Package: biplotEZ 3.0

biplotEZ: EZ-to-Use Biplots

Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Authors:Sugnet Lubbe [aut, cre, cph], Niël le Roux [aut], Johané Nienkemper-Swanepoel [aut], Raeesa Ganey [aut], Ruan Buys [aut], Zoë-Mae Adams [aut], Peter Manefeldt [aut]

biplotEZ_3.0.tar.gz
biplotEZ_3.0.zip(r-4.7-x86_64)biplotEZ_3.0.zip(r-4.7-arm64)biplotEZ_3.0.zip(r-4.6-x86_64)biplotEZ_3.0.zip(r-4.6-arm64)biplotEZ_3.0.zip(r-4.5-x86_64)
biplotEZ_3.0.tgz(r-4.6-x86_64)biplotEZ_3.0.tgz(r-4.6-arm64)biplotEZ_3.0.tgz(r-4.5-x86_64)biplotEZ_3.0.tgz(r-4.5-arm64)
biplotEZ_3.0.tar.gz(r-4.7-arm64)biplotEZ_3.0.tar.gz(r-4.7-x86_64)biplotEZ_3.0.tar.gz(r-4.6-arm64)biplotEZ_3.0.tar.gz(r-4.6-x86_64)
biplotEZ_3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
biplotEZ/json (API)

# Install 'biplotEZ' in R:
install.packages('biplotEZ', repos = c('https://muvisu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/muvisu/biplotez/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

8.82 score 10 stars 2 packages 65 scripts 560 downloads 31 exports 3 dependencies

Last updated from:b134476618. Checks:13 WARNING, 2 OK. Indexed: yes.

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macos-release-x86_64WARNING377
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wasm-releaseOK104

Exports:alpha.bagsAoDaxesbiplotCAclassificationclassifyCLPsCLRsCVAdensity1Ddensity2Dellipsesextended.matching.coefficientfit.measuresgg_biplotinterpolatelegend.typeMCAmeansnewaxesnewsamplesPCAPCOpredictionreflectregressrotatesamplessqrtManhattantranslate_axes

Dependencies:plotrixRcppwithr

biplotEZ enhancements
Introduction | The function alpha.bags() | The function ellipses() | The function density2D() | The functions interpolate() with newsamples() and newaxes() | The function prediction() | The functions reflect() and rotate() | Zooming in on the biplot with zoom = TRUE in plot() | The function translate_axes()
Rendered frombiplotEZ_enhancements.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2024-07-04

biplotEZ
What is a PCA biplot | The function biplot() | Using biplot() with princomp() or prcomp() | The functions PCA() and plot() | The function samples() | The function means() | The function axes() | The functions fit.measures() and summary() | Quality of approximation | Adequacy of representation of the variables | Predictivities | Axis predictivity | Sample predictivity | Axes represented as vectors | References
Rendered frombiplotEZ.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2023-10-19

Biplots in 1D
One-dimensional PCA biplot | One-dimensional CVA biplot | Classification using classify() | One-dimensional CA biplot | The function interpolate() | The function newsamples() | The function newaxes() | The function prediction() | Predicting Samples | Predicting Group Means | Ellipses and Alpha bags | The function density1D() | The function legend.type() | The function fit.measures() | References
Rendered fromBiplots_in_1D.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2024-07-04

CA and MCA
What is a Correspondence Analysis? | CA biplot | Coordinates | Lambda scaling | Measures of fit | The function CA() | Variant="Princ" | Variant="Stand" | Variant="Symmetric" | Interpolating new samples | Aesthetics | Additional example | What is a Multiple Correspondence Analysis? | MCA biplot | The function MCA() | Measure of fit | References
Rendered fromCA_MCA.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2026-09-25

Class separation
What is a CVA biplot | The function CVA() | The function means() | The function classify() | The functions fit.measures() and summary() | Overall quality of fit | Adequacy of representation of variables | Predictivity | Between class predictivity | Axis predictivity | Class predictivity | Within class predictivity | Within class axis predictivity | Within class sample predictivity | Additional CVA dimensions | Analysis of Distance (AoD) | References
Rendered fromClass_separation.Rmdusingknitr::rmarkdown

Last update: 2026-09-02
Started: 2023-11-14

Biplots in 3D
PCA 3D biplot | Adding new samples | Adding new variables | Predicting samples | Predicting group means | Ellipses | CVA 3d biplot | CA 3D biplot
Rendered fromBiplots_in_3D.Rmdusingknitr::rmarkdown

Last update: 2024-11-05
Started: 2024-05-21

MDS
What is MDS | Regression biplot | Principal Coordinate Analysis (PCO) biplots | References
Rendered fromMDS.Rmdusingknitr::rmarkdown

Last update: 2024-07-04
Started: 2024-07-04

Readme and manuals

Help Manual

Help pageTopics
'+' composes with ggplot components and returns a plain ggplot, so 'bp |> gg_biplot() + labs(...)' still works.+.gg_biplot
Create alpha bagsalpha.bags
Use the Analysis of Distance (AoD) method to construct the biplotAoD
Calculate elements for the Analysis of Distance (AoD) biplotAoD.biplot
Format aesthetics for the biplot axesaxes
First step to create a new biplot with 'biplotEZ'biplot
biplotEZ: EZ-to-Use BiplotsbiplotEZ
Correspondence Analysis (CA) methodCA
CA biplotCA.biplot
Classification biplot methodclassification
classification biplotclassification.biplot
Classify samples into classesclassify
Format aesthetics for the category level pointsCLPs
Format aesthetics for the category level regionsCLRs
Perform Canonical Variate Analysis (CVA)CVA
Calculate elements for the CVA biplotCVA.biplot
Construct additional dimensions when the dimension of the canonical space is smaller than the dimension of the biplotCVAlowdim
Creates a kernel density in 1-dimensiondensity1D
Create a density in 2-dimensionsdensity2D
Concentration ellipses (kappa-ellipses)ellipses
Extended matching coefficientextended.matching.coefficient
Compute measures of fit for the biplot.fit.measures
ggplot2 plot method for biplotEZ objectsgg_biplot
Interpolate supplementary points and variables to add to the biplotinterpolate
Format the legend for the biplotlegend.type
Multiple Correspondence Analysis (MCA) methodMCA
MCA biplotMCA.biplot
Format aesthetics for the class or group meansmeans
Format aesthetics for the supplementary (new) biplot axesnewaxes
Format aesthetics for the supplementary (new) biplot samplesnewsamples
Perform Principal Components Analysis (PCA)PCA
Calculate elements for the PCA biplotPCA.biplot
Principal Coordinate Analysis (PCO) biplot methodPCO
PCO biplotPCO.biplot
Generic Plotting function of objects of class biplotplot.biplot
plot() on a gg_biplot re-displays the ggplotplot.gg_biplot
Predict samples to display on the biplotprediction
Generic print function for objects of class biplotprint.biplot
Printing a gg_biplot displays the stored ggplot, then (as base print.biplot does) reports any predictions - here with the actual predicted values, which base leaves to be read off the axes.print.gg_biplot
Reflect the biplot about a chosen axisreflect
Regression biplot methodregress
Regression biplotregress.biplot
Rotate the biplot a chosen amount of degreesrotate
Format aesthetics for the biplot samplessamples
Computes the square root of the Manhattan distance An example of a Euclidean embeddable distance metricsqrtManhattan
Generic summary function for objects of class biplotsummary.biplot
Applies primary or secondary approach to tiesties
Translate biplot axestranslate_axes