Package 'wideRhino'

Title: High-Dimensional Methods via Generalised Singular Decomposition
Description: Construct a Canonical Variate Analysis Biplot via the Generalised Singular Value Decomposition, for cases when the number of samples is less than the number of variables. For more information on these biplots, see Ganey, R., & Gardner-Lubbe, S. (2026) <doi:10.1007/s10260-025-00831-y>.
Authors: Raeesa Ganey [aut, cre] (ORCID: <https://orcid.org/0009-0008-6973-0999>)
Maintainer: Raeesa Ganey <[email protected]>
License: MIT + file LICENSE
Version: 1.1.0
Built: 2026-05-15 16:06:53 UTC
Source: https://github.com/raeesaganey91/widerhino

Help Index

Calibrate axis

Description

Calibrate axis

Usage

.calibrate.axis(
  j,
  Xhat,
  means,
  sd,
  axes.rows,
  ax.which,
  ax.tickvec,
  ax.orthogxvec,
  ax.orthogyvec
)

Arguments

j

j
Xhat

Xhat
means

means
sd

sd
axes.rows

axes.rows
ax.which

ax.which
ax.tickvec

ax.tickvec
ax.orthogxvec

ax.orthogxvec
ax.orthogyvec

ax.orothogyvec

Value

Calibrated axes

Provide axes coordinates

Description

Provide axes coordinates

Usage

axes_coordinates(bp, which.var = 1:bp$p)

Arguments

bp

Object
which.var

which variable(s) to find coordinates

Value

Axes coordinates

Plot the CVA biplot

Description

Plot the CVA biplot

Usage

CVAbiplot(x, which.var = 1:x$p, var.label = TRUE, group.col = NULL)

Arguments

x

Object from CVAgsvd
which.var

which variable to display on the biplot
var.label

whether to display label for variable name
group.col

vector of colours for the groups in the data

Value

A CVA biplot based on the GSVD

Examples

data(sim_data)
CVAgsvd(X=sim_data[,2:301],group = sim_data[,1])|>
CVAbiplot(group.col=c("tan1","darkcyan","darkslateblue"),which.var = 1:10)

CVA using the GSVD

Description

Compute canonical variate analysis using the generalised singular value decomposition when number of variables (p) is larger than the number of samples (n).

Usage

CVAgsvd(X, group)

Arguments

X

n x p data matrix
group

vector of size n showing the groups

Details

If p < n, then the solution defaults to the standard canonical variate analysis.

Value

An object with components of a CVA biplot

Examples

CVAgsvd(X=iris[,1:4],group = iris[,5]) |>
CVAbiplot(group.col = c("orange","red","pink"))

Get GSVD Get the components of the GSVD decomposition

Description

Get GSVD Get the components of the GSVD decomposition

Usage

get.GSVD(A, B)

Arguments

A

Matrix A
B

Matrix B

Value

Returns components from the GSVD decomposition

Simulated Data

Description

Class

Group variable: 0, 1, 2

X1

Variable 1

...

X300

Variable 300

Format

A data set with 100 rows and 301 columns

Source

simulated data