From the shell:
# CRAN check from parent directory R CMD check AMR
clinical_breakpoints
as.sir()
recipes
NA
NA_ab_
NA_mo_
NA_character_
NA_integer_
reference_data
> breakpoint_R
< breakpoint_R
host = NA
parallel = TRUE
AMR_env
mclapply
sir_interpretation_history
%pm>%
pm_pull
as.sir.default
\u00a
"1"
as.mic()
"1e-3"
e
as.ab()
ETH
MTH
PHE
PHN
STH
THA
THI1
cli
mdro()
infer_from_combinations
TRUE
A data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the clinical_breakpoints data set (same column names and column types). Please note that the guideline argument will be ignored when reference_data is manually set.
guideline
A data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must have the same column names as the clinical_breakpoints data set. Column types are coerced automatically where possible: the mo column is passed through as.mo(), the ab column through as.ab(), and plain character, numeric, or logical columns are cast to the expected type. When reference_data is manually set, the guideline argument is optional: if omitted (or if its value does not match any row in the custom data), all rows in reference_data are considered. If guideline is set to a value that exists in the guideline column of the custom data, only matching rows are used — useful when a single custom table contains multiple guidelines. For the R classification, the EUCAST convention is used by default: MIC values > breakpoint_R and disk diffusion values < breakpoint_R are classified as R, with values between breakpoint_S and breakpoint_R classified as I (or SDD). Only when using the standard clinical_breakpoints with a CLSI guideline are the closed-interval rules (>= breakpoint_R for MIC, <= breakpoint_R for disk) applied; custom reference_data always uses the open-interval (EUCAST) convention regardless of the guideline name.
mo
as.mo()
ab
breakpoint_S
breakpoint_R
>= breakpoint_R
<= breakpoint_R
A logical to indicate if parallel computing must be used, defaults to FALSE. This requires no additional packages, as the used parallel package is part of base R. On Windows and on R < 4.0.0 parallel::parLapply() will be used, in all other cases the more efficient parallel::mclapply() will be used.
FALSE
parallel
parallel::parLapply()
parallel::mclapply()
A logical to indicate if parallel computing must be used, defaults to FALSE. The parallel package is part of base R and no additional packages are required. On Unix/macOS with R >= 4.0.0, parallel::mclapply() (fork-based) is used; on Windows and R < 4.0.0, parallel::parLapply() with a PSOCK cluster is used (requires the AMR package to be installed, not just loaded via devtools::load_all()). Parallelism distributes columns across cores; it is most beneficial when there are many antibiotic columns and a large number of rows.
devtools::load_all()