| Huberplot {PPtreeViz} | R Documentation |
Huber plot
Description
Huber plot for 2D data
Usage
Huberplot(origdata2D,origclass,PPmethod="LDA",weight=TRUE,r=1,
lambda=0.5,opt.proj=TRUE,UserDefFtn=NULL,...)
Arguments
origdata2D |
2-dimensional numerical data for Huber plot |
origclass |
class information vector of data |
PPmethod |
method for projection pursuit; "LDA", "PDA", "Lr", "GINI", "ENTROPY", and "UserDef" |
weight |
weight flag in LDA, PDA and Lr index |
r |
r in Lr index |
lambda |
lambda in PDA index |
opt.proj |
flag to show the best projection in the plot |
UserDefFtn |
User defined index function when PPmethod="UserDef" |
... |
arguments to be passed to methods |
Details
Draw Huber plot for 2-dimensional data with various PP indices and the histogram of the projected data onto the optimal projection to explore the behavior of the projection prsuit indices
References
Lee, EK., Cook, D., Klinke, S., and Lumley, T.(2005) Projection Pursuit for Exploratory Supervised Classification, Journal of Computational and Graphical Statistics, 14(4):831-846.
Examples
data(iris)
Huberplot(iris[,1:2],iris[,5],PPmethod="LDA")