyNA(ln$coverage))
expect_false(anyNA(ln$lower_ci))
expect_false(anyNA(ln$upper_ci))
expect_true(all(ln$coverage >= 0 & ln$coverage <= 1))
expect_true(all(ln$lower_ci <= ln$coverage))
expect_true(all(ln$upper_ci >= ln$coverage))
# 3. a second parallel run gives the same column names
wisca_par2 <- suppressWarnings(suppressMessages(
wisca(example_isolates, antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), simulations = 100, parallel = TRUE, info = FALSE)
))
expect_equal(colnames(wisca_par), colnames(wisca_par2))
# 4. parallel with workers = 1 gives same structure as sequential
future::plan(future::multicore, workers = 1)
wisca_par1 <- suppressWarnings(suppressMessages(
wisca(example_isolates, antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), simulations = 100, parallel = TRUE, info = FALSE)
))
expect_equal(colnames(wisca_seq), colnames(wisca_par1))
# 5. grouped antibiogram in parallel yields identical structure to sequential
if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
future::plan(future::sequential)
ab_grp_seq <- suppressWarnings(suppressMessages(
example_isolates %>%
group_by(ward) %>%
wisca(antimicrobials = c("TZP", "TZP+TOB"), simulations = 50, info = FALSE)
))
future::plan(future::multicore)
ab_grp_par <- suppressWarnings(suppressMessages(
example_isolates %>%
group_by(ward) %>%
wisca(antimicrobials = c("TZP", "TZP+TOB"), simulations = 50, parallel = TRUE, info = FALSE)
))
expect_equal(colnames(ab_grp_seq), colnames(ab_grp_par))
expect_equal(nrow(ab_grp_seq), nrow(ab_grp_par))
}
# 6. parallel = TRUE without a plan raises an informative error
future::plan(future::sequential)
expect_error(
suppressWarnings(wisca(example_isolates, antimicrobials = "TZP", parallel = TRUE, info = FALSE)),
"non-sequential"
)
future::plan(future::sequential)
}
# Generate plots with ggplot2 or base R --------------------------------