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of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 10 Aug 2017 14:42:51 +0200

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/10/t1502369140jmkfdpiryblcqd8.htm/, Retrieved Sun, 19 May 2024 22:44:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307086, Retrieved Sun, 19 May 2024 22:44:09 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

103

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Recursive Partitioning (Regression Trees)] [] [2017-08-10 12:42:51] [57b70cf7dac5655af4bf79fd35e84c41] [Current]

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Dataseries X:

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1 	 	 	 	1 	 	N-
1 	12 	2.4 	3.8 	1 	-3.3 	N+
1 	6 	1.1 	1.4 	1 	1.7 	N-
1 	4 	0.6 	0.8 	1 	-1 	N-
1 	9 	1.8 	2.4 	1 	-1.2 	N-
0 	5 	0.5 	1 	0 	-2.9 	N-
1 	19 	5.9 	9.6 	1 	-26.2 	N+
1 	5 	0.4 	0.3 	1 	3.2 	N-
1 	6 	1.3 	1.2 	1 	2.2 	N-
1 	4 	0.5 	0.6 	1 	-0.5 	N-
1 	5 	0.6 	1.8 	1 	-7.3 	N-
1 	6 	0.9 	1.3 	1 	-0.9 	N-
1 	19 	0.8 	2 	1 	-4.1 	N+
1 	5 	0.6 	1.3 	1 	-5.9 	N+
0 	6 	0.4 	0.7 	0 	-1.1 	N-
0 	 	 	 	0 	 	N-
1 	5 	0.4 	0.7 	1 	-0.2 	N-
1 	4 	0.3 	0.5 	1 	-0.5 	N-
1 	 	 	 	1 	 	N+
1 	10 	5.9 	8.6 	1 	-10.7 	N+
1 	11 	1.5 	2.7 	1 	-2.2 	N+
0 	13 	1 	1.7 	0 	-1.3 	N+
0 	 	 	 	0 	 	N-
1 	 	 	 	1 	 	N-
1 	5 	1.1 	1 	1 	1.8 	N-
1 	 	 	 	1 	 	N-
1 	3 	0.5 	0.7 	1 	0.4 	N-
1 	8 	0.9 	1.3 	1 	-2.7 	N-
1 	6 	0.8 	1.2 	1 	-0.8 	N-
0 	5 	1.1 	2 	0 	-1.5 	N-
1 	7 	0.8 	1.6 	1 	-0.2 	N-
0 	5 	0.6 	1.1 	0 	-0.1 	N-
1 	3 	0.5 	0.5 	1 	2.4 	N+
1 	 	 	 	1 	 	N-
1 	8 	0.7 	1.7 	1 	-4.3 	N+
1 	10 	1 	0.9 	1 	1.9 	N-
1 	5 	0.4 	0.8 	1 	-0.6 	N-
1 	 	 	 	1 	 	N-
0 	5 	1.2 	1 	0 	7.5 	N-
0 	7 	0 	0.8 	0 	-6.9 	N-
0 	9 	1 	0.9 	0 	2.4 	N-
1 	10 	1.2 	2 	1 	-4.5 	N-
1 	 	 	 	1 	 	N+
1 	17 	4.7 	7.8 	1 	-27.6 	N+
1 	 	 	 	1 	 	N-
1 	5 	0.3 	1.7 	1 	-7.6 	N-
1 	10 	8.1 	12.5 	1 	-27.7 	N+
0 	 	 	 	0 	 	N+
1 	 	 	 	1 	 	N-
1 	5 	1 	1.5 	1 	-1.7 	N-
1 	 	 	 	1 	 	N+
1 	10 	1.4 	2.2 	1 	-3.5 	N-
0 	5 	0.8 	1.4 	0 	0.1 	N-
1 	 	 	 	1 	 	N-
1 	5 	0.7 	0.5 	1 	1.3 	N-
1 	5 	0.9 	1.1 	1 	0.8 	N-
1 	6 	0.7 	3.1 	1 	-14.3 	N+
1 	21 	6.4 	8.7 	1 	-13 	N+
0 	7 	0.7 	1.7 	0 	-2.4 	N+
0 	 	 	 	0 	 	N-
0 	7 	2.5 	6.8 	0 	-26.8 	N+
1 	7 	3 	3.8 	1 	-7.5 	N+
0 	 	 	 	0 	 	N+
1 	7 	0.6 	1.6 	1 	-3.6 	N-
0 	 	 	 	0 	 	N-
1 	6 	1 	1.6 	1 	-3.4 	N-
0 	6 	2.3 	2.3 	0 	1.5 	N-
1 	4 	0.9 	0.6 	1 	4.3 	N+
1 	8 	1.2 	1.9 	1 	-4.5 	N-
1 	 	 	 	1 	 	N-
1 	4 	0.6 	1.2 	1 	-2.9 	N+
1 	5 	0.7 	1.2 	1 	-0.7 	N-
1 	5 	0.7 	0.9 	1 	-1.3 	N-
1 	5 	0.7 	0.9 	1 	-0.7 	N-
0 	4 	0.9 	0.7 	0 	4.4 	N-
1 	5 	0.3 	1 	1 	-3 	N-
1 	5 	0.6 	0.8 	1 	-0.7 	N-
1 	4 	0.6 	0.7 	1 	-0.5 	N-
1 	5 	1 	1.4 	1 	0.2 	N-
1 	5 	0.5 	0.7 	1 	-0.2 	N-
1 	7 	0.7 	1.1 	1 	-2.8 	N+
0 	 	 	 	0 	 	N-
0 	5 	0.9 	2.1 	0 	-2.5 	N-
1 	 	 	 	1 	 	N-
1 	5 	0.4 	0.9 	1 	-3.5 	N-
1 	7 	0.8 	1.5 	1 	-3.9 	N+
1 	5 	0.9 	1.4 	1 	1.1 	N-
0 	7 	0.8 	1.5 	0 	-1.7 	N+
0 	 	 	 	0 	 	N-
1 	 	 	 	1 	 	N+
1 	6 	0.8 	0.6 	1 	3.5 	N-
1 	4 	0.8 	 	1 	7.7 	N-
1 	9 	1.1 	2.1 	1 	-2.9 	N-
1 	7 	1.3 	1.3 	1 	0.2 	N-
1 	7 	1.1 	1.1 	1 	4.3 	N-
0 	7 	0.9 	1.4 	0 	1.9 	N-
1 	3 	0.5 	1.5 	1 	-3.8 	N-
1 	6 	0.7 	1.6 	1 	-1.3 	N-
1 	11 	2.4 	5.5 	1 	-6 	N+
1 	 	 	 	1 	 	N-
1 	7 	0.7 	2.2 	1 	-7.2 	N+
1 	9 	1.1 	1.7 	1 	-5 	N+
1 	 	 	 	1 	 	N-
1 	7 	1.8 	2.4 	1 	0.5 	N-
0 	5 	1 	1.1 	0 	5.6 	N-
0 	6 	0.8 	1 	0 	0.4 	N-
0 	9 	0.8 	1.2 	0 	-2.5 	N-
1 	8 	0.9 	2.1 	1 	-7.9 	N+
1 	5 	0.9 	1.1 	1 	-0.1 	N+
1 	 	 	 	1 	 	N-
1 	 	 	 	1 	 	N-
1 	6 	0.7 	3.3 	1 	-13.7 	N-
0 	 	 	 	0 	 	N-
1 	5 	1.1 	0.6 	1 	4.9 	N-
1 	 	 	 	1 	 	N-
1 	 	 	 	1 	 	N+
1 	7 	1 	1.8 	1 	-3.9 	N-
0 	 	 	 	0 	 	N-
1 	 	 	 	1 	 	N-
1 	5 	1 	0.7 	1 	3 	N-
1 	6 	0.9 	1.2 	1 	-0.8 	N-
1 	11 	2.3 	4.3 	1 	-3.7 	N+
0 	15 	6.2 	14.5 	0 	-70.9 	N+
0 	6 	0.6 	1.2 	0 	-0.5 	N-
0 	 	 	 	0 	 	N-
0 	4 	0.3 	0.4 	0 	0.1 	N-
1 	 	 	 	1 	 	N-
0 	13 	4.9 	7.6 	0 	-23.1 	N+
1 	3 	0.7 	0.7 	1 	3.5 	N-
0 	 	 	 	0 	 	N-
1 	 	 	 	1 	 	N-
0 	5 	1.7 	1.4 	0 	5.1 	N-
1 	6 	0.7 	0.8 	1 	0.5 	N-
1 	7 	1.6 	2.3 	1 	-4.7 	N-

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307086&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

0 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=307086&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307086&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Parameters (Session):

par1 = 7 ; par2 = equal ; par3 = 3 ; par4 = yes ;

Parameters (R input):

par1 = 7 ; par2 = equal ; par3 = 3 ; par4 = yes ;

R code (references can be found in the software module):

par4 <- 'yes'
par3 <- '3'
par2 <- 'equal'
par1 <- '7'
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code