plot.clusterpro: Plots for Unsupervised Data Visualization

In kogalur/varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority (VarPro)

Plots for Unsupervised Data Visualization

Description

Plots for unsupervised data visualization

Usage

## S3 method for class 'clusterpro'
plot(x, xvar.names, shrink=TRUE,
   col=TRUE, col.names=NULL, sort=TRUE, cex=FALSE, breaks=10, ... )

Arguments

x	clusterpro object returned from a previous call to clusterpro.
xvar.names	Names (or integer indices) of the x-variables to plot. Defaults to all variables.
shrink	Logical. If TRUE, shrinks the release variable to zero.
col	Logical. If TRUE, colors the points in the plot.
col.names	Variable used to color the plots. Defaults to the release variable. Can also be an integer index.
sort	Logical. If TRUE, sorts plots by variable importance.
cex	Numeric value to scale point size.
breaks	Number of breaks used when mapping colors to points.
...	Additional arguments passed to plot.

Details

Generates a two-dimensional visualization using UMAP applied to the enhanced data corresponding to a release variable. This provides a low-dimensional representation of the clustered structure derived from the rule-based transformation of the original data.

Author(s)

Hemant Ishwaran

References

McInnes L., Healy J. and Melville J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv e-prints.

See Also

clusterpro

kogalur/varPro documentation built on June 2, 2025, 6:24 a.m.