Classification
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Classification Tree Model
Document Information
Package base version 2.7.1
11/6/2008
Project 5: Statistical Learning with Multi-Scale Cardiovascular Data
Contact email: yqin@jhu.edu
CardioVascular Research Grid
Johns Hopkins University
Introduction to classification tree model
A classification tree is the discrete version of a decision tree. A decision tree is a predictive model; that is, a mapping from data observations to conclusions about its dependent variable.
In a classification tree, the dependent variable is discrete, usually binary. By splitting dataset to more pure ones in terms of the dependent variable, the model can find the most effective variables by which it uses for dividing. When certain purity is reached, the model stops splitting, then we call the stop point a leaf. Each splitting is called branches. In the tree structures, leaves represent classifications and branches represent conjunctions that lead to those classifications.
Now, let’s look at an example. Suppose Y is dependent variable (binary), X1, X2, X3, X4 and X5 are independent variables which are used to predict Y.
First, the model tries to find the best variable X among X1 to X5 and critical value A, so that when dividing the whole dataset to two datasets based on the criteria X>=A or X<A, the purities of two new datasets are the highest. Let’s say, X1 is such a variable. Then we have:
Now repeat the procedure to each sub dataset (nod) until every sub dataset reaches certain pre-specified purity. So we have a list of variables which we use to divide the dataset. We also have a series of critical values (A, B, C…) which correspond to each splitting. In the end we have diagram like this.
At each leaf, the purity of Y is above certain level, and we have posterior distributions of Y given the series of conditions (e.g. X1<=A, X2>=C and so on). So we can estimate Y based on X1 to X5.
Please note that not all the variables have to be used in the analysis. The program only picks the best variables to split the dataset. It is very possible that several variables are not picked. For example, in the diagram above, X3 is not used anywhere in the analysis.
Comparison variables
Besides the ordinary classification tree analysis, we also want to take comparison into consideration. For example, we want to include the criterion such as X1>=X2 or X1<X2. To solve this, we generate a indicator Ind1, such that, when Ind1=-1, X1<=X2 and ind1=+1 otherwise. So the variable list becomes X1...X5 and Ind1. We do the tree classification analysis again with original variables and new indicators. When an indicator is used in the model, it means the criteria is applied to split the dataset.
Instruction for the tree.revised() function
The code in the end of this article creates a function especially for this tree analysis. The function only needs you to input the dataset’s name and make sure each column of this dataset has a name.
Once the dataset name is input. The function will guide you through the procedure of selecting the dependent variable, independent variables and comparison variables (the ones you want to compare between).
Now let’s look at an example.
data(cpus, package="MASS") tree.revised(iris)
In the iris dataset, there are 5 columns, namely, “Sepal Length”, ”Sepal Width”, ”Petal Length”, ”Petal Width”, ”Species”
Once hit “enter”, the function will generate three windows look like below.
Now choose the variables by checking the boxes next to them.
For dependent variables, just choose one, which has to be factor format or numeric.
For the comparison variables, once you check some variables, all these variables will be compared in all possible pairs. So if you select n variables for comparison, you will have n(n-1) indicators (i.e. n(n-1) comparisons). Each indicator equals –1 if, in the pair, the one appears in the top is smaller than the one appears in the bottom, and equals +1 otherwise.
For the independent variables, it doesn’t have to include all the comparison variables. There can be some variables that are included in comparison but not in individual variables list.
Once the selection is done, hit “OK”, the program will confirm with you selectionyou're your selection is not complete, then the program will remind you which part is missing. If everything is fine, then the output is displayed in the R console window.
node), split, n, deviance, yval, (yprob)
* denotes terminal node
1) root 150 329.60 setosa ( 0.3333 0.3333 0.3333 )
2) Sepal.Width_Petal.Length < 0 100 138.60 versicolor ( 0.0000 0.5000 0.5000 )
4) Sepal.Length < 6.15 45 50.05 versicolor ( 0.0000 0.7556 0.2444 )
8) Sepal.Length < 5.75 24 18.08 versicolor ( 0.0000 0.8750 0.1250 ) *
9) Sepal.Length > 5.75 21 27.91 versicolor ( 0.0000 0.6190 0.3810 ) *
5) Sepal.Length > 6.15 55 66.33 virginica ( 0.0000 0.2909 0.7091 )
10) Sepal.Length < 7.05 43 56.77 virginica ( 0.0000 0.3721 0.6279 ) *
11) Sepal.Length > 7.05 12 0.00 virginica ( 0.0000 0.0000 1.0000 ) *
3) Sepal.Width_Petal.Length > 0 50 0.00 setosa ( 1.0000 0.0000 0.0000 ) *
In the output, “Sepal.Width_Petal.Length” is an indicator whose name contains two comparison variables names and connects them with a “_”. The numbers in the parentheses are the posterior distribution of Y, which is Species in this case. The asterisk in the end indicates that this is a leaf.
So in this case, the diagram looks like this:
R code for tree model
Commented Code
Click Here to download a zipped folder with the code.
rm(list=ls())
tree.revised<-function(d)
{
library(tree)
require(tcltk)
###############################
# dependent variable
################################
# 0. choose dependent variable
################################
# 0.1 create dependent variable list
CV.list_dep <- c()
CV.name.list_dep <- c()
# List all the variables in data.frame d, and record their names column numbers.
# Store them to CV.list_dep and CV.name.list_dep.
for (i in seq(1,dim(d)[2]))
{
CV.list_dep<-c(CV.list_dep,i)
CV.name.list_dep<-c(CV.name.list_dep,names(d)[i])
}
names(CV.list_dep)<-CV.name.list_dep
################################
# 0.2 create choosing dependent variable module
# Create a tk object level. i.e. create a window.
tt_dep <- tktoplevel()
# Change the title of the window to "Dependent variable".
tkwm.title(tt_dep,"Dependent variable")
# Create a list to store checkboxes for dependent variable selection.
cb_dep<-list()
# Assign each variables a check box.
for (i in 1:length(CV.list_dep))
{
checkbox_dep <- tkcheckbutton(tt_dep)
#print(i)
cb_dep[[i]] <- checkbox_dep
}
# Create a list to store selection of dependent variable.
cbValue_dep <- list()
# Assign "cbValue_dep" all "0"s for intial values.
for (i in 1:length(CV.list_dep))
{
cbValue_dep[[i]] <- tclVar("0")
}
# Associate each checkbox with each of their selections.
for (i in 1:length(CV.list_dep))
{
#print(i)
tkconfigure(cb_dep[[i]],variable=cbValue_dep[[i]])
}
# Show the guidence in the window.
tkgrid(tklabel(tt_dep,text="Please choose the dependent variable:"))
# Show the variable names and checkboxes in the window.
for (i in 1:length(CV.list_dep))
{
#print(i)
tkgrid(tklabel(tt_dep,text=CV.name.list_dep[i]),cb_dep[[i]])
}
# Create an indicator recording whether the "OK" in the window has been clicked.
# if not clicked, ind.OnOk_dep equals to 0. If clicked, the indicator becomes 1.
ind.OnOk_dep <- 0
# create a function to execute once the "OK" is clicked.
OnOK_dep <- function()
{
# Create a vector to store the varialbe selection.
cbVal_dep <<- c()
# Pass the value from cbValue_dep to cbVal_dep. And force them to be numeric.
for (i in 1:length(CV.list_dep))
{
cbVal_dep[i] <<- as.numeric(tclvalue(cbValue_dep[[i]]))
}
# Close the window.
tkdestroy(tt_dep)
# Create a vector to store the names of the selected varialbes.
cbCharacter_dep <<- CV.name.list_dep[cbVal_dep==1]
#print(cbVal_dep)
#print(cbCharacter_dep)
# Create a message (a string) to confirm with user.
msg_dep="Your choices for dependent variable is:"
# Complete the message by adding the name of selected variable to the string
for (i in 1:length(cbCharacter_dep))
{
msg_dep=paste(msg_dep,cbCharacter_dep[i],sep=" ")
}
# Show the message by pop up a new window.
tkmessageBox(message=msg_dep)
# Detect whether other selections (selections for comparison variables and
# individual varialbes) have been made. "ind.OnOk" and "ind.OnOk_indi" are
# indicators of whether the selections for comparison and individual variables
# are completed.
# If complete, do the tree analysis.
if ((ind.OnOk == 0)|(ind.OnOk_indi == 0))
{
# If not complete, pop up a reminder message
tkmessageBox(message="Please choose comparison and individual variables")
}
else
{
# If complete, store the column number of dependent variable to dep.var.
dep.var=(1:dim(d)[2])[cbVal_dep==1]
# Store the column numbers of individual variables to CV.input.list.
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
# Store the column numbers of comparison variables to comparison.CV.list.
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
# Do the tree analysis, and print the output.
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
# Change the indictor for dependent variable to 1.
ind.OnOk_dep <<- 1
}
# Create a botton which once get clicked, the OnOK_dep will be executed.
OK.but_dep <- tkbutton(tt_dep,text="OK",command=OnOK_dep)
# Show the botton in the window.
tkgrid(OK.but_dep)
tkfocus(tt_dep)
################################
# 1. choose variables to create comparison indicators
################################
# 1.1 create comparison variable list
# Use the same variable list for the selection of comparison variables.
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 1.2 create choosing variables module
# same as the comments for dependent variable selection.
tt <- tktoplevel()
tkwm.title(tt,"Comparison variable")
cb<-list()
for (i in 1:length(CV.list))
{
checkbox <- tkcheckbutton(tt)
#print(i)
cb[[i]] <- checkbox
}
cbValue <- list()
for (i in 1:length(CV.list))
{
cbValue[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb[[i]],variable=cbValue[[i]])
}
tkgrid(tklabel(tt,text="Please choose the variables you want to compare:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt,text=CV.name.list[i]),cb[[i]])
}
ind.OnOk <- 0
OnOK <- function()
{
cbVal <<- c()
for (i in 1:length(CV.list))
{
cbVal[i] <<- as.numeric(tclvalue(cbValue[[i]]))
}
tkdestroy(tt)
cbCharacter <<- CV.name.list[cbVal==1]
print(cbVal)
print(cbCharacter)
msg="Your choices for comparison variables are:"
for (i in 1:length(cbCharacter))
{
msg=paste(msg,cbCharacter[i],sep=" ")
}
tkmessageBox(message=msg)
if ((ind.OnOk_dep == 0)|(ind.OnOk_indi == 0))
{
tkmessageBox(message="Please choose dependent and individual variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk <<- 1
}
OK.but <- tkbutton(tt,text="OK",command=OnOK)
tkgrid(OK.but)
tkfocus(tt)
################################
# 2. choose variables for individual variables
################################
# 2.1 create individual variable list
# Use the same variable list for the selection of comparison variables.
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 2.2 create individual variables module
# same as the comments for dependent variable selection.
tt_indi <- tktoplevel()
tkwm.title(tt_indi,"Individual variable")
cb_indi<-list()
for (i in 1:length(CV.list))
{
checkbox_indi <- tkcheckbutton(tt_indi)
#print(i)
cb_indi[[i]] <- checkbox_indi
}
cbValue_indi <- list()
for (i in 1:length(CV.list))
{
cbValue_indi[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb_indi[[i]],variable=cbValue_indi[[i]])
}
tkgrid(tklabel(tt_indi,text="Please choose the individual variables you want to include:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt_indi,text=CV.name.list[i]),cb_indi[[i]])
}
ind.OnOk_indi <- 0
OnOK_indi <- function()
{
cbVal_indi <<- c()
for (i in 1:length(CV.list))
{
cbVal_indi[i] <<- as.numeric(tclvalue(cbValue_indi[[i]]))
}
tkdestroy(tt_indi)
cbCharacter_indi <<- CV.name.list[cbVal_indi==1]
print(cbVal_indi)
print(cbCharacter_indi)
msg_indi="Your choices for individual variables are:"
for (i in 1:length(cbCharacter_indi))
{
msg_indi=paste(msg_indi,cbCharacter_indi[i],sep=" ")
}
tkmessageBox(message=msg_indi)
if ((ind.OnOk_dep == 0)|(ind.OnOk == 0))
{
tkmessageBox(message="Please choose dependent and comparison variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk_indi <<- 1
}
OK.but_indi <- tkbutton(tt_indi,text="OK",command=OnOK_indi)
tkgrid(OK.but_indi)
tkfocus(tt_indi)
}
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
# Create a function "tree.comparison" to be used in the analysis.
tree.comparison <- function(dep.var,CV.input.list,comparison.CV.list,d)
{
# Assign the names for comparison.CV.list.
names(comparison.CV.list)=names(d)[comparison.CV.list]
# Create a separate variable to store the names of comparison variables.
comparison.CV.name.list=names(comparison.CV.list)
# Assign k with intial value of 0.
k=0
# For each pair of comparison, we add a column in the dataset. We use
# double loops to list all possible pairs.
for (i in 1:(length(comparison.CV.list)-1))
{
for (j in ((i+1):length(comparison.CV.list)))
{
# increase k by one for each pair.
k=k+1
# Store the column numbers of two comparison variables in a pair
col1 <- comparison.CV.list[i]
col2 <- comparison.CV.list[j]
# Generate the indicator for this pair. It equals to -1 if the
# first variable is smaller than the second one, and 1 otherwise.
ind=ifelse(d[,col1]<d[,col2],-1,1)
# If this the first pair, then create a new dataset c. If not, then
# add a column into c.
if (k==1)
{
c=ind
c=as.data.frame(c)
}
else
{
c=cbind(c,ind)
}
#print(comparison.CV.name.list[i])
#print(comparison.CV.name.list[j])
# Assign names for these indicator. The name contains two variable names and
# connect them with a "_"
ind.name=paste(comparison.CV.name.list[i],"_",comparison.CV.name.list[j],sep="")
names(c)[dim(c)[2]]=ind.name
}
}
# Creata a new data frame with dependent variable, individual variables and comparison variables.
e <- as.data.frame(cbind(d[,dep.var],d[,CV.input.list],c))
# Assign names for e.
names(e)=c(names(d)[c(dep.var,CV.input.list)],names(c))
#print(e)
# Do the tree analysis using the tree() in R.
tree.ltr <- tree(e[,1]~.,e[,-1])
# Return the analysis output.
return(tree.ltr)
}
##############################################
# Simulation example
# Generate a 100 by 10 matrix with every number drawn from a standard normal distribution.
x=matrix(rnorm(10000),100,10)
# create indicators for x.
a1=ifelse(x[,1]>x[,2],1,-1)
a2=ifelse(x[,2]>x[,3],1,-1)
a3=ifelse(x[,3]>x[,4],1,-1)
# Create individual variables.
a4=x[,4]
a5=x[,5]
# Create Y based on the simulated data.
y=ifelse((10*a1+5*a2+3*a3+10*a4)>0,1,0)
# Create the whole dataset used in the function
ddd=cbind(y,x)
# Convert the dataset to data frame.
ddd=as.data.frame(ddd)
# Assign names for all the columns in ddd.
names(ddd)=c("y","x1","x2","x3","x4","x5","x6","x7","x8","x9","x10")
# Do the tree analysis.
tree.revised(ddd)
Uncommented Code
Click Here to download a zipped folder with the code.
rm(list=ls())
tree.revised<-function(d)
{
library(tree)
require(tcltk)
###############################
# dependent variable
################################
# 0. choose dependent variable
################################
# 0.1 create dependent variable list
CV.list_dep <- c()
CV.name.list_dep <- c()
# List all the variables in data.frame d, and record their names column numbers.
# Store them to CV.list_dep and CV.name.list_dep.
for (i in seq(1,dim(d)[2]))
{
CV.list_dep<-c(CV.list_dep,i)
CV.name.list_dep<-c(CV.name.list_dep,names(d)[i])
}
names(CV.list_dep)<-CV.name.list_dep
################################
# 0.2 create choosing dependent variable module
# Create a tk object level. i.e. create a window.
tt_dep <- tktoplevel()
# Change the title of the window to "Dependent variable".
tkwm.title(tt_dep,"Dependent variable")
# Create a list to store checkboxes for dependent variable selection.
cb_dep<-list()
# Assign each variables a check box.
for (i in 1:length(CV.list_dep))
{
checkbox_dep <- tkcheckbutton(tt_dep)
#print(i)
cb_dep[[i]] <- checkbox_dep
}
# Create a list to store selection of dependent variable.
cbValue_dep <- list()
# Assign "cbValue_dep" all "0"s for intial values.
for (i in 1:length(CV.list_dep))
{
cbValue_dep[[i]] <- tclVar("0")
}
# Associate each checkbox with each of their selections.
for (i in 1:length(CV.list_dep))
{
#print(i)
tkconfigure(cb_dep[[i]],variable=cbValue_dep[[i]])
}
# Show the guidence in the window.
tkgrid(tklabel(tt_dep,text="Please choose the dependent variable:"))
# Show the variable names and checkboxes in the window.
for (i in 1:length(CV.list_dep))
{
#print(i)
tkgrid(tklabel(tt_dep,text=CV.name.list_dep[i]),cb_dep[[i]])
}
# Create an indicator recording whether the "OK" in the window has been clicked.
# if not clicked, ind.OnOk_dep equals to 0. If clicked, the indicator becomes 1.
ind.OnOk_dep <- 0
# create a function to execute once the "OK" is clicked.
OnOK_dep <- function()
{
# Create a vector to store the varialbe selection.
cbVal_dep <<- c()
# Pass the value from cbValue_dep to cbVal_dep. And force them to be numeric.
for (i in 1:length(CV.list_dep))
{
cbVal_dep[i] <<- as.numeric(tclvalue(cbValue_dep[[i]]))
}
# Close the window.
tkdestroy(tt_dep)
# Create a vector to store the names of the selected varialbes.
cbCharacter_dep <<- CV.name.list_dep[cbVal_dep==1]
#print(cbVal_dep)
#print(cbCharacter_dep)
# Create a message (a string) to confirm with user.
msg_dep="Your choices for dependent variable is:"
# Complete the message by adding the name of selected variable to the string
for (i in 1:length(cbCharacter_dep))
{
msg_dep=paste(msg_dep,cbCharacter_dep[i],sep=" ")
}
# Show the message by pop up a new window.
tkmessageBox(message=msg_dep)
# Detect whether other selections (selections for comparison variables and
# individual varialbes) have been made. "ind.OnOk" and "ind.OnOk_indi" are
# indicators of whether the selections for comparison and individual variables
# are completed.
# If complete, do the tree analysis.
if ((ind.OnOk == 0)|(ind.OnOk_indi == 0))
{
# If not complete, pop up a reminder message
tkmessageBox(message="Please choose comparison and individual variables")
}
else
{
# If complete, store the column number of dependent variable to dep.var.
dep.var=(1:dim(d)[2])[cbVal_dep==1]
# Store the column numbers of individual variables to CV.input.list.
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
# Store the column numbers of comparison variables to comparison.CV.list.
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
# Do the tree analysis, and print the output.
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
# Change the indictor for dependent variable to 1.
ind.OnOk_dep <<- 1
}
# Create a botton which once get clicked, the OnOK_dep will be executed.
OK.but_dep <- tkbutton(tt_dep,text="OK",command=OnOK_dep)
# Show the botton in the window.
tkgrid(OK.but_dep)
tkfocus(tt_dep)
################################
# 1. choose variables to create comparison indicators
################################
# 1.1 create comparison variable list
# Use the same variable list for the selection of comparison variables.
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 1.2 create choosing variables module
# same as the comments for dependent variable selection.
tt <- tktoplevel()
tkwm.title(tt,"Comparison variable")
cb<-list()
for (i in 1:length(CV.list))
{
checkbox <- tkcheckbutton(tt)
#print(i)
cb[[i]] <- checkbox
}
cbValue <- list()
for (i in 1:length(CV.list))
{
cbValue[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb[[i]],variable=cbValue[[i]])
}
tkgrid(tklabel(tt,text="Please choose the variables you want to compare:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt,text=CV.name.list[i]),cb[[i]])
}
ind.OnOk <- 0
OnOK <- function()
{
cbVal <<- c()
for (i in 1:length(CV.list))
{
cbVal[i] <<- as.numeric(tclvalue(cbValue[[i]]))
}
tkdestroy(tt)
cbCharacter <<- CV.name.list[cbVal==1]
print(cbVal)
print(cbCharacter)
msg="Your choices for comparison variables are:"
for (i in 1:length(cbCharacter))
{
msg=paste(msg,cbCharacter[i],sep=" ")
}
tkmessageBox(message=msg)
if ((ind.OnOk_dep == 0)|(ind.OnOk_indi == 0))
{
tkmessageBox(message="Please choose dependent and individual variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk <<- 1
}
OK.but <- tkbutton(tt,text="OK",command=OnOK)
tkgrid(OK.but)
tkfocus(tt)
################################
# 2. choose variables for individual variables
################################
# 2.1 create individual variable list
# Use the same variable list for the selection of comparison variables.
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 2.2 create individual variables module
# same as the comments for dependent variable selection.
tt_indi <- tktoplevel()
tkwm.title(tt_indi,"Individual variable")
cb_indi<-list()
for (i in 1:length(CV.list))
{
checkbox_indi <- tkcheckbutton(tt_indi)
#print(i)
cb_indi[[i]] <- checkbox_indi
}
cbValue_indi <- list()
for (i in 1:length(CV.list))
{
cbValue_indi[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb_indi[[i]],variable=cbValue_indi[[i]])
}
tkgrid(tklabel(tt_indi,text="Please choose the individual variables you want to include:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt_indi,text=CV.name.list[i]),cb_indi[[i]])
}
ind.OnOk_indi <- 0
OnOK_indi <- function()
{
cbVal_indi <<- c()
for (i in 1:length(CV.list))
{
cbVal_indi[i] <<- as.numeric(tclvalue(cbValue_indi[[i]]))
}
tkdestroy(tt_indi)
cbCharacter_indi <<- CV.name.list[cbVal_indi==1]
print(cbVal_indi)
print(cbCharacter_indi)
msg_indi="Your choices for individual variables are:"
for (i in 1:length(cbCharacter_indi))
{
msg_indi=paste(msg_indi,cbCharacter_indi[i],sep=" ")
}
tkmessageBox(message=msg_indi)
if ((ind.OnOk_dep == 0)|(ind.OnOk == 0))
{
tkmessageBox(message="Please choose dependent and comparison variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk_indi <<- 1
}
OK.but_indi <- tkbutton(tt_indi,text="OK",command=OnOK_indi)
tkgrid(OK.but_indi)
tkfocus(tt_indi)
}
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
# Create a function "tree.comparison" to be used in the analysis.
tree.comparison <- function(dep.var,CV.input.list,comparison.CV.list,d)
{
# Assign the names for comparison.CV.list.
names(comparison.CV.list)=names(d)[comparison.CV.list]
# Create a separate variable to store the names of comparison variables.
comparison.CV.name.list=names(comparison.CV.list)
# Assign k with intial value of 0.
k=0
# For each pair of comparison, we add a column in the dataset. We use
# double loops to list all possible pairs.
for (i in 1:(length(comparison.CV.list)-1))
{
for (j in ((i+1):length(comparison.CV.list)))
{
# increase k by one for each pair.
k=k+1
# Store the column numbers of two comparison variables in a pair
col1 <- comparison.CV.list[i]
col2 <- comparison.CV.list[j]
# Generate the indicator for this pair. It equals to -1 if the
# first variable is smaller than the second one, and 1 otherwise.
ind=ifelse(d[,col1]<d[,col2],-1,1)
# If this the first pair, then create a new dataset c. If not, then
# add a column into c.
if (k==1)
{
c=ind
c=as.data.frame(c)
}
else
{
c=cbind(c,ind)
}
#print(comparison.CV.name.list[i])
#print(comparison.CV.name.list[j])
# Assign names for these indicator. The name contains two variable names and
# connect them with a "_"
ind.name=paste(comparison.CV.name.list[i],"_",comparison.CV.name.list[j],sep="")
names(c)[dim(c)[2]]=ind.name
}
}
# Creata a new data frame with dependent variable, individual variables and comparison variables.
e <- as.data.frame(cbind(d[,dep.var],d[,CV.input.list],c))
# Assign names for e.
names(e)=c(names(d)[c(dep.var,CV.input.list)],names(c))
#print(e)
# Do the tree analysis using the tree() in R.
tree.ltr <- tree(e[,1]~.,e[,-1])
# Return the analysis output.
return(tree.ltr)
}
##############################################
# Simulation example
# Generate a 100 by 10 matrix with every number drawn from a standard normal distribution.
x=matrix(rnorm(10000),100,10)
# create indicators for x.
a1=ifelse(x[,1]>x[,2],1,-1)
a2=ifelse(x[,2]>x[,3],1,-1)
a3=ifelse(x[,3]>x[,4],1,-1)
# Create individual variables.
a4=x[,4]
a5=x[,5]
# Create Y based on the simulated data.
y=ifelse((10*a1+5*a2+3*a3+10*a4)>0,1,0)
# Create the whole dataset used in the function
ddd=cbind(y,x)
# Convert the dataset to data frame.
ddd=as.data.frame(ddd)
# Assign names for all the columns in ddd.
names(ddd)=c("y","x1","x2","x3","x4","x5","x6","x7","x8","x9","x10")
# Do the tree analysis.
tree.revised(ddd)
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3.2. Uncommented (download here)
rm(list=ls())
tree.revised<-function(d)
{
library(tree)
require(tcltk)
###############################
# dependent variable
################################
# 0. choose dependent variable
################################
# 0.1 create dependent variable list
CV.list_dep <- c()
CV.name.list_dep <- c()
for (i in seq(1,dim(d)[2]))
{
CV.list_dep<-c(CV.list_dep,i)
CV.name.list_dep<-c(CV.name.list_dep,names(d)[i])
}
names(CV.list_dep)<-CV.name.list_dep
################################
# 0.2 create choosing dependent variable module
tt_dep <- tktoplevel()
tkwm.title(tt_dep,"Dependent variable")
cb_dep<-list()
for (i in 1:length(CV.list_dep))
{
checkbox_dep <- tkcheckbutton(tt_dep)
#print(i)
cb_dep[[i]] <- checkbox_dep
}
cbValue_dep <- list()
for (i in 1:length(CV.list_dep))
{
cbValue_dep[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list_dep))
{
#print(i)
tkconfigure(cb_dep[[i]],variable=cbValue_dep[[i]])
}
tkgrid(tklabel(tt_dep,text="Please choose the dependent variable:"))
for (i in 1:length(CV.list_dep))
{
#print(i)
tkgrid(tklabel(tt_dep,text=CV.name.list_dep[i]),cb_dep[[i]])
}
ind.OnOk_dep <- 0
OnOK_dep <- function()
{
cbVal_dep <<- c()
for (i in 1:length(CV.list_dep))
{
cbVal_dep[i] <<- as.numeric(tclvalue(cbValue_dep[[i]]))
}
tkdestroy(tt_dep)
cbCharacter_dep <<- CV.name.list_dep[cbVal_dep==1]
print(cbVal_dep)
print(cbCharacter_dep)
msg_dep="Your choices for dependent variable is:"
for (i in 1:length(cbCharacter_dep))
{
msg_dep=paste(msg_dep,cbCharacter_dep[i],sep=" ")
}
tkmessageBox(message=msg_dep)
if ((ind.OnOk == 0)|(ind.OnOk_indi == 0))
{
tkmessageBox(message="Please choose comparison and individual variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk_dep <<- 1
}
OK.but_dep <- tkbutton(tt_dep,text="OK",command=OnOK_dep)
tkgrid(OK.but_dep)
tkfocus(tt_dep)
################################
# 1. choose variables to create comparison indicators
################################
# 1.1 create comparison variable list
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 1.2 create choosing variables module
tt <- tktoplevel()
tkwm.title(tt,"Comparison variable")
cb<-list()
for (i in 1:length(CV.list))
{
checkbox <- tkcheckbutton(tt)
#print(i)
cb[[i]] <- checkbox
}
cbValue <- list()
for (i in 1:length(CV.list))
{
cbValue[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb[[i]],variable=cbValue[[i]])
}
tkgrid(tklabel(tt,text="Please choose the variables you want to compare:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt,text=CV.name.list[i]),cb[[i]])
}
ind.OnOk <- 0
OnOK <- function()
{
cbVal <<- c()
for (i in 1:length(CV.list))
{
cbVal[i] <<- as.numeric(tclvalue(cbValue[[i]]))
}
tkdestroy(tt)
cbCharacter <<- CV.name.list[cbVal==1]
print(cbVal)
print(cbCharacter)
msg="Your choices for comparison variables are:"
for (i in 1:length(cbCharacter))
{
msg=paste(msg,cbCharacter[i],sep=" ")
}
tkmessageBox(message=msg)
if ((ind.OnOk_dep == 0)|(ind.OnOk_indi == 0))
{
tkmessageBox(message="Please choose dependent and individual variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk <<- 1
}
OK.but <- tkbutton(tt,text="OK",command=OnOK)
tkgrid(OK.but)
tkfocus(tt)
################################
# 2. choose variables for individual variables
################################
# 2.1 create individual variable list
CV.list<-CV.list_dep
CV.name.list<-CV.name.list_dep
################################
# 2.2 create individual variables module
tt_indi <- tktoplevel()
tkwm.title(tt_indi,"Individual variable")
cb_indi<-list()
for (i in 1:length(CV.list))
{
checkbox_indi <- tkcheckbutton(tt_indi)
#print(i)
cb_indi[[i]] <- checkbox_indi
}
cbValue_indi <- list()
for (i in 1:length(CV.list))
{
cbValue_indi[[i]] <- tclVar("0")
}
for (i in 1:length(CV.list))
{
#print(i)
tkconfigure(cb_indi[[i]],variable=cbValue_indi[[i]])
}
tkgrid(tklabel(tt_indi,text="Please choose the individual variables you want to include:"))
for (i in 1:length(CV.list))
{
#print(i)
tkgrid(tklabel(tt_indi,text=CV.name.list[i]),cb_indi[[i]])
}
ind.OnOk_indi <- 0
OnOK_indi <- function()
{
cbVal_indi <<- c()
for (i in 1:length(CV.list))
{
cbVal_indi[i] <<- as.numeric(tclvalue(cbValue_indi[[i]]))
}
tkdestroy(tt_indi)
cbCharacter_indi <<- CV.name.list[cbVal_indi==1]
print(cbVal_indi)
print(cbCharacter_indi)
msg_indi="Your choices for individual variables are:"
for (i in 1:length(cbCharacter_indi))
{
msg_indi=paste(msg_indi,cbCharacter_indi[i],sep=" ")
}
tkmessageBox(message=msg_indi)
if ((ind.OnOk_dep == 0)|(ind.OnOk == 0))
{
tkmessageBox(message="Please choose dependent and comparison variables")
}
else
{
dep.var=(1:dim(d)[2])[cbVal_dep==1]
CV.input.list=(1:dim(d)[2])[cbVal_indi==1]
comparison.CV.list=(1:dim(d)[2])[cbVal==1]
print(dep.var)
print(CV.input.list)
print(comparison.CV.list)
print(tree.comparison(dep.var,CV.input.list,comparison.CV.list,d))
}
ind.OnOk_indi <<- 1
}
OK.but_indi <- tkbutton(tt_indi,text="OK",command=OnOK_indi)
tkgrid(OK.but_indi)
tkfocus(tt_indi)
}
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
###############################################################
tree.comparison <- function(dep.var,CV.input.list,comparison.CV.list,d)
{
names(comparison.CV.list)=names(d)[comparison.CV.list]
comparison.CV.name.list=names(comparison.CV.list)
k=0
for (i in 1:(length(comparison.CV.list)-1))
{
for (j in ((i+1):length(comparison.CV.list)))
{
k=k+1
col1 <- comparison.CV.list[i]
col2 <- comparison.CV.list[j]
ind=ifelse(d[,col1]<d[,col2],-1,1)
if (k==1)
{
c=ind
c=as.data.frame(c)
}
else
{
c=cbind(c,ind)
}
#print(comparison.CV.name.list[i])
#print(comparison.CV.name.list[j])
ind.name=paste(comparison.CV.name.list[i],"_",comparison.CV.name.list[j],sep="")
names(c)[dim(c)[2]]=ind.name
}
}
e <- as.data.frame(cbind(d[,dep.var],d[,CV.input.list],c))
names(e)=c(names(d)[c(dep.var,CV.input.list)],names(c))
#print(e)
tree.ltr <- tree(e[,1]~.,e[,-1])
return(tree.ltr)
}
x=matrix(rnorm(10000),100,10)
a1=ifelse(x[,1]>x[,2],1,-1)
a2=ifelse(x[,2]>x[,3],1,-1)
a3=ifelse(x[,3]>x[,4],1,-1)
a4=x[,4]
a5=x[,5]
y=ifelse((10*a1+5*a2+3*a3+10*a4)>0,1,0)
ddd=cbind(y,x)
ddd=as.data.frame(ddd)
names(ddd)=c("y","x1","x2","x3","x4","x5","x6","x7","x8","x9","x10")
tree.revised(ddd)
#data(cpus, package="MASS")
#tree.revised(cpus)
#tree.revised(iris)
Reference
http://www.statsoft.com/textbook/stclatre.html
www.wikipedia.org
