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biplotEZ

The goal of biplotEZ is to provide users an EZ-to-use platform for visually representing their data with biplots. Currently, this package includes principal component analysis (PCA) and canonical variate analysis (CVA) biplots. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation.

Installation

You can install the development version of biplotEZ like this:

library(devtools)
devtools::install_github("MuViSU/biplotEZ")

Example

This is a basic example which shows you how to construct a PCA biplot:

library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()

While the PCA biplot provides a visual representation of the overall data set, optimally representing the variance in 1, 2 or 3 dimensions, the CVA biplot aims to optimally separate specified groups in the data. This is a basic example which shows you how to construct a CVA biplot:

biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()

An over-the-top example of changing all the formatting and adding all the bells and whistles:

biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor

CA biplot

The default CA biplots represents row principal coordinates with a call such as:

biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.

To change to row standard coordinates use a call such as:

biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.

Regression biplot

With the function regress linear regression biplot axes can be fitted to a biplot

out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()

Report Bugs and Support

If you encounter any issues or have questions, please open an issue on the GitHub repository.

About

User-friendly biplots with R

www.biplotEZ.org

Resources

Readme
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10 stars

Watchers

1 watching

Forks

3 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
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Readme
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biplotEZ
The goal of biplotEZ is to provide users an EZ-to-use platform for
visually representing their data with biplots. Currently, this package
includes principal component analysis (PCA) and canonical variate
analysis (CVA) biplots. This is accompanied by various formatting
options for the samples and axes. Alpha-bags and concentration ellipses
are included for visual enhancements and interpretation.
Installation
You can install the development version of biplotEZ like this:
library(devtools)
devtools::install_github("MuViSU/biplotEZ")
Example
This is a basic example which shows you how to construct a PCA biplot:
library(biplotEZ)
biplot (iris[,1:4], Title="Test PCA biplot") |> PCA() |> plot()
While the PCA biplot provides a visual representation of the overall
data set, optimally representing the variance in 1, 2 or 3 dimensions,
the CVA biplot aims to optimally separate specified groups in the data.
This is a basic example which shows you how to construct a CVA biplot:
biplot (iris[,1:4], Title="Test CVA biplot") |> CVA(classes=iris[,5]) |> plot()
An over-the-top example of changing all the formatting and adding all
the bells and whistles:
biplot (iris[,1:4], group.aes=iris[,5]) |> PCA() |> samples(col="gold", pch=15) |>
axes(which=2:3, col="cyan", label.cex=1.2, tick.col="blue", tick.label.col="purple") |>
alpha.bags (alpha=c(0.5,0.75,0.95), which=3, col="red", lty=1:3, lwd=3) |>
ellipses(alpha=0.9, which=1:2, col=c("green","olivedrab")) |>
legend.type(bags=TRUE, ellipses=TRUE) |>
plot()
#> Computing 0.5 -bag for virginica #> Computing 0.75 -bag for virginica #> Computing 0.95 -bag for virginica #> Computing 2.15 -ellipse for setosa #> Computing 2.15 -ellipse for versicolor
CA biplot
The default CA biplots represents row principal coordinates with a call
such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA() |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
To change to row standard coordinates use a call such as:
biplot(HairEyeColor[,,2], center=FALSE) |> CA(variant="Stand") |> samples(col=c("magenta","purple"), pch=c(15,18)) |> plot()
#> Warning: The ggplot2 engine does not yet support CA maps; falling back to base graphics.
Regression biplot
With the function regress linear regression biplot axes can be fitted
to a biplot
out<- biplot(iris) |> PCO(dist.func=sqrtManhattan) biplot(iris) |> regress(out$Z) |> plot()
Report Bugs and Support
If you encounter any issues or have questions, please open an issue on
the GitHub repository.
About
User-friendly biplots with R
www.biplotEZ.org
Resources
Readme
Activity
Stars
10 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages