biplot {biplotEZ}

First step to create a new biplot with biplotEZ

Description

This function produces a list of elements to be used when producing a biplot, which provides a useful data analysis tool and allows the visual appraisal of the structure of large data matrices. Biplots are the multivariate analogue of scatter plots. They approximate the multivariate distribution of a sample in a few dimensions and they superimpose on this display representations of the variables on which the samples are measured.

Usage

biplot(data, classes = NULL, group.aes = NULL, center = TRUE, scaled = FALSE,
Title = NULL)

Arguments

data

a dataframe or matrix containing all variables the user wants to analyse.

classes

vector identifying class membership.

group.aes

vector identifying groups for aesthetic formatting.

center

logical, indicating whether data should be column centered, with default TRUE.

scaled

logical, indicating whether data should be standardized to unit column variances, with default FALSE.

Title

title of the biplot to be rendered, enter text in " ".

Value

A list with the following components is available:

X

matrix of the centered and scaled numeric variables.

Xcat

data frame of the categorical variables.

raw.X

original data.

center

TRUE or FALSE, whether X is centered.

scaled

TRUE or FALSE, wether X is scaled.

means

vector of means for each numeric variable.

sd

vector of standard deviations for each numeric variable.

group.aes

vector of category levels for the grouping variable. This is to be used for colour, pch and cex specifications.

Title

title of the biplot to be rendered

References

Gabriel, K.R. (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58(3):453–467.

Gower, J., Gardner-Lubbe, S. & Le Roux, N. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Chichester, England: John Wiley & Sons Ltd.

Gower, J.C. & Hand, D.J.(1996, ISBN: 0-412-71630-5) Biplots. London: Chapman & Hall.

Examples

biplot(data = iris)
# create a PCA biplot
biplot(data = iris) |> PCA() |> plot()