diff --git a/DESCRIPTION b/DESCRIPTION index c0cd36711..597f40030 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: AMR Version: 3.0.1.9059 -Date: 2026-05-06 +Date: 2026-06-23 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 diff --git a/NEWS.md b/NEWS.md index 909b4bb0a..186b7c9f9 100644 --- a/NEWS.md +++ b/NEWS.md @@ -51,6 +51,7 @@ Planned as v3.1.0, end of June 2026. * Improved console messages with clickable links throughout, powered by `cli` if it is installed (#191, #265) * `as.disk()`: input validation is now more strict, rejecting values that are not recognisable as a numeric disk zone diameter + # AMR 3.0.1 This is a bugfix release following the release of v3.0.0 in June 2025. diff --git a/R/aa_globals.R b/R/aa_globals.R index 4fd932a82..1ba28e171 100755 --- a/R/aa_globals.R +++ b/R/aa_globals.R @@ -113,7 +113,7 @@ TAXONOMY_VERSION <- list( LPSN = list( name = "List of Prokaryotic names with Standing in Nomenclature (LPSN)", accessed_date = as.Date("2026-05-07"), - citation = "Freese, HM *et al.* (2026). **TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity.** Nucleic Acids Research, 54, D884–D891; \\doi{10.1093/nar/gkaf1110}.", + citation = "Freese, HM *et al.* (2026). **TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity.** Nucleic Acids Research, 54, D884\u2013D891; \\doi{10.1093/nar/gkaf1110}.", url = "https://lpsn.dsmz.de" ), MycoBank = list( @@ -149,10 +149,13 @@ TAXONOMY_VERSION <- list( ) globalVariables(c( + ".coverage", ".GenericCallEnv", + ".lower", ".mo", ".rowid", ".syndromic_group", + ".upper", "ab", "ab_txt", "affect_ab_name", @@ -188,6 +191,7 @@ globalVariables(c( "hjust", "host_index", "host_match", + "incidence", "input", "input_given", "intrinsic_resistant", @@ -215,6 +219,7 @@ globalVariables(c( "old", "old_name", "p_susceptible", + "pathogen", "pattern", "R", "rank_index", diff --git a/R/antibiogram.R b/R/antibiogram.R index 0045b30d7..f9e8590cf 100755 --- a/R/antibiogram.R +++ b/R/antibiogram.R @@ -898,7 +898,7 @@ antibiogram.default <- function(x, susceptibility = sim_susceptibility ) out_wisca$coverage[out_wisca$group == group] <- mean(sim_coverage) - ci_vals <- unname(stats::quantile(coverage_simulations, probs = probs)) + ci_vals <- unname(stats::quantile(sim_coverage, probs = probs)) out_wisca$lower_ci[out_wisca$group == group] <- ci_vals[1] out_wisca$upper_ci[out_wisca$group == group] <- ci_vals[2] } @@ -1657,10 +1657,10 @@ autoplot.antibiogram <- function(object, if (is.null(caption)) { if (is_wisca) { - out <- out + labs(caption = "Overlapping credible intervals:\nclinically non-inferior (Bielicki 2020)") + out <- out + ggplot2::labs(caption = "Overlapping credible intervals:\nclinically non-inferior (Bielicki 2020)") } } else if (!caption %in% c(FALSE, NA)) { - out <- out + labs(caption = caption) + out <- out + ggplot2::labs(caption = caption) } out <- out + diff --git a/R/mo_property.R b/R/mo_property.R index f8acdb58a..74836bce4 100755 --- a/R/mo_property.R +++ b/R/mo_property.R @@ -46,7 +46,7 @@ #' #' The short name ([mo_shortname()]) returns the first character of the genus and the full species, such as `"E. coli"`, for species and subspecies. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will always be considered *Escherichia coli*. As a result, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`. #' -#' Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G\u00f6ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), [mo_kingdom()] and [mo_domain()] return different results for bacteria and archaea: [mo_kingdom()] returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while [mo_domain()] returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results. +#' Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G"{o}ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), [mo_kingdom()] and [mo_domain()] return different results for bacteria and archaea: [mo_kingdom()] returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while [mo_domain()] returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results. #' #' Determination of human pathogenicity ([mo_pathogenicity()]) is strongly based on Bartlett *et al.* (2022, \doi{10.1099/mic.0.001269}). This function returns a [factor] with the levels *Pathogenic*, *Potentially pathogenic*, *Non-pathogenic*, and *Unknown*. #' diff --git a/R/sysdata.rda b/R/sysdata.rda index d768e0a73..8356a6243 100755 Binary files a/R/sysdata.rda and b/R/sysdata.rda differ diff --git a/R/tidymodels.R b/R/tidymodels.R index f357b815f..b2513e0aa 100755 --- a/R/tidymodels.R +++ b/R/tidymodels.R @@ -120,13 +120,14 @@ all_disk_predictors <- function() { #' @rdname amr-tidymodels #' @export step_mic_log2 <- function( - recipe, - ..., - role = NA, - trained = FALSE, - columns = NULL, - skip = FALSE, - id = recipes::rand_id("mic_log2")) { + recipe, + ..., + role = NA, + trained = FALSE, + columns = NULL, + skip = FALSE, + id = recipes::rand_id("mic_log2") +) { recipes::add_step( recipe, step_mic_log2_new( @@ -195,13 +196,14 @@ tidy.step_mic_log2 <- function(x, ...) { #' @rdname amr-tidymodels #' @export step_sir_numeric <- function( - recipe, - ..., - role = NA, - trained = FALSE, - columns = NULL, - skip = FALSE, - id = recipes::rand_id("sir_numeric")) { + recipe, + ..., + role = NA, + trained = FALSE, + columns = NULL, + skip = FALSE, + id = recipes::rand_id("sir_numeric") +) { recipes::add_step( recipe, step_sir_numeric_new( diff --git a/data-raw/interpretive_rules.tsv b/data-raw/interpretive_rules.tsv index af076d4c2..3a14d5ec6 100644 --- a/data-raw/interpretive_rules.tsv +++ b/data-raw/interpretive_rules.tsv @@ -1444,7 +1444,7 @@ EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella cephalo EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella aminoglycosides R EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF S CIP S EUCAST Expert Rules 3.3 Expert Rules on Salmonella genus is Salmonella PEF R CIP R -EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZO R Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")] +EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX R AMC, AMP, AMX, APL, APX, ATM, AXS, AZA, AZD, AZL, BAM, BIA, BNB, BNP, CAC, CAR, CAT, CAZ, CCL, CCP, CCV, CCX, CDC, CDR, CEB, CEC, CED, CEM, CEP, CEQ, CFA, CFM, CFM1, CFP, CFR, CFS, CFZ, CHE, CIC, CID, CLM, CLO, CMX, CMZ, CND, CPA, CPC, CPD, CPI, CPL, CPM, CPO, CPR, CPX, CRB, CRD, CRN, CRO, CSE, CSL, CSU, CTA, CTB, CTC, CTF, CTL, CTS, CTT, CTX, CTZ, CXA, CXM, CZA, CZD, CZL, CZO, CZP, CZT, CZX, DIC, DIT, DIX, DIZ, DOR, EPC, ETP, FDC, FEP, FLC, FOV, FOX, FPE, FPT, FPZ, FTA, HAP, HET, IMR, IPM, LEN, LEX, LOR, LTM, MAN, MEC, MEM, MET, MEV, MEZ, MSU, MTM, NAF, OXA, PAN, PEN, PHE, PHN, PIP, PIS, PME, PNM, PNO, PRB, PRC, PRP, PSU, PVM, RIA, RID, RIT, RZM, SAM, SBC, SLT6, SRX, TAL, TAN, TBP, TCC, TEM, TIC, TIO, TMN, TZP, ZOP R Betalactams without ceftaroline and ceftobiprole = betalactams()[!betalactams() %in% c("CPT", "BPR")] EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus FOX S betalactams_with_inhibitor, carbapenems S Must be S to all betactams with recognised anti-staphylococcal activity EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus_species is Staphylococcus aureus PEN R AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC R all penicillins without beta-lactamse inhibitor and excluding isoxazolylpenicillines EUCAST Expert Rules 3.3 Expert Rules on Staphylococcus genus is Staphylococcus ERY, CLI S macrolides, lincosamides S diff --git a/data/antibiotics.rda b/data/antibiotics.rda index 9a9e74d3d..4b919f059 100644 Binary files a/data/antibiotics.rda and b/data/antibiotics.rda differ diff --git a/data/antimicrobials.rda b/data/antimicrobials.rda index f5953720f..26199aa8c 100644 Binary files a/data/antimicrobials.rda and b/data/antimicrobials.rda differ diff --git a/index.md b/index.md index c020ad69f..367ff6458 100644 --- a/index.md +++ b/index.md @@ -26,12 +26,9 @@

- amr-for-r.org

-

- doi.org/10.18637/jss.v104.i03

@@ -174,24 +171,26 @@ example_isolates %>% #> ℹ Using column mo as input for `mo_fullname()` #> ℹ Using column mo as input for `mo_is_gram_negative()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` -#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant -#> Phenotypes' v1.2 (2023). This note will be shown once per session. -#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK -#> (amikacin), and KAN (kanamycin) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) +#> ℹ Determining intrinsic resistance based on 'EUCAST Expected +#> Resistant Phenotypes' v1.2 (2023). This note will be shown +#> once per session. +#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB +#> (tobramycin), AMK (amikacin), and KAN (kanamycin) +#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM +#> (meropenem) #> # A tibble: 35 × 7 -#> bacteria GEN TOB AMK KAN IPM MEM -#> -#> 1 Pseudomonas aeruginosa I S NA R S NA -#> 2 Pseudomonas aeruginosa I S NA R S NA -#> 3 Pseudomonas aeruginosa I S NA R S NA -#> 4 Pseudomonas aeruginosa S S S R NA S -#> 5 Pseudomonas aeruginosa S S S R S S -#> 6 Pseudomonas aeruginosa S S S R S S -#> 7 Stenotrophomonas maltophilia R R R R R R -#> 8 Pseudomonas aeruginosa S S S R NA S -#> 9 Pseudomonas aeruginosa S S S R NA S -#> 10 Pseudomonas aeruginosa S S S R S S +#> bacteria GEN TOB AMK KAN IPM MEM +#> +#> 1 Pseudomonas aer… I S NA R S NA +#> 2 Pseudomonas aer… I S NA R S NA +#> 3 Pseudomonas aer… I S NA R S NA +#> 4 Pseudomonas aer… S S S R NA S +#> 5 Pseudomonas aer… S S S R S S +#> 6 Pseudomonas aer… S S S R S S +#> 7 Stenotrophomona… R R R R R R +#> 8 Pseudomonas aer… S S S R NA S +#> 9 Pseudomonas aer… S S S R NA S +#> 10 Pseudomonas aer… S S S R S S #> # ℹ 25 more rows ``` @@ -215,23 +214,24 @@ output format automatically (such as markdown, LaTeX, HTML, etc.). ``` r antibiogram(example_isolates, antimicrobials = c(aminoglycosides(), carbapenems())) -#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK -#> (amikacin), and KAN (kanamycin) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) +#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB +#> (tobramycin), AMK (amikacin), and KAN (kanamycin) +#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM +#> (meropenem) ``` -| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | -|:---|:---|:---|:---|:---|:---|:---| -| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | -| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | -| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) | -| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) | -| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) | -| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) | -| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) | -| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) | -| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) | -| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | +| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | +|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------| +| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | +| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | +| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) | +| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) | +| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) | +| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) | +| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) | +| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) | +| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) | +| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage: @@ -242,10 +242,10 @@ antibiogram(example_isolates, mo_transform = "gramstain") ``` -| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | -|:---|:---|:---|:---| -| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) | -| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) | +| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | +|:--------------|:------------------------|:-------------------------------------|:-------------------------------------| +| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) | +| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) | Like many other functions in this package, `antibiogram()` comes with support for 28 languages that are often detected automatically based on @@ -318,16 +318,18 @@ example_isolates %>% summarise(across(c(GEN, TOB), list(total_R = resistance, conf_int = function(x) sir_confidence_interval(x, collapse = "-")))) -#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' -#> category susceptible. Set the `guideline` argument or the `AMR_guideline` -#> option to either "CLSI" or "EUCAST", see `?AMR-options`. +#> ℹ `resistance()` assumes the EUCAST guideline and thus +#> considers the 'I' category susceptible. Set the `guideline` +#> argument or the `AMR_guideline` option to either "CLSI" or +#> "EUCAST", see `?AMR-options`. #> ℹ This message will be shown once per session. #> # A tibble: 3 × 5 -#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int -#> -#> 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347 -#> 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449 -#> 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493 +#> ward GEN_total_R GEN_conf_int TOB_total_R +#> +#> 1 Clinical 0.229 0.205-0.254 0.315 +#> 2 ICU 0.290 0.253-0.33 0.400 +#> 3 Outpatient 0.2 0.131-0.285 0.368 +#> # ℹ 1 more variable: TOB_conf_int ``` Or use [antimicrobial @@ -344,15 +346,16 @@ out <- example_isolates %>% # calculate AMR using resistance(), over all aminoglycosides and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) -#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK -#> (amikacin), and KAN (kanamycin) +#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB +#> (tobramycin), AMK (amikacin), and KAN (kanamycin) #> ℹ For `polymyxins()` using column COL (colistin) #> Warning: There was 1 warning in `summarise()`. -#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`. +#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), +#> resistance)`. #> ℹ In group 3: `ward = "Outpatient"`. #> Caused by warning: -#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" -#> (whilst `minimum = 30`). +#> ! Introducing NA: only 23 results available for KAN in group: +#> ward = "Outpatient" (whilst `minimum = 30`). out #> # A tibble: 3 × 6 #> ward GEN TOB AMK KAN COL @@ -366,11 +369,12 @@ out # transform the antibiotic columns to names: out %>% set_ab_names() #> # A tibble: 3 × 6 -#> ward gentamicin tobramycin amikacin kanamycin colistin -#> -#> 1 Clinical 0.229 0.315 0.626 1 0.780 -#> 2 ICU 0.290 0.400 0.662 1 0.857 -#> 3 Outpatient 0.2 0.368 0.605 NA 0.889 +#> ward gentamicin tobramycin amikacin kanamycin +#> +#> 1 Clinical 0.229 0.315 0.626 1 +#> 2 ICU 0.290 0.400 0.662 1 +#> 3 Outpatient 0.2 0.368 0.605 NA +#> # ℹ 1 more variable: colistin ``` ``` r diff --git a/man/g.test.Rd b/man/g.test.Rd index 39a42bc1f..8d072b379 100644 --- a/man/g.test.Rd +++ b/man/g.test.Rd @@ -46,7 +46,7 @@ A list with class \code{"htest"} containing the following \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 + residual cell variance {(\if{html}{\out{}}Agresti 2007\if{html}{\out{}}, section 2.4.5)} for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).} } \description{ diff --git a/man/ggplot_pca.Rd b/man/ggplot_pca.Rd index aff174a6d..7b7c86bc8 100644 --- a/man/ggplot_pca.Rd +++ b/man/ggplot_pca.Rd @@ -42,8 +42,9 @@ ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE, } \item{pc.biplot}{ - If true, use what Gabriel (1971) refers to as a "principal component - biplot", with \code{lambda = 1} and observations scaled up by sqrt(n) and + If true, use what {\if{html}{\cite{}\out{}}Gabriel (1971)\if{html}{\out{}}} refers to as a + \dQuote{principal component biplot}, + with \code{lambda = 1} and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance. diff --git a/man/mo_property.Rd b/man/mo_property.Rd index c269939cc..cd241cdf6 100644 --- a/man/mo_property.Rd +++ b/man/mo_property.Rd @@ -200,7 +200,7 @@ All functions will, at default, \strong{not} keep old taxonomic properties, as s The short name (\code{\link[=mo_shortname]{mo_shortname()}}) returns the first character of the genus and the full species, such as \code{"E. coli"}, for species and subspecies. Exceptions are abbreviations of staphylococci (such as \emph{"CoNS"}, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as \emph{"GBS"}, Group B Streptococci). Please bear in mind that e.g. \emph{E. coli} could mean \emph{Escherichia coli} (kingdom of Bacteria) as well as \emph{Entamoeba coli} (kingdom of Protozoa). Returning to the full name will be done using \code{\link[=as.mo]{as.mo()}} internally, giving priority to bacteria and human pathogens, i.e. \code{"E. coli"} will always be considered \emph{Escherichia coli}. As a result, \code{mo_fullname(mo_shortname("Entamoeba coli"))} returns \code{"Escherichia coli"}. -Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G\u00f6ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), \code{\link[=mo_kingdom]{mo_kingdom()}} and \code{\link[=mo_domain]{mo_domain()}} return different results for bacteria and archaea: \code{\link[=mo_kingdom]{mo_kingdom()}} returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while \code{\link[=mo_domain]{mo_domain()}} returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results. +Following the formal introduction of the new kingdom rank into prokaryotic nomenclature by G"{o}ker and Oren (2024, \doi{10.1099/ijsem.0.006242}), \code{\link[=mo_kingdom]{mo_kingdom()}} and \code{\link[=mo_domain]{mo_domain()}} return different results for bacteria and archaea: \code{\link[=mo_kingdom]{mo_kingdom()}} returns the new formal kingdom (e.g. "Pseudomonadati", "Bacillati"), while \code{\link[=mo_domain]{mo_domain()}} returns the new domain (e.g. "Bacteria", "Archaea"). For non-prokaryotic organisms, both functions return identical results. Determination of human pathogenicity (\code{\link[=mo_pathogenicity]{mo_pathogenicity()}}) is strongly based on Bartlett \emph{et al.} (2022, \doi{10.1099/mic.0.001269}). This function returns a \link{factor} with the levels \emph{Pathogenic}, \emph{Potentially pathogenic}, \emph{Non-pathogenic}, and \emph{Unknown}.