Recursive Partitioning (Regression Trees) - Free Statistics and Forecasting Software (Calculators) v.1.2.1

All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.

This free online software (calculator) computes recursive partitioning analysis (regression trees) as discussed by Everitt Brian S. and Torsten Hothorn (2009), A Handbook of Statistical Analyses Using R, 2nd ed., CRC Press, Francis & Taylor Group. This software allows you to model continuous and categorical variables.

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Send output to:

Data X (click to load default data)
1 1 41 38 13 12 14 1 1 39 32 16 11 18 1 1 30 35 19 15 11 1 0 31 33 15 6 12 1 1 34 37 14 13 16 1 1 35 29 13 10 18 1 1 39 31 19 12 14 1 1 34 36 15 14 14 1 1 36 35 14 12 15 1 1 37 38 15 9 15 1 0 38 31 16 10 17 1 1 36 34 16 12 19 1 0 38 35 16 12 10 1 1 39 38 16 11 16 1 1 33 37 17 15 18 1 0 32 33 15 12 14 1 0 36 32 15 10 14 1 1 38 38 20 12 17 1 0 39 38 18 11 14 1 1 32 32 16 12 16 1 0 32 33 16 11 18 1 1 31 31 16 12 11 1 1 39 38 19 13 14 1 1 37 39 16 11 12 1 0 39 32 17 12 17 1 1 41 32 17 13 9 1 0 36 35 16 10 16 1 1 33 37 15 14 14 1 1 33 33 16 12 15 1 0 34 33 14 10 11 1 1 31 31 15 12 16 1 0 27 32 12 8 13 1 1 37 31 14 10 17 1 1 34 37 16 12 15 1 0 34 30 14 12 14 1 0 32 33 10 7 16 1 0 29 31 10 9 9 1 0 36 33 14 12 15 1 1 29 31 16 10 17 1 0 35 33 16 10 13 1 0 37 32 16 10 15 1 1 34 33 14 12 16 1 0 38 32 20 15 16 1 0 35 33 14 10 12 1 1 38 28 14 10 15 1 1 37 35 11 12 11 1 1 38 39 14 13 15 1 1 33 34 15 11 15 1 1 36 38 16 11 17 1 0 38 32 14 12 13 1 1 32 38 16 14 16 1 0 32 30 14 10 14 1 0 32 33 12 12 11 1 1 34 38 16 13 12 1 0 32 32 9 5 12 1 1 37 35 14 6 15 1 1 39 34 16 12 16 1 1 29 34 16 12 15 1 0 37 36 15 11 12 1 1 35 34 16 10 12 1 0 30 28 12 7 8 1 0 38 34 16 12 13 1 1 34 35 16 14 11 1 1 31 35 14 11 14 1 1 34 31 16 12 15 1 0 35 37 17 13 10 1 1 36 35 18 14 11 1 0 30 27 18 11 12 1 1 39 40 12 12 15 1 0 35 37 16 12 15 1 0 38 36 10 8 14 1 1 31 38 14 11 16 1 1 34 39 18 14 15 1 0 38 41 18 14 15 1 0 34 27 16 12 13 1 1 39 30 17 9 12 1 1 37 37 16 13 17 1 1 34 31 16 11 13 1 0 28 31 13 12 15 1 0 37 27 16 12 13 1 0 33 36 16 12 15 1 1 35 37 16 12 15 1 0 37 33 15 12 16 1 1 32 34 15 11 15 1 1 33 31 16 10 14 1 0 38 39 14 9 15 1 1 33 34 16 12 14 1 1 29 32 16 12 13 1 1 33 33 15 12 7 1 1 31 36 12 9 17 1 1 36 32 17 15 13 1 1 35 41 16 12 15 1 1 32 28 15 12 14 1 1 29 30 13 12 13 1 1 39 36 16 10 16 1 1 37 35 16 13 12 1 1 35 31 16 9 14 1 0 37 34 16 12 17 1 0 32 36 14 10 15 1 1 38 36 16 14 17 1 0 37 35 16 11 12 1 1 36 37 20 15 16 1 0 32 28 15 11 11 1 1 33 39 16 11 15 1 0 40 32 13 12 9 1 1 38 35 17 12 16 1 0 41 39 16 12 15 1 0 36 35 16 11 10 1 1 43 42 12 7 10 1 1 30 34 16 12 15 1 1 31 33 16 14 11 1 1 32 41 17 11 13 1 1 37 34 12 10 18 1 0 37 32 18 13 16 1 1 33 40 14 13 14 1 1 34 40 14 8 14 1 1 33 35 13 11 14 1 1 38 36 16 12 14 1 0 33 37 13 11 12 1 1 31 27 16 13 14 1 1 38 39 13 12 15 1 1 37 38 16 14 15 1 1 36 31 15 13 15 1 1 31 33 16 15 13 1 0 39 32 15 10 17 1 1 44 39 17 11 17 1 1 33 36 15 9 19 1 1 35 33 12 11 15 1 0 32 33 16 10 13 1 0 28 32 10 11 9 1 1 40 37 16 8 15 1 0 27 30 12 11 15 1 0 37 38 14 12 15 1 1 32 29 15 12 16 1 0 28 22 13 9 11 1 0 34 35 15 11 14 1 1 30 35 11 10 11 1 1 35 34 12 8 15 1 0 31 35 11 9 13 1 1 32 34 16 8 15 1 0 30 37 15 9 16 1 1 30 35 17 15 14 1 0 31 23 16 11 15 1 1 40 31 10 8 16 1 1 32 27 18 13 16 1 0 36 36 13 12 11 1 0 32 31 16 12 12 1 0 35 32 13 9 9 1 1 38 39 10 7 16 1 1 42 37 15 13 13 1 0 34 38 16 9 16 1 1 35 39 16 6 12 1 1 38 34 14 8 9 1 1 33 31 10 8 13 1 1 32 37 13 6 14 1 1 33 36 15 9 19 1 1 34 32 16 11 13 1 1 32 38 12 8 12 0 0 27 26 13 10 10 0 0 31 26 12 8 14 0 0 38 33 17 14 16 0 1 34 39 15 10 10 0 0 24 30 10 8 11 0 0 30 33 14 11 14 0 1 26 25 11 12 12 0 1 34 38 13 12 9 0 0 27 37 16 12 9 0 0 37 31 12 5 11 0 1 36 37 16 12 16 0 0 41 35 12 10 9 0 1 29 25 9 7 13 0 1 36 28 12 12 16 0 0 32 35 15 11 13 0 1 37 33 12 8 9 0 0 30 30 12 9 12 0 1 31 31 14 10 16 0 1 38 37 12 9 11 0 1 36 36 16 12 14 0 0 35 30 11 6 13 0 0 31 36 19 15 15 0 0 38 32 15 12 14 0 1 22 28 8 12 16 0 1 32 36 16 12 13 0 0 36 34 17 11 14 0 1 39 31 12 7 15 0 0 28 28 11 7 13 0 0 32 36 11 5 11 0 1 32 36 14 12 11 0 1 38 40 16 12 14 0 1 32 33 12 3 15 0 1 35 37 16 11 11 0 1 32 32 13 10 15 0 0 37 38 15 12 12 0 1 34 31 16 9 14 0 1 33 37 16 12 14 0 0 33 33 14 9 8 0 0 30 30 16 12 9 0 0 24 30 14 10 15 0 0 34 31 11 9 17 0 0 34 32 12 12 13 0 1 33 34 15 8 15 0 1 34 36 15 11 15 0 1 35 37 16 11 14 0 0 35 36 16 12 16 0 0 36 33 11 10 13 0 0 34 33 15 10 16 0 1 34 33 12 12 9 0 0 41 44 12 12 16 0 0 32 39 15 11 11 0 0 30 32 15 8 10 0 1 35 35 16 12 11 0 0 28 25 14 10 15 0 1 33 35 17 11 17 0 1 39 34 14 10 14 0 0 36 35 13 8 8 0 1 36 39 15 12 15 0 0 35 33 13 12 11 0 0 38 36 14 10 16 0 1 33 32 15 12 10 0 0 31 32 12 9 15 0 1 32 36 8 6 16 0 0 31 32 14 10 19 0 0 33 34 14 9 12 0 0 34 33 11 9 8 0 0 34 35 12 9 11 0 1 34 30 13 6 14 0 0 33 38 10 10 9 0 0 32 34 16 6 15 0 1 41 33 18 14 13 0 1 34 32 13 10 16 0 0 36 31 11 10 11 0 0 37 30 4 6 12 0 0 36 27 13 12 13 0 1 29 31 16 12 10 0 0 37 30 10 7 11 0 0 27 32 12 8 12 0 0 35 35 12 11 8 0 0 28 28 10 3 12 0 0 35 33 13 6 12 0 0 29 35 12 8 11 0 0 32 35 14 9 13 0 1 36 32 10 9 14 0 1 19 21 12 8 10 0 1 21 20 12 9 12 0 0 31 34 11 7 15 0 0 33 32 10 7 13 0 1 36 34 12 6 13 0 1 33 32 16 9 13 0 0 37 33 12 10 12 0 0 34 33 14 11 12 0 0 35 37 16 12 9 0 1 31 32 14 8 9 0 1 37 34 13 11 15 0 1 35 30 4 3 10 0 1 27 30 15 11 14 0 0 34 38 11 12 15 0 0 40 36 11 7 7 0 0 29 32 14 9 14 0 0 38 34 15 12 8 0 1 34 33 14 8 10 0 0 21 27 13 11 13 0 0 36 32 11 8 13 0 1 38 34 15 10 13 0 0 30 29 11 8 8 0 0 35 35 13 7 12 0 1 30 27 13 8 13 0 1 36 33 16 10 12 0 0 34 38 13 8 10 0 1 35 36 16 12 13 0 0 34 33 16 14 12 0 0 32 39 12 7 9 0 1 33 29 7 6 15 0 0 33 32 16 11 13 0 1 26 34 5 4 13 0 0 35 38 16 9 13 0 0 21 17 4 5 15 0 0 38 35 12 9 15 0 0 35 32 15 11 14 0 1 33 34 14 12 15 0 0 37 36 11 9 11 0 0 38 31 16 12 15 0 1 34 35 15 10 14 0 0 27 29 12 9 13 0 1 16 22 6 6 12 0 0 40 41 16 10 16 0 0 36 36 10 9 16 0 1 42 42 15 13 9 0 1 30 33 14 12 14
Names of X columns:
Pop Gender Connected Separate Learning Software Happiness
Endogenous Variable (Column Number)


Categorization


Number of categories (only if categorization<>none)


Cross-Validation? (only if categorization<>none)


Chart options
Width:
Height:

Source code of R module

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == "kmeans") {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste("C",i,sep="")
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == "quantiles") {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == "hclust") {
hc <- hclust(dist(x[,par1])^2, "cen")
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste("C",i,sep="")
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=="equal") {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste("C",1:par3,sep=""))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == "none") {
m <- ctree(as.formula(paste(colnames(x)[par1]," ~ .",sep="")),data = x)
}
load(file="createtable")
if (par2 != "none") {
m <- ctree(as.formula(paste("as.factor(",colnames(x)[par1],") ~ .",sep="")),data = x)
if (par4=="yes") {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,"10-Fold Cross Validation",3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"",1,TRUE)
a<-table.element(a,"Prediction (training)",par3+1,TRUE)
a<-table.element(a,"Prediction (testing)",par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"Actual",1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste("C",jjj,sep=""),1,TRUE)
a<-table.element(a,"CV",1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste("C",jjj,sep=""),1,TRUE)
a<-table.element(a,"CV",1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste("as.factor(",colnames(x)[par1],") ~ .",sep="")),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste("C",i,sep=""),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,"Overall",1,TRUE)
for (jjj in 1:par3) a<-table.element(a,"-")
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,"-")
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file="mytable3.tab")
}
}
print(m)
bitmap(file="test1.png")
plot(m)
dev.off()
bitmap(file="test1a.png")
plot(x[,par1] ~ as.factor(where(m)),main="Response by Terminal Node",xlab="Terminal Node",ylab="Response")
dev.off()
if (par2 == "none") {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c("Actuals","Forecasts","Residuals")
print(result)
}
if (par2 != "none") {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file="test2.png")
if(par2=="none") {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main="Kernel Density Plot of Actuals")
plot(density(result$Residuals),main="Kernel Density Plot of Residuals")
plot(result$Forecasts,result$Actuals,main="Actuals versus Predictions",xlab="Predictions",ylab="Actuals")
plot(density(result$Forecasts),main="Kernel Density Plot of Predictions")
par(op)
}
if(par2!="none") {
plot(myt,main="Confusion Matrix",xlab="Actual",ylab="Predicted")
}
dev.off()
if (par2 == "none") {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,"Goodness of Fit",2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"Correlation",1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"R-squared",1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"RMSE",1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file="mytable1.tab")
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,"Actuals, Predictions, and Residuals",4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"#",header=TRUE)
a<-table.element(a,"Actuals",header=TRUE)
a<-table.element(a,"Forecasts",header=TRUE)
a<-table.element(a,"Residuals",header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file="mytable.tab")
}
if (par2 != "none") {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,"Confusion Matrix (predicted in columns / actuals in rows)",par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,"",1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste("C",i,sep=""),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste("C",i,sep=""),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file="mytable2.tab")
}

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:: Recursive Partitioning (Regression Trees) - Free Statistics Software (Calculator) ::