CVAgsvd: CVA using the GSVD
In wideRhino: High-Dimensional Methods via Generalised Singular Decomposition
CVAgsvd R Documentation
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"))
wideRhino documentation built on July 2, 2026, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| CVAgsvd | R Documentation |
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"))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.