Provides tools for creating 2D and 3D biplots of multivariate data based on principal components analysis, together with diagnostics for reduction quality and enhanced visualization of variables and objects.
bpca is an R package for biplot analysis based on principal components.
hj, sqrt, jk, gh) for different interpretations.xtable(bpca(...)) and print(..., type = "html").Install from CRAN:
install.packages("bpca")
Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("jcfaria/bpca")
library(bpca)
# Example with the classic iris dataset
bp <- bpca(iris[-5], d = 1:2)
plot(
bp,
var.col = "blue",
var.factor = 2,
main = "Biplot - Iris Dataset"
)
For more complete examples, see:
demo("bpca", package = "bpca")vignette("bpca-overview", package = "bpca")/R: Core computational and plotting functions./data: Example datasets shipped with the package./demo: Runnable demos illustrating usage./man: Documentation (.Rd files)./inst: Extra package materials (e.g. citations)./vignettes: Vignettes and tutorials.Contributions are welcome. Open an Issue or submit a Pull Request on github.com/jcfaria/bpca with:
Developed by:
Faria, J. C.; Allaman, I. B.
Universidade Estadual de Santa Cruz - UESC
Departamento de Ciências Exatas - DCEX
Ilhéus - Bahia - Brasil
Demétrio, C. G. B.
Universidade de São Paulo - USP
Escola Superior de Agricultura Luiz de Queiroz - ESALQ
Piracicaba - São Paulo - Brasil