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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 computationFri, 24 Dec 2010 10:41:56 +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/24/t1293187227uwx7ysufumihw8j.htm/, Retrieved Tue, 30 Apr 2024 07:15:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114727, Retrieved Tue, 30 Apr 2024 07:15:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

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)] [] [2010-12-10 15:32:27] [39e83c7b0ac936e906a817a1bb402750]
-   P     [Recursive Partitioning (Regression Trees)] [] [2010-12-10 16:48:18] [39e83c7b0ac936e906a817a1bb402750]
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-10 17:49:01] [39e83c7b0ac936e906a817a1bb402750]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2010-12-21 12:59:30] [39e83c7b0ac936e906a817a1bb402750]
-    D            [Recursive Partitioning (Regression Trees)] [] [2010-12-24 10:41:56] [558c060a42ec367ec2c020fab85c25c7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	11	23	1	6
12	22	24	2	5
26	23	24	2	20
16	21	21	2	12
18	19	21	2	11
12	12	19	2	12
18	24	12	1	11
20	21	21	1	9
18	21	25	2	13
24	26	27	2	9
17	18	21	1	14
19	21	27	1	12
12	22	20	1	18
25	26	16	2	9
23	20	26	1	15
22	20	24	2	12
23	26	25	2	12
16	27	25	1	12
16	27	27	1	15
15	16	23	2	11
24	26	22	1	13
18	20	10	1	10
23	25	25	2	17
18	16	18	1	13
19	20	21	1	17
17	20	20	1	15
22	24	18	1	13
22	24	25	1	17
8	22	28	1	21
12	18	27	1	12
22	21	20	2	12
16	17	20	1	15
12	15	20	2	8
28	28	27	2	15
15	23	23	1	16
17	19	23	2	9
16	15	22	2	13
24	26	26	1	11
27	20	21	1	9
10	11	17	1	15
20	17	27	2	9
17	16	16	2	15
20	21	26	1	14
16	18	17	1	8
16	17	24	2	11
22	21	23	2	14
19	18	20	1	14
11	16	10	1	12
11	13	21	1	15
28	28	25	1	11
12	25	28	1	11
22	24	25	2	9
15	15	20	2	8
19	21	20	1	13
12	11	27	1	12
18	27	26	1	24
21	23	19	2	11
21	21	26	1	11
15	16	20	2	16
12	20	22	1	12
25	21	19	2	18
12	10	23	2	12
25	18	28	2	14
17	20	22	2	16
26	21	27	2	24
24	24	14	1	13
18	26	25	1	11
20	23	22	1	14
17	22	24	1	16
11	13	23	1	12
27	27	25	1	21
14	24	28	2	11
22	19	28	1	6
19	17	16	2	9
19	16	25	1	14
18	20	21	1	16
9	8	27	1	18
22	16	21	2	9
17	17	22	1	13
23	23	26	2	17
16	18	21	1	11
23	24	24	1	16
13	17	24	1	11
21	20	23	1	11
17	22	26	2	11
15	22	21	1	20
16	20	24	1	10
19	18	23	1	12
19	21	21	2	11
16	23	20	1	14
23	28	22	1	12
19	19	26	1	12
17	22	23	1	12
20	17	23	2	10
25	25	22	2	12
22	22	25	2	10
18	21	21	2	10
16	15	21	1	13
18	20	25	1	12
15	25	26	2	13
19	21	21	1	9
23	24	24	1	14
20	23	21	2	14
24	22	23	1	12
17	14	24	1	18
20	11	24	1	17
11	22	24	1	12
20	22	25	1	15
8	6	28	1	8
22	15	18	2	8
20	26	28	1	12
23	26	22	1	10
11	20	28	1	18
22	26	22	1	15
10	15	24	1	16
19	25	27	2	11
26	22	21	2	10
22	20	26	2	7
12	18	24	1	17
13	23	25	1	7
19	22	20	2	14
19	23	21	1	12
21	17	23	1	15
11	20	23	1	13
21	21	19	2	10
25	23	22	1	16
27	25	15	2	11
21	25	24	2	7
14	21	18	2	15
16	22	18	1	18
16	18	23	1	11
19	18	17	1	13
24	18	19	2	11
18	21	21	2	13
16	21	12	2	12
20	25	25	2	11
19	24	25	1	11
20	24	24	1	13
27	28	24	2	8
24	24	24	2	12
23	22	22	2	9
20	22	22	1	14
20	20	21	1	18
20	25	23	1	15
15	13	21	1	9
17	21	24	1	11
16	23	22	1	17
20	18	25	2	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114727&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114727&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114727&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1690630.916374130.8506
C23412340.40741240.3692
Overall--0.6958--0.6447

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 690 & 63 & 0.9163 & 74 & 13 & 0.8506 \tabularnewline
C2 & 341 & 234 & 0.407 & 41 & 24 & 0.3692 \tabularnewline
Overall & - & - & 0.6958 & - & - & 0.6447 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114727&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]690[/C][C]63[/C][C]0.9163[/C][C]74[/C][C]13[/C][C]0.8506[/C][/ROW]
[ROW][C]C2[/C][C]341[/C][C]234[/C][C]0.407[/C][C]41[/C][C]24[/C][C]0.3692[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6958[/C][C]-[/C][C]-[/C][C]0.6447[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114727&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114727&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
C1690630.916374130.8506
C23412340.40741240.3692
Overall--0.6958--0.6447






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1786
C24024

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 78 & 6 \tabularnewline
C2 & 40 & 24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114727&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]78[/C][C]6[/C][/ROW]
[ROW][C]C2[/C][C]40[/C][C]24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114727&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114727&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
C1786
C24024


Figures (Output of Computation)

Input Parameters & R Code

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