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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, 10 Dec 2010 17:49:01 +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/10/t1292003362hpvfp6m806jkxa2.htm/, Retrieved Mon, 29 Apr 2024 08:11:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107861, Retrieved Mon, 29 Apr 2024 08:11:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

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] [558c060a42ec367ec2c020fab85c25c7] [Current]
-   PD          [Recursive Partitioning (Regression Trees)] [] [2010-12-21 12:59:30] [39e83c7b0ac936e906a817a1bb402750]
-    D            [Recursive Partitioning (Regression Trees)] [] [2010-12-24 10:41:56] [39e83c7b0ac936e906a817a1bb402750]
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Dataset

Dataseries X:
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23	13	14	22	11	23	8	1	6	15
20	12	7	20	22	24	4	2	5	23
26	26	22	25	23	24	7	2	20	26
19	16	12	23	21	21	4	2	12	19
17	18	15	20	19	21	4	2	11	19
17	12	9	22	12	19	5	2	12	16
21	18	20	18	24	12	15	1	11	23
18	20	10	22	21	21	5	1	9	22
16	18	12	23	21	25	7	2	13	19
26	24	23	28	26	27	4	2	9	24
20	17	10	19	18	21	4	1	14	19
14	19	11	26	21	27	7	1	12	25
22	12	20	27	22	20	8	1	18	23
23	25	11	23	26	16	4	2	9	31
25	23	22	27	20	26	8	1	15	29
24	22	19	23	20	24	4	2	12	18
24	23	20	23	26	25	5	2	12	17
16	16	16	19	27	25	16	1	12	22
16	16	12	21	27	27	7	1	15	21
20	15	14	25	16	23	4	2	11	24
20	24	14	22	26	22	6	1	13	22
15	18	9	13	20	10	4	1	10	16
22	23	19	12	25	25	5	2	17	22
20	18	17	20	16	18	4	1	13	21
20	19	14	24	20	21	4	1	17	25
24	17	19	23	20	20	6	1	15	22
27	22	20	25	24	18	4	1	13	24
25	22	20	28	24	25	4	1	17	25
13	8	9	24	22	28	4	1	21	29
15	12	10	18	18	27	8	1	12	19
19	22	6	19	21	20	5	2	12	29
20	16	15	24	17	20	4	1	15	25
11	12	9	22	15	20	10	2	8	19
28	28	24	28	28	27	4	2	15	27
21	15	11	24	23	23	4	1	16	25
25	17	4	28	19	23	4	2	9	23
22	16	12	21	15	22	5	2	13	24
24	24	22	25	26	26	5	1	11	25
21	27	16	23	20	21	4	1	9	23
15	10	14	17	11	17	6	1	15	22
22	20	13	27	17	27	4	2	9	32
18	17	13	18	16	16	4	2	15	22
23	20	10	23	21	26	4	1	14	18
20	16	12	18	18	17	4	1	8	19
23	16	13	28	17	24	4	2	11	23
24	22	16	28	21	23	4	2	14	24
19	19	18	22	18	20	6	1	14	19
16	11	10	23	16	10	4	1	12	16
18	11	12	22	13	21	5	1	15	23
28	28	9	28	28	25	4	1	11	17
18	12	7	23	25	28	4	1	11	17
21	22	16	26	24	25	5	2	9	28
15	15	12	20	15	20	10	2	8	24
18	19	15	20	21	20	10	1	13	21
24	12	15	28	11	27	4	1	12	14
23	18	8	28	27	26	4	1	24	21
20	21	14	22	23	19	4	2	11	20
20	21	13	21	21	26	8	1	11	25
24	15	18	21	16	20	4	2	16	20
17	12	11	19	20	22	14	1	12	17
26	25	12	21	21	19	4	2	18	26
18	12	12	21	10	23	5	2	12	17
26	25	24	28	18	28	4	2	14	17
21	17	11	23	20	22	8	2	16	24
20	26	5	27	21	27	4	2	24	30
25	24	17	23	24	14	4	1	13	25
9	18	9	23	26	25	5	1	11	15
23	20	20	23	23	22	8	1	14	25
20	17	17	26	22	24	7	1	16	18
19	11	14	23	13	23	4	1	12	20
26	27	23	27	27	25	4	1	21	32
13	14	10	20	24	28	9	2	11	14
21	22	19	28	19	28	4	1	6	20
14	19	5	19	17	16	4	2	9	25
26	19	16	24	16	25	5	1	14	25
23	18	19	26	20	21	4	1	16	25
19	9	5	20	8	27	4	1	18	35
25	22	15	25	16	21	6	2	9	29
21	17	18	25	17	22	6	1	13	25
24	23	20	27	23	26	4	2	17	21
20	16	17	22	18	21	6	1	11	21
22	23	19	25	24	24	4	1	16	24
20	13	11	26	17	24	6	1	11	26
23	21	12	21	20	23	4	1	11	24
21	17	13	23	22	26	8	2	11	20
16	15	7	24	22	21	5	1	20	24
20	16	8	24	20	24	8	1	10	18
16	19	15	20	18	23	7	1	12	17
25	19	13	22	21	21	4	2	11	22
18	16	18	25	23	20	6	1	14	22
25	23	19	27	28	22	4	1	12	22
21	19	12	22	19	26	5	1	12	24
18	17	12	20	22	23	6	1	12	32
21	20	17	24	17	23	4	2	10	19
22	25	17	25	25	22	4	2	12	21
22	22	11	28	22	25	4	2	10	23
19	18	11	20	21	21	8	2	10	18
18	16	17	22	15	21	9	1	13	19
24	18	5	17	20	25	4	1	12	22
23	15	8	20	25	26	12	2	13	27
22	19	17	23	21	21	4	1	9	21
19	23	18	22	24	24	8	1	14	20
17	20	17	22	23	21	8	2	14	21
22	24	17	23	22	23	4	1	12	20
24	17	10	25	14	24	4	1	18	29
24	20	8	28	11	24	4	1	17	30
20	11	9	24	22	24	15	1	12	10
19	20	13	25	22	25	3	1	15	23
19	8	14	25	6	28	8	1	8	29
20	22	5	21	15	18	4	2	8	19
22	20	16	25	26	28	5	1	12	26
25	23	22	23	26	22	4	1	10	22
21	11	15	20	20	28	3	1	18	26
21	22	14	26	26	22	11	1	15	27
18	10	8	21	15	24	6	1	16	19
17	19	10	24	25	27	4	2	11	24
25	26	18	24	22	21	5	2	10	26
23	22	18	25	20	26	4	2	7	22
15	12	9	20	18	24	16	1	17	23
22	13	15	25	23	25	8	1	7	25
20	19	9	11	22	20	4	2	14	19
23	19	15	24	23	21	4	1	12	20
26	21	21	23	17	23	4	1	15	25
16	11	9	24	20	23	5	1	13	14
22	21	16	24	21	19	8	2	10	19
22	25	15	26	23	22	4	1	16	27
25	27	10	27	25	15	4	2	11	21
14	21	4	21	25	24	4	2	7	21
18	14	12	20	21	18	8	2	15	14
16	16	14	18	22	18	8	1	18	21
22	16	14	23	18	23	4	1	11	23
17	19	18	20	18	17	18	1	13	18
27	24	19	24	18	19	4	2	11	20
21	18	16	20	21	21	5	2	13	19
15	16	7	21	21	12	4	2	12	15
24	20	12	28	25	25	4	2	11	23
22	19	18	24	24	25	4	1	11	26
16	20	13	25	24	24	7	1	13	21
25	27	21	23	28	24	4	2	8	13
24	24	24	24	24	24	6	2	12	24
23	23	17	22	22	22	4	2	9	17
20	20	12	25	22	22	4	1	14	21
18	20	12	20	20	21	6	1	18	28
22	20	10	24	25	23	5	1	15	22
18	15	14	19	13	21	4	1	9	18
20	17	14	25	21	24	8	1	11	27
22	16	13	25	23	22	6	1	17	25
23	20	17	26	18	25	5	2	12	21

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107861&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]8 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=107861&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107861&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1701570.92487930.9634
C23652150.370735250.4167
Overall--0.6846--0.7324

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 701 & 57 & 0.9248 & 79 & 3 & 0.9634 \tabularnewline
C2 & 365 & 215 & 0.3707 & 35 & 25 & 0.4167 \tabularnewline
Overall & - & - & 0.6846 & - & - & 0.7324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107861&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]701[/C][C]57[/C][C]0.9248[/C][C]79[/C][C]3[/C][C]0.9634[/C][/ROW]
[ROW][C]C2[/C][C]365[/C][C]215[/C][C]0.3707[/C][C]35[/C][C]25[/C][C]0.4167[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6846[/C][C]-[/C][C]-[/C][C]0.7324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107861&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107861&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
C1701570.92487930.9634
C23652150.370735250.4167
Overall--0.6846--0.7324






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=107861&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=107861&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107861&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 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 5 ; 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')
}