Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (17.0%) to scientific vocabulary
Keywords
biplots
multivariate-data
Last synced: 6 months ago
·
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Repository
An R Package for Reactive Calibrated Axes PCA Biplots
Basic Info
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 0
Topics
biplots
multivariate-data
Created over 2 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Changelog
Contributing
License
Codemeta
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# bipl5
[](https://github.com/RuanBuys/bipl5/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/RuanBuys/bipl5?branch=main)
[](https://github.com/RuanBuys/bipl5/actions?query=workflow%3Apkgcheck)
The goal of bipl5 is to provide a modern take on PCA biplots with calibrated axes. The biplots are rendered in HTML via the plotly graphing library, upon which custom JavaScript code is appended to make the plot reactive. The traditional biplot view is also extended through an algorithm that translates the axes out of the data centroid, decluttering the view. In addition to the former, inter-class kernel densities are superimposed on the axes to give an indication of the data spread across the variables.
## Installation
You can install the latest version of bipl5 from CRAN with:
```{r}
install.packages("bipl5")
library(bipl5)
```
## Example: Traditional PCA biplot
This is a basic example of constructing a traditional PCA biplot with calibrated axes and vector loadings. In the literature, a square is often drawn around the biplot purely for aesthetic reasons. This approached was modified by constructing a circle which bounds the plot:
```{r example}
PCAbiplot(iris[,-5],group=iris[,5])
```
On the rendered HTML file there are reactive events embedded on the plot, such as changing the principal components used for the scaffolding. More detailed information can be obtained by reading the help documentation
```{r cars}
?PCAbiplot
```
## Example: Automated Orthogonal Parallel Translation of the axes
The cluttering in the centroid of the biplot can be relieved by the Orthogonal Parallel Translation of the axes out of the data centroid in such a way that the correlation structure among variables is retained. This is done with the following:
```{r}
x<-PCAbiplot(iris[,-5],group=iris[,5])
x |> TDAbiplot()
```
Owner
- Login: RuanBuys
- Kind: user
- Repositories: 1
- Profile: https://github.com/RuanBuys
CodeMeta (codemeta.json)
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