PPclassNodeViz: PPtree node visualization

In PPtreeViz: Projection Pursuit Classification Tree Visualization

Description

Visualization tools to explore each node of PPtree

Usage

PPclassNodeViz(PPclassOBJ,node.id,Rule,legend,std,image,diff.prop)

Arguments

PPclassOBJ	PPregclass object
node.id	node ID
Rule	cutoff rule
legend	flag to represent legend in the plot. Default value is TRUE
std	flag to standardize data before drawing plot
image	flag to draw image plot of correlation matrix
diff.prop	percentage of number of variables with significant differences and shown in red in the bar chart style means

Details

For the inner node, four plots are provided - the bar chart style plot with projection pursuit coefficients of each variable, the histogram of the projected data, the bar chart style plots with means of each variables for the left and the right group, and the image plot of correlations.

References

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection Pursuit Classification Tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

data(iris)
Tree.result <- PPTreeclass(Species~., data = iris,"LDA")
Tree.result
PPclassNodeViz(Tree.result,1,1)

Example output

Loading required package: gridExtra
Loading required package: ggplot2
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
============================================================= 
Projection Pursuit Classification Tree result 
=============================================================

1) root
   2)* proj1*X < cut1  ->  "setosa"
   3)  proj1*X >= cut1
      4)* proj2*X < cut2  ->  "virginica"
      5)* proj2*X >= cut2  ->  "versicolor"

Error rates of various cutoff values 
-------------------------------------------------------------
           Rule1 Rule2  Rule3  Rule4 Rule5 Rule6  Rule7  Rule8
error.rate  0.02  0.02 0.0267 0.0267  0.02  0.02 0.0267 0.0267
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

PPtreeViz documentation built on Dec. 6, 2019, 9:07 a.m.