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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 15 Nov 2011 13:35:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/15/t1321382158f9qclmfypfg95n8.htm/, Retrieved Fri, 29 Mar 2024 10:51:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143335, Retrieved Fri, 29 Mar 2024 10:51:45 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Recursive Partitioning (Regression Trees)] [] [2011-11-15 18:35:51] [c80accbb627afb8a1e74b91ef6a0d2c4] [Current]
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Dataseries X:
1	99.2	96.7	101
1	99	98.1	100.1
1	100	100	100
1	111.6	104.9	90.6
1	122.2	104.9	86.5
1	117.6	109.5	89.7
1	121.1	110.8	90.6
1	136	112.3	82.8
1	154.2	109.3	70.1
1	153.6	105.3	65.4
1	158.5	101.7	61.3
0	140.6	95.4	62.5
0	136.2	96.4	63.6
0	168	97.6	52.6
0	154.3	102.4	59.7
0	149	101.6	59.5
0	165.5	103.8	61.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143335&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143335&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143335&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Goodness of Fit
CorrelationNA
R-squaredNA
RMSE0.4779

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & NA \tabularnewline
R-squared & NA \tabularnewline
RMSE & 0.4779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143335&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]NA[/C][/ROW]
[ROW][C]R-squared[/C][C]NA[/C][/ROW]
[ROW][C]RMSE[/C][C]0.4779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143335&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143335&T=1

As an alternative you can also use a QR Code:  

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

Goodness of Fit
CorrelationNA
R-squaredNA
RMSE0.4779







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.6470588235294120.352941176470588
210.6470588235294120.352941176470588
310.6470588235294120.352941176470588
410.6470588235294120.352941176470588
510.6470588235294120.352941176470588
610.6470588235294120.352941176470588
710.6470588235294120.352941176470588
810.6470588235294120.352941176470588
910.6470588235294120.352941176470588
1010.6470588235294120.352941176470588
1110.6470588235294120.352941176470588
1200.647058823529412-0.647058823529412
1300.647058823529412-0.647058823529412
1400.647058823529412-0.647058823529412
1500.647058823529412-0.647058823529412
1600.647058823529412-0.647058823529412
1700.647058823529412-0.647058823529412

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
2 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
3 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
4 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
5 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
6 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
7 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
8 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
9 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
10 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
11 & 1 & 0.647058823529412 & 0.352941176470588 \tabularnewline
12 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
13 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
14 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
15 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
16 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
17 & 0 & 0.647058823529412 & -0.647058823529412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143335&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.647058823529412[/C][C]0.352941176470588[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.647058823529412[/C][C]-0.647058823529412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143335&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143335&T=2

As an alternative you can also use a QR Code:  

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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.6470588235294120.352941176470588
210.6470588235294120.352941176470588
310.6470588235294120.352941176470588
410.6470588235294120.352941176470588
510.6470588235294120.352941176470588
610.6470588235294120.352941176470588
710.6470588235294120.352941176470588
810.6470588235294120.352941176470588
910.6470588235294120.352941176470588
1010.6470588235294120.352941176470588
1110.6470588235294120.352941176470588
1200.647058823529412-0.647058823529412
1300.647058823529412-0.647058823529412
1400.647058823529412-0.647058823529412
1500.647058823529412-0.647058823529412
1600.647058823529412-0.647058823529412
1700.647058823529412-0.647058823529412



Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software 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')
}
}
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')
}