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2
DESCRIPTION
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@@ -1,5 +1,5 @@
Package: AMR Package: AMR
Version: 3.0.1.9084 Version: 3.0.1.9085
Date: 2026-07-09 Date: 2026-07-09
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)

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NEWS.md
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@@ -1,4 +1,4 @@
# AMR 3.0.1.9084 # AMR 3.0.1.9085
Planned as v3.1.0, end of June 2026. Planned as v3.1.0, end of June 2026.

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R/tidymodels.R
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@@ -120,13 +120,14 @@ all_disk_predictors <- function() {
#' @rdname amr-tidymodels #' @rdname amr-tidymodels
#' @export #' @export
step_mic_log2 <- function( step_mic_log2 <- function(
recipe, recipe,
..., ...,
role = NA, role = NA,
trained = FALSE, trained = FALSE,
columns = NULL, columns = NULL,
skip = FALSE, skip = FALSE,
id = recipes::rand_id("mic_log2")) { id = recipes::rand_id("mic_log2")
) {
recipes::add_step( recipes::add_step(
recipe, recipe,
step_mic_log2_new( step_mic_log2_new(
@@ -195,13 +196,14 @@ tidy.step_mic_log2 <- function(x, ...) {
#' @rdname amr-tidymodels #' @rdname amr-tidymodels
#' @export #' @export
step_sir_numeric <- function( step_sir_numeric <- function(
recipe, recipe,
..., ...,
role = NA, role = NA,
trained = FALSE, trained = FALSE,
columns = NULL, columns = NULL,
skip = FALSE, skip = FALSE,
id = recipes::rand_id("sir_numeric")) { id = recipes::rand_id("sir_numeric")
) {
recipes::add_step( recipes::add_step(
recipe, recipe,
step_sir_numeric_new( step_sir_numeric_new(

4
README.Rmd
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@@ -32,7 +32,9 @@ Overview:
---- ----
The `AMR` package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. The `AMR` package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.
**Our aim has always been to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
The `AMR` package supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl). The `AMR` package supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).

9
README.md
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@@ -32,10 +32,11 @@ The `AMR` package is a peer-reviewed, free and open-source R package
with zero dependencies to simplify the analysis and prediction of with zero dependencies to simplify the analysis and prediction of
Antimicrobial Resistance (AMR) and to work with microbial and Antimicrobial Resistance (AMR) and to work with microbial and
antimicrobial data and properties, by using evidence-based methods. antimicrobial data and properties, by using evidence-based methods.
**Our aim is to provide a standard** for clean and reproducible AMR data
analysis, that can therefore empower epidemiological analyses to **Our aim has always been to provide a standard** for clean and
continuously enable surveillance and treatment evaluation in any reproducible AMR data analysis, that can therefore empower
setting. epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting.
The `AMR` package supports and can read any data format, including The `AMR` package supports and can read any data format, including
WHONET data. This package works on Windows, macOS and Linux with all WHONET data. This package works on Windows, macOS and Linux with all

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index.Rmd
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@@ -41,7 +41,9 @@ AMR:::reset_all_thrown_messages()
## Introduction ## Introduction
The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project! The `AMR` package was already cited [over 100 times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC) in scientific research. The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods.
**Our aim has always been to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project! The `AMR` package was already cited [over 100 times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC) in scientific research.
After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](./reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl). After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](./reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).

65
index.md
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@@ -27,12 +27,9 @@
<div style="display: flex; font-size: 0.8em;"> <div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;"> <p style="text-align:left; width: 50%;">
<small><a href="https://amr-for-r.org/">amr-for-r.org</a></small> <small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
</p> </p>
<p style="text-align:right; width: 50%;"> <p style="text-align:right; width: 50%;">
<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small> <small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
</p> </p>
@@ -49,8 +46,10 @@ R package with [zero
dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify
the analysis and prediction of Antimicrobial Resistance (AMR) and to the analysis and prediction of Antimicrobial Resistance (AMR) and to
work with microbial and antimicrobial data and properties, by using work with microbial and antimicrobial data and properties, by using
evidence-based methods. **Our aim is to provide a standard** for clean evidence-based methods.
and reproducible AMR data analysis, that can therefore empower
**Our aim has always been to provide a standard** for clean and
reproducible AMR data analysis, that can therefore empower
epidemiological analyses to continuously enable surveillance and epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting. We are a team of [many different treatment evaluation in any setting. We are a team of [many different
researchers](./authors.html) from around the globe to make this a researchers](./authors.html) from around the globe to make this a
@@ -60,7 +59,7 @@ times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation
in scientific research. in scientific research.
After installing this package, R knows [**~97 000 distinct microbial After installing this package, R knows [**~97 000 distinct microbial
species**](./reference/microorganisms.html) (updated May 2026) and all species**](./reference/microorganisms.html) (updated mei 2026) and all
[**~620 antimicrobial and antiviral [**~620 antimicrobial and antiviral
drugs**](./reference/antimicrobials.html) by name and code (including drugs**](./reference/antimicrobials.html) by name and code (including
ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all
@@ -171,11 +170,13 @@ example_isolates %>%
#> ℹ Using column mo as input for `mo_fullname()` #> 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_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). #> ℹ Determining intrinsic resistance based on 'EUCAST Expected
#> This note will be shown once per session. #> Resistant Phenotypes' v1.2 (2023). This note will be shown
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN #> once per session.
#> (kanamycin) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> # A tibble: 35 × 7 #> # A tibble: 35 × 7
#> bacteria GEN TOB AMK KAN IPM MEM #> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir> #> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
@@ -225,8 +226,8 @@ wisca(example_isolates,
``` ```
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---| |:------------------------|:-------------------------------------|:-------------------------------------|
| 70% (64.8-75.2%) | 93.6% (92-95.1%) | 89.9% (87.1-92.5%) | | 70% (64.8-75.1%) | 93.6% (92.1-95%) | 89.9% (86.9-92.3%) |
WISCA supports stratification by any clinical variable, so you can WISCA supports stratification by any clinical variable, so you can
generate syndrome-specific or ward-specific coverage estimates: generate syndrome-specific or ward-specific coverage estimates:
@@ -240,10 +241,10 @@ wisca(example_isolates,
``` ```
| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---| |:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
| Clinical | 74.6% (69.3-80.3%) | 93.6% (92.1-95%) | 90.4% (87-93.2%) | | Clinical | 74.7% (69-80.3%) | 93.6% (92-95.2%) | 90.4% (86.8-93.1%) |
| ICU | 56.9% (48.2-66.3%) | 86.7% (83.4-89.7%) | 82.9% (78.1-87.3%) | | ICU | 56.9% (48.7-66%) | 86.8% (83.6-90%) | 82.8% (78.3-87.3%) |
| Outpatient | 57.3% (45.8-69.1%) | 76.6% (70.6-81.9%) | 67.9% (58-76.9%) | | Outpatient | 57.2% (46-68.2%) | 76.5% (70.3-82.2%) | 67.7% (57.3-77.2%) |
**For AMR surveillance**, traditional antibiograms remain the right tool **For AMR surveillance**, traditional antibiograms remain the right tool
for tracking resistance per species over time: for tracking resistance per species over time:
@@ -252,13 +253,14 @@ for tracking resistance per species over time:
antibiogram(example_isolates, antibiogram(example_isolates,
mo_transform = "gramstain", mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP")) antimicrobials = c("AMC", carbapenems(), "TZP"))
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
``` ```
| Pathogen | Amoxicillin/clavulanic acid | Imipenem | Meropenem | Piperacillin/tazobactam | | Pathogen | Amoxicillin/clavulanic acid | Imipenem | Meropenem | Piperacillin/tazobactam |
|:---|:---|:---|:---|:---| |:--------------|:----------------------------|:--------------------|:---------------------|:------------------------|
| Gram-negative | 76% (73-79%,N=726) | 99% (98-100%,N=631) | 100% (99-100%,N=626) | 88% (85-91%,N=641) | | Gram-negative | 76% (73-79%,N=726) | 99% (98-100%,N=631) | 100% (99-100%,N=626) | 88% (85-91%,N=641) |
| Gram-positive | 76% (74-79%,N=1138) | 81% (75-85%,N=257) | 77% (70-82%,N=203) | 86% (82-89%,N=345) | | Gram-positive | 76% (74-79%,N=1138) | 81% (75-85%,N=257) | 77% (70-82%,N=203) | 86% (82-89%,N=345) |
Combination antibiograms show the additional coverage gained by adding a Combination antibiograms show the additional coverage gained by adding a
second agent, stratified by species: second agent, stratified by species:
@@ -269,10 +271,10 @@ antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN")) antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
``` ```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | 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-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) | | 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()` and `wisca()` Like many other functions in this package, `antibiogram()` and `wisca()`
come with support for 28 languages that are often detected automatically come with support for 28 languages that are often detected automatically
@@ -367,15 +369,16 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins: # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()), summarise(across(c(aminoglycosides(), polymyxins()),
resistance)) resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (kanamycin) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin) #> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`. #> 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"`. #> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning: #> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (whilst `minimum = #> ! Introducing NA: only 23 results available for KAN in group:
#> 30`). #> ward = "Outpatient" (whilst `minimum = 30`).
out out
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL #> ward GEN TOB AMK KAN COL

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man/AMR.Rd
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@@ -12,7 +12,7 @@ The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyrigh
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}). This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated May 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}. After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated mei 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages. The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
} }

2
man/g.test.Rd
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@@ -46,7 +46,7 @@ A list with class \code{"htest"} containing the following
\code{(observed - expected) / sqrt(expected)}.} \code{(observed - expected) / sqrt(expected)}.}
\item{stdres}{standardized residuals, \item{stdres}{standardized residuals,
\code{(observed - expected) / sqrt(V)}, where \code{V} is the \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{<a href="#reference+chisq.test.Rd+R+3AAgresti+3A2007" class="citation">}}Agresti 2007\if{html}{\out{</a>}}, section 2.4.5)}
for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).} for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
} }
\description{ \description{

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man/ggplot_pca.Rd
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@@ -59,8 +59,9 @@ ggplot_pca(
} }
\item{pc.biplot}{ \item{pc.biplot}{
If true, use what Gabriel (1971) refers to as a "principal component If true, use what {\if{html}{\cite{}\out{<a href="#reference+biplot.princomp.Rd+R+3AGabriel+3A1971" class="citation">}}Gabriel (1971)\if{html}{\out{</a>}}} refers to as a
biplot", with \code{lambda = 1} and observations scaled up by sqrt(n) and \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 scaled down by sqrt(n). Then inner products between
variables approximate covariances and distances between observations variables approximate covariances and distances between observations
approximate Mahalanobis distance. approximate Mahalanobis distance.