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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 computationTue, 21 Dec 2010 12:59:30 +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/21/t12929362461qwy474oqkdutc7.htm/, Retrieved Wed, 15 May 2024 20:52:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113455, Retrieved Wed, 15 May 2024 20:52:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

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] [558c060a42ec367ec2c020fab85c25c7] [Current]
-    D            [Recursive Partitioning (Regression Trees)] [] [2010-12-24 10:41:56] [39e83c7b0ac936e906a817a1bb402750]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	11	23	8	1	6
7	22	24	4	2	5
22	23	24	7	2	20
12	21	21	4	2	12
15	19	21	4	2	11
9	12	19	5	2	12
20	24	12	15	1	11
10	21	21	5	1	9
12	21	25	7	2	13
23	26	27	4	2	9
10	18	21	4	1	14
11	21	27	7	1	12
20	22	20	8	1	18
11	26	16	4	2	9
22	20	26	8	1	15
19	20	24	4	2	12
20	26	25	5	2	12
16	27	25	16	1	12
12	27	27	7	1	15
14	16	23	4	2	11
14	26	22	6	1	13
9	20	10	4	1	10
19	25	25	5	2	17
17	16	18	4	1	13
14	20	21	4	1	17
19	20	20	6	1	15
20	24	18	4	1	13
20	24	25	4	1	17
9	22	28	4	1	21
10	18	27	8	1	12
6	21	20	5	2	12
15	17	20	4	1	15
9	15	20	10	2	8
24	28	27	4	2	15
11	23	23	4	1	16
4	19	23	4	2	9
12	15	22	5	2	13
22	26	26	5	1	11
16	20	21	4	1	9
14	11	17	6	1	15
13	17	27	4	2	9
13	16	16	4	2	15
10	21	26	4	1	14
12	18	17	4	1	8
13	17	24	4	2	11
16	21	23	4	2	14
18	18	20	6	1	14
10	16	10	4	1	12
12	13	21	5	1	15
9	28	25	4	1	11
7	25	28	4	1	11
16	24	25	5	2	9
12	15	20	10	2	8
15	21	20	10	1	13
15	11	27	4	1	12
8	27	26	4	1	24
14	23	19	4	2	11
13	21	26	8	1	11
18	16	20	4	2	16
11	20	22	14	1	12
12	21	19	4	2	18
12	10	23	5	2	12
24	18	28	4	2	14
11	20	22	8	2	16
5	21	27	4	2	24
17	24	14	4	1	13
9	26	25	5	1	11
20	23	22	8	1	14
17	22	24	7	1	16
14	13	23	4	1	12
23	27	25	4	1	21
10	24	28	9	2	11
19	19	28	4	1	6
5	17	16	4	2	9
16	16	25	5	1	14
19	20	21	4	1	16
5	8	27	4	1	18
15	16	21	6	2	9
18	17	22	6	1	13
20	23	26	4	2	17
17	18	21	6	1	11
19	24	24	4	1	16
11	17	24	6	1	11
12	20	23	4	1	11
13	22	26	8	2	11
7	22	21	5	1	20
8	20	24	8	1	10
15	18	23	7	1	12
13	21	21	4	2	11
18	23	20	6	1	14
19	28	22	4	1	12
12	19	26	5	1	12
12	22	23	6	1	12
17	17	23	4	2	10
17	25	22	4	2	12
11	22	25	4	2	10
11	21	21	8	2	10
17	15	21	9	1	13
5	20	25	4	1	12
8	25	26	12	2	13
17	21	21	4	1	9
18	24	24	8	1	14
17	23	21	8	2	14
17	22	23	4	1	12
10	14	24	4	1	18
8	11	24	4	1	17
9	22	24	15	1	12
13	22	25	3	1	15
14	6	28	8	1	8
5	15	18	4	2	8
16	26	28	5	1	12
22	26	22	4	1	10
15	20	28	3	1	18
14	26	22	11	1	15
8	15	24	6	1	16
10	25	27	4	2	11
18	22	21	5	2	10
18	20	26	4	2	7
9	18	24	16	1	17
15	23	25	8	1	7
9	22	20	4	2	14
15	23	21	4	1	12
21	17	23	4	1	15
9	20	23	5	1	13
16	21	19	8	2	10
15	23	22	4	1	16
10	25	15	4	2	11
4	25	24	4	2	7
12	21	18	8	2	15
14	22	18	8	1	18
14	18	23	4	1	11
18	18	17	18	1	13
19	18	19	4	2	11
16	21	21	5	2	13
7	21	12	4	2	12
12	25	25	4	2	11
18	24	25	4	1	11
13	24	24	7	1	13
21	28	24	4	2	8
24	24	24	6	2	12
17	22	22	4	2	9
12	22	22	4	1	14
12	20	21	6	1	18
10	25	23	5	1	15
14	13	21	4	1	9
14	21	24	8	1	11
13	23	22	6	1	17
17	18	25	5	2	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time24 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 & 24 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113455&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]24 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=113455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113455&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 time24 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1758120.98446730.9571
C2499700.1236650.0704
Overall--0.6184--0.5106

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 758 & 12 & 0.9844 & 67 & 3 & 0.9571 \tabularnewline
C2 & 499 & 70 & 0.123 & 66 & 5 & 0.0704 \tabularnewline
Overall & - & - & 0.6184 & - & - & 0.5106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113455&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]758[/C][C]12[/C][C]0.9844[/C][C]67[/C][C]3[/C][C]0.9571[/C][/ROW]
[ROW][C]C2[/C][C]499[/C][C]70[/C][C]0.123[/C][C]66[/C][C]5[/C][C]0.0704[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6184[/C][C]-[/C][C]-[/C][C]0.5106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113455&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
C1758120.98446730.9571
C2499700.1236650.0704
Overall--0.6184--0.5106






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1840
C2640

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 84 & 0 \tabularnewline
C2 & 64 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113455&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]84[/C][C]0[/C][/ROW]
[ROW][C]C2[/C][C]64[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113455&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113455&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
C1840
C2640


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')
}