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Description of Statistical Computation

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 computationMon, 13 Dec 2010 10:47:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t12922372628dfkodxwwbhj2gh.htm/, Retrieved Mon, 06 May 2024 23:54:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108819, Retrieved Mon, 06 May 2024 23:54:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS10 RP] [2010-12-12 20:07:21] [65eb19f81eab2b6e672eafaed2a27190]
-   PD    [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 09:33:29] [65eb19f81eab2b6e672eafaed2a27190]
-   P         [Recursive Partitioning (Regression Trees)] [WS10 RP Equal 2 cat.] [2010-12-13 10:47:37] [8b27277f7b82c0354d659d066108e38e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5	2	1	3	11	16	14	6
12	1	1	1	11	13	11	4
11	1	1	3	15	16	11	5
6	1	1	3	11	6	9	4
12	1	2	3	9	11	11	4
11	1	1	3	14	13	16	6
12	1	1	1	12	15	13	6
7	2	4	3	6	9	11	4
8	1	1	3	4	6	4	4
13	1	1	1	13	11	15	6
12	1	1	1	12	9	13	4
13	1	1	3	10	4	13	6
12	1	1	1	12	8	13	5
12	1	3	3	9	11	11	4
11	2	1	3	16	16	15	6
12	2	1	1	13	5	12	3
12	1	1	1	12	6	14	5
12	1	6	1	11	7	13	6
11	2	1	3	12	16	13	4
13	2	1	1	12	12	12	6
9	1	1	3	11	7	13	2
11	2	1	3	16	13	14	7
11	1	1	1	9	12	13	5
11	2	1	3	8	10	15	2
9	1	1	1	11	12	12	4
11	2	1	4	9	8	10	4
12	2	1	3	16	15	14	6
12	1	1	3	14	15	13	6
10	2	1	3	10	10	11	5
12	1	4	3	14	13	15	6
12	2	1	1	16	16	14	6
12	1	1	3	12	10	13	4
9	2	1	3	13	14	14	6
9	1	1	3	16	16	16	6
12	1	1	3	15	13	13	6
14	2	1	1	5	4	5	2
12	2	1	3	12	7	11	4
11	1	1	1	11	15	10	5
9	1	1	2	15	5	11	3
11	2	1	3	15	14	15	7
7	1	1	1	12	11	15	5
15	1	1	1	5	8	12	3
11	1	1	3	16	14	15	8
12	1	1	3	16	12	15	8
12	2	2	1	12	12	14	5
9	2	1	3	6	15	11	6
12	2	1	3	7	8	12	3
11	2	1	3	14	16	12	5
11	2	2	3	8	9	12	4
8	1	4	3	12	13	13	5
7	2	1	1	10	8	9	5
12	2	4	3	11	15	12	6
8	1	1	2	13	14	14	5
10	1	1	1	15	12	16	6
12	1	2	2	10	11	12	6
15	2	3	3	9	6	8	4
12	1	1	3	16	14	16	8
12	2	2	1	11	8	16	6
12	2	1	3	8	8	13	4
12	2	1	1	14	15	14	6
8	2	1	3	11	14	15	5
10	1	1	3	12	14	14	5
14	2	1	3	14	17	18	6
10	1	1	3	15	16	13	6
12	2	1	3	14	13	13	6
14	2	1	3	11	7	13	6
6	2	1	1	11	14	17	6
11	1	1	3	15	12	13	6
10	2	1	3	12	14	13	7
14	2	1	3	7	12	12	4
12	1	1	1	10	8	11	4
13	2	1	1	13	14	13	3
11	2	1	3	15	17	16	6
11	1	1	3	15	14	13	5
12	1	1	1	13	13	13	5
13	2	2	3	8	7	10	3
12	1	1	1	14	13	13	5
8	2	1	3	11	8	13	4
12	2	1	3	12	7	12	3
11	1	1	3	16	16	16	7
10	2	1	3	8	10	6	4
12	1	1	3	12	14	14	4
11	2	2	7	11	11	14	5
12	1	1	1	13	11	13	6
12	1	1	3	6	6	11	2
10	2	1	1	4	4	10	2
12	1	1	3	11	11	12	6
12	2	1	1	7	7	12	4
11	2	1	3	12	11	12	5
10	1	1	3	12	12	13	6
12	1	1	1	16	16	16	7
11	1	1	1	15	15	15	8
12	1	4	3	13	16	16	6
12	1	1	2	12	10	15	6
10	1	1	1	9	11	13	3
11	1	1	1	16	17	16	7
10	1	1	2	11	5	13	3
11	2	1	2	14	15	14	6
11	2	1	1	10	9	12	4
12	1	1	2	10	8	16	4
11	1	1	3	11	8	12	6
11	1	2	3	16	14	14	6
7	1	1	2	8	4	13	6
12	1	1	3	16	8	14	4
8	1	1	1	12	15	13	7
10	1	1	3	11	12	14	5
12	1	1	2	16	15	15	7
11	1	1	3	9	9	12	4
13	2	1	2	13	15	14	6
9	1	1	3	14	19	12	6
11	1	1	1	10	13	15	6
13	1	1	1	12	14	12	5
8	1	1	3	11	10	14	5
12	1	1	3	12	15	13	6
11	1	1	3	13	12	13	7
11	2	1	1	14	12	12	4
12	1	1	3	12	12	13	4
13	1	1	3	14	10	16	8
11	1	1	1	13	14	13	6
10	1	1	1	8	10	14	3
10	1	4	3	13	8	8	4
10	1	1	1	10	11	12	5
12	2	1	3	8	8	14	5
12	2	1	3	15	13	14	6
13	1	1	3	15	16	18	8
11	1	2	1	12	11	14	2
11	2	1	1	8	10	12	4
12	1	2	3	15	12	16	7
9	1	1	3	9	6	12	5
11	2	1	3	14	14	12	6
12	1	1	3	16	14	14	6
12	1	1	3	14	8	14	4
13	2	1	3	14	13	13	5
6	1	1	3	14	13	12	6
11	1	1	3	14	10	16	6
10	2	1	2	14	12	15	6
12	2	4	3	13	14	14	6
11	1	1	3	12	14	13	5
12	2	5	3	13	7	12	5
12	1	1	1	19	15	15	6
7	1	1	2	8	9	15	4
12	1	1	3	10	5	13	6
12	1	1	1	7	13	12	3
9	1	1	1	12	7	12	6
12	1	1	3	16	14	16	8
12	1	1	3	15	14	16	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108819&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108819&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108819&T=0

As an alternative you can also use a QR Code:  
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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1609680.899678150.8387
C22493590.590537450.5488
Overall--0.7533--0.7029

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 609 & 68 & 0.8996 & 78 & 15 & 0.8387 \tabularnewline
C2 & 249 & 359 & 0.5905 & 37 & 45 & 0.5488 \tabularnewline
Overall & - & - & 0.7533 & - & - & 0.7029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108819&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]609[/C][C]68[/C][C]0.8996[/C][C]78[/C][C]15[/C][C]0.8387[/C][/ROW]
[ROW][C]C2[/C][C]249[/C][C]359[/C][C]0.5905[/C][C]37[/C][C]45[/C][C]0.5488[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.7533[/C][C]-[/C][C]-[/C][C]0.7029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108819&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108819&T=1

As an alternative you can also use a QR Code:  
QR Code


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

10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1609680.899678150.8387
C22493590.590537450.5488
Overall--0.7533--0.7029






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1734
C23336

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 73 & 4 \tabularnewline
C2 & 33 & 36 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108819&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]73[/C][C]4[/C][/ROW]
[ROW][C]C2[/C][C]33[/C][C]36[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108819&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108819&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1734
C23336


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = equal ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 8 ; par2 = equal ; par3 = 2 ; par4 = yes ;
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')
}