Package 'PPtreeViz'

Title: Projection Pursuit Classification Tree Visualization
Description: Tools for exploring projection pursuit classification tree using various projection pursuit indexes.
Authors: Eun-Kyung Lee
Maintainer: Eun-Kyung Lee <[email protected]>
License: GPL (>= 2)
Version: 2.0.4
Built: 2025-01-06 04:01:55 UTC
Source: https://github.com/cran/PPtreeViz

Help Index

ENTROPY PPindex

Description

ENTROPY projection pursuit index

Usage

ENTROPYindex1D(origclass,origdata,proj)

Arguments

origclass

class information vector
origdata

data matrix without class information
proj

projection matrix

Details

Calculate ENTROPY projection pursuit index value

Examples

data(iris)
ENTROPYindex1D(iris[,5],as.matrix(iris[,1,drop=FALSE]))

GINI PPindex

Description

GINI projection pursuit index

Usage

GINIindex1D(origclass,origdata,proj)

Arguments

origclass

class information vector
origdata

data matrix without class information
proj

projection matrix

Details

Calculate GINI projection pursuit index value

Examples

data(iris)
GINIindex1D(iris[,5],as.matrix(iris[,1,drop=FALSE]))

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")

LDA PPindex

Description

LDA projection pursuit index

Usage

LDAindex(origclass,origdata,proj,weight=TRUE)

Arguments

origclass

class information vector
origdata

data matrix without class information
proj

projection matrix
weight

weight flag in LDA

Details

Calculate LDA projection pursuit index value

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)
LDAindex(iris[,5],as.matrix(iris[,1:4]))

PP optimization using LDA index

Description

PP optimization using LDA index

Usage

LDAopt(origclass,origdata,q=1,weight=TRUE,...)

Arguments

origclass

class information vector of data
origdata

data matrix without class information
q

dimension of projection vector
weight

weight flag in LDA index
...

arguments to be passed to methods

Details

Find the q-dimensional optimal projection using LDA projectin pursuit index

Value

indexbest maximum LDA index value

projbest optimal q-dimensional projection matrix

origclass original class information vector

origdata original data matrix without class information

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)
LDA.proj.result <- LDAopt(iris[,5],iris[,1:4])
LDA.proj.result$indexbest
LDA.proj.result$projbest

Lr PPindex

Description

Lr projection pursuit index

Usage

Lrindex(origclass,origdata,proj,weight=TRUE,r=1)

Arguments

origclass

class information vector
origdata

data matrix without class information
proj

projection matrix
weight

weight flag in Lr index
r

r in Lr index

Details

Calculate Lr projection pursuit index value

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)
Lrindex(iris[,5],as.matrix(iris[,1:4]),r=1)

PDA PPindex

Description

PDA projection pursuit index

Usage

PDAindex(origclass,origdata,proj,weight=TRUE,lambda=0.1)

Arguments

origclass

class information vector
origdata

data matrix without class information
proj

projection matrix
weight

weight flag in PDA
lambda

lambda in PDA index

Details

Calculate PDA projection pursuit index value

References

Lee, EK., Cook, D.(2010) A projection pursuit index for large p small n data, Statistics and Computing, 20:381-392.

Examples

data(iris)
PDAindex(iris[,5],as.matrix(iris[,1:4]),lambda=0.2)

PP optimization using PDA index

Description

PP optimization using PDA index

Usage

PDAopt(origclass,origdata,q=1,weight=TRUE,lambda=0.1,...)

Arguments

origclass

class information vector of data
origdata

data matrix without class information
q

dimension of projection vector
weight

weight flag in PDA index
lambda

lambda in PDA index
...

arguments to be passed to methods

Details

Find the q-dimensional optimal projection using PDA projectin pursuit index

Value

indexbest maximum PDA index value

projbest optimal q-dimensional projection matrix

origclass original class information vector

origdata original data matrix without class information

References

Lee, EK, Cook, D.(2010) A Projection Pursuit Index for Large p Small n Data, Statistics and Computing, 20:381-392.

Examples

data(iris)
PDA.proj.result <- PDAopt(iris[,5],iris[,1:4],weight=TRUE,q=2,lambda=0.1)
PDA.proj.result$indexbest
PDA.proj.result$projbest

PPtree plot

Description

projection pursuit classification tree plot

Usage

## S3 method for class 'PPtreeclass'
plot(
  x,
  font.size = 17,
  width.size = 1,
  main = "Projection Pursuit Classification Tree",
  sub = NULL,
  ...
)

Arguments

x

PPtreeclass object
font.size

font size of plot
width.size

size of eclipse in each node.
main

main title
sub

sub title
...

arguments to be passed to methods

Details

Draw projection pursuit classification tree with tree structure. It is modified from a function in party library.

References

Lee, EK(2017) PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees, Journal of Statistical Software <doi:10.18637/jss.v083.i08>

Examples

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

predict PPtree

Description

predict projection pursuit classification tree

Usage

PPclassify(Tree.result,test.data,Rule,true.class=NULL,...)

Arguments

Tree.result

PPtreeclass object
test.data

the test dataset
Rule

split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size
true.class

true class of test dataset if available
...

arguments to be passed to methods

Details

Predict class for the test set with the fitted projection pursuit classification tree and calculate prediction error.

Value

predict.class predicted class

predict.error number of the prediction errors

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)
n <- nrow(iris)
tot <- c(1:n)
n.train <- round(n*0.9)
train <- sample(tot,n.train)
test <- tot[-train]
Tree.result <- PPTreeclass(Species~.,data=iris[train,],"LDA")
PPclassify(Tree.result,iris[test,1:4],1,iris[test,5])

PPtree node 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)

Projection pursuit optimization

Description

PP optimization using various projection pursuit indices

Usage

PPopt(origclass,origdata,q=1,PPmethod="LDA",weight=TRUE,r=1,
             lambda=0.1,energy=0,cooling=0.999,TOL=0.0001,maxiter = 50000)

Arguments

origclass

class information vector
origdata

data matrix without class information
q

dimension of projection matrix
PPmethod

method for projection pursuit; "LDA", "PDA", "Lr", "GINI", and "ENTROPY"
weight

weight flag in LDA, PDA and Lr index
r

r in Lr index
lambda

lambda in PDA index
energy

energy parameter
cooling

cooling parameter
TOL

tolerance
maxiter

number of maximum iteration

Details

Find the q-dim optimal projection using various projectin pursuit indices with class information

Value

indexbest maximum LDA index value

projbest optimal q-dim projection matrix

origclass original class information vector

origdata original data matrix without class information

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)
PP.proj.result <- PPopt(iris[,5],as.matrix(iris[,1:4]))
PP.proj.result

PPopt visualization

Description

Visualize PPopt result

Usage

PPoptViz(PPoptOBJ)

Arguments

PPoptOBJ

PPoptim object. result from LDAopt, PDAopt, and PPopt

Details

Visualize the result of projection pursuit optimization

References

Lee, EK(2017) PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees, Journal of Statistical Software <doi:10.18637/jss.v083.i08>

Examples

data(iris)
PPoptViz(LDAopt(iris[,5],iris[,1:4],q=1))
PPoptViz(LDAopt(iris[,5],iris[,1:4],q=2))

Projection pursuit classification tree

Description

Construct the projection pursuit classification tree

Usage

PPTreeclass(formula,data, PPmethod="LDA",weight=TRUE,
                     r=1,lambda=0.1,energy=0,maxiter=50000,...)

Arguments

formula

an object of class "formula"
data

data frame
PPmethod

method for projection pursuit; "LDA", "PDA", "Lr", "GINI", and "ENTROPY"
weight

weight flag in LDA, PDA and Lr index
r

r in Lr index
lambda

lambda in PDA index
energy

parameter for the probability to take new projection
maxiter

maximum iteration number
...

arguments to be passed to methods

Details

Find tree structure using various projection pursuit indices of classification in each split.

Value

Tree.Struct tree structure of projection pursuit classification tree

projbest.node 1 dimensional optimal projections of each node split

splitCutoff.node cutoff values of each node split

origclass original class

origdata original data

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

predict PPtree

Description

predict projection pursuit classification tree

Usage

## S3 method for class 'PPtreeclass'
predict(object, newdata = NULL, Rule = 1, ...)

Arguments

object

a fitted object of class inheriting from "PP.Tree.class"
newdata

the test dataset
Rule

split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size
...

arguments to be passed to methods

Details

Predict class for the test set with the fitted projection pursuit classification tree and calculate prediction error.

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)
n <- nrow(iris)
tot <- c(1:n)
n.train <- round(n*0.9)
train <- sample(tot,n.train)
test <- tot[-train]
Tree.result <- PPTreeclass(Species~., data=iris[train,],"LDA")
predict(Tree.result)

Print PP.Tree.class result

Description

Print PP.Tree.class result

Usage

## S3 method for class 'PPtreeclass'
print(x, coef.print = FALSE, cutoff.print = FALSE, verbose = TRUE, ...)

Arguments

x

PPtreeclass object
coef.print

print projection coefficients in each node ifTRUE
cutoff.print

print cutoff values in each node if TRUE
verbose

print if TRUE, no output if FALSE
...

arguments to be passed to methods

Details

Print the projection pursuit classification tree result

References

Lee, EK(2017) PPtreeViz: An R Package for Visualizing Projection Pursuit Classification Trees, Journal of Statistical Software <doi:10.18637/jss.v083.i08>

Examples

data(iris)
Tree.result <- PPTreeclass(Species~.,data = iris,"LDA")
Tree.result
print(Tree.result,coef.print=TRUE,cutoff.print=TRUE)