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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 computationWed, 15 Dec 2010 15:54:32 +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/15/t1292428491bqme4a5dql2xi7x.htm/, Retrieved Fri, 03 May 2024 13:35:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110503, Retrieved Fri, 03 May 2024 13:35:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

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 Recursive Pa...] [2010-12-13 18:39:24] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
-           [Recursive Partitioning (Regression Trees)] [Paper: recursive ...] [2010-12-15 15:54:32] [350231caf55a86a218fd48dc4d2e2f8b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	1	13	6	4	6	4	6
12	12	8	13	5	1	18	6	2	7	2	6
15	10	12	16	6	0	13	5	4	4	4	6
12	9	7	12	6	1	17	4	2	6	5	4
10	10	10	11	5	0	13	4	2	6	5	4
12	12	7	12	3	NA	17	6	5	5	2	6
15	13	16	18	8	NA	NA	3	5	6	3	6
9	12	11	11	4	1	13	3	3	3	2	6
12	12	14	14	4	1	13	6	4	6	4	6
11	6	6	9	4	1	18	3	2	5	5	5
11	5	16	14	6	1	13	6	2	2	5	6
11	12	11	12	6	1	13	6	2	6	3	6
15	11	16	11	5	1	13	4	2	6	5	4
7	14	12	12	4	0	13	5	5	5	4	5
11	14	7	13	6	1	14	4	4	6	3	5
11	12	13	11	4	1	13	6	5	5	2	6
10	12	11	12	6	1	17	6	2	6	2	6
14	11	15	16	6	1	14	6	3	4	5	5
10	11	7	9	4	1	12	4	6	5	2	4
6	7	9	11	4	1	13	5	6	3	3	6
11	9	7	13	2	1	17	2	6	3	3	3
15	11	14	15	7	1	13	6	2	6	2	6
11	11	15	10	5	NA	NA	6	5	5	4	5
12	12	7	11	4	NA	NA	NA	NA	NA	NA	NA
14	12	15	13	6	1	13	6	2	6	2	6
15	11	17	16	6	NA	NA	NA	NA	NA	NA	NA
9	11	15	15	7	1	13	5	7	5	3	5
13	8	14	14	5	1	14	4	6	5	2	4
13	9	14	14	6	0	17	NA	NA	NA	NA	NA
16	12	8	14	4	0	13	6	4	6	4	6
13	10	8	8	4	0	12	4	6	5	2	4
12	10	14	13	7	0	16	6	5	4	6	5
14	12	14	15	7	1	14	6	2	7	2	6
11	8	8	13	4	1	17	6	6	7	5	7
9	12	11	11	4	0	13	6	4	6	4	6
16	11	16	15	6	1	14	6	2	6	2	6
12	12	10	15	6	1	16	6	2	6	2	6
10	7	8	9	5	1	14	6	6	7	2	6
13	11	14	13	6	0	13	1	7	2	5	1
16	11	16	16	7	1	11	6	4	6	4	6
14	12	13	13	6	NA	12	4	6	5	2	4
15	9	5	11	3	NA	NA	4	6	5	2	4
5	15	8	12	3	1	13	6	2	6	2	5
8	11	10	12	4	1	15	6	2	6	5	6
11	11	8	12	6	1	13	4	2	6	5	4
16	11	13	14	7	1	13	4	4	6	3	5
17	11	15	14	5	0	13	6	2	6	2	6
9	15	6	8	4	0	14	5	5	5	6	4
9	11	12	13	5	1	11	6	2	6	2	6
13	12	16	16	6	0	14	5	2	4	2	6
10	12	5	13	6	1	12	NA	NA	NA	NA	NA
6	9	15	11	6	1	14	5	7	2	3	5
12	12	12	14	5	1	13	6	4	6	4	6
8	12	8	13	4	0	13	4	2	6	5	4
14	13	13	13	5	0	13	6	5	6	5	6
12	11	14	13	5	1	13	6	4	6	4	6
11	9	12	12	4	1	13	6	2	6	2	6
16	9	16	16	6	1	13	1	7	2	5	1
8	11	10	15	2	1	13	6	2	6	2	6
15	11	15	15	8	1	14	6	4	6	3	6
7	12	8	12	3	1	13	2	6	3	3	3
16	12	16	14	6	1	10	5	4	4	4	6
14	9	19	12	6	1	15	6	2	6	5	6
16	11	14	15	6	1	18	NA	NA	NA	NA	NA
9	9	6	12	5	1	13	4	3	4	5	5
14	12	13	13	5	1	13	6	4	6	4	6
11	12	15	12	6	0	16	4	2	7	2	4
13	12	7	12	5	NA	NA	NA	NA	NA	NA	NA
15	12	13	13	6	1	13	6	3	4	5	5
5	14	4	5	2	0	10	NA	NA	NA	NA	NA
15	11	14	13	5	0	13	2	6	3	3	3
13	12	13	13	5	1	13	6	4	6	4	6
11	11	11	14	5	1	13	6	2	6	2	6
11	6	14	17	6	1	13	6	6	7	2	6
12	10	12	13	6	0	13	6	6	7	5	7
12	12	15	13	6	1	13	4	4	6	3	5
12	13	14	12	5	1	13	6	2	6	2	6
12	8	13	13	5	1	13	6	6	7	5	7
14	12	8	14	4	1	13	6	4	6	4	6
6	12	6	11	2	1	13	6	2	6	2	6
7	12	7	12	4	0	13	6	4	6	4	6
14	6	13	12	6	1	NA	NA	NA	NA	NA	NA
14	11	13	16	6	1	13	6	4	6	4	6
10	10	11	12	5	1	13	6	2	6	2	6
13	12	5	12	3	1	15	6	2	6	3	6
12	13	12	12	6	0	13	4	3	4	5	5
9	11	8	10	4	1	17	4	4	6	3	5
12	7	11	15	5	NA	16	3	2	5	5	5
16	11	14	15	8	1	14	6	2	6	2	6
10	11	9	12	4	1	13	6	4	6	4	6
14	11	10	16	6	0	15	NA	NA	NA	NA	NA
10	11	13	15	6	0	NA	2	6	3	3	3
16	12	16	16	7	1	13	6	3	4	5	5
15	10	16	13	6	1	13	6	4	6	4	6
12	11	11	12	5	NA	NA	NA	NA	NA	NA	NA
10	12	8	11	4	NA	13	7	2	4	6	5
8	7	4	13	6	1	NA	4	4	6	3	5
8	13	7	10	3	1	16	6	2	6	2	6
11	8	14	15	5	1	13	6	4	6	4	6
13	12	11	13	6	1	13	6	4	6	4	6
16	11	17	16	7	1	15	6	2	6	5	7
16	12	15	15	7	NA	12	NA	NA	NA	NA	NA
14	14	17	18	6	1	15	6	2	6	2	4
11	10	5	13	3	1	18	NA	NA	NA	NA	NA
4	10	4	10	2	0	NA	NA	NA	NA	NA	NA
14	13	10	16	8	NA	18	NA	NA	NA	NA	NA
9	10	11	13	3	1	13	6	4	6	4	6
14	11	15	15	8	1	13	NA	NA	NA	NA	NA
8	10	10	14	3	1	18	6	6	7	5	7
8	7	9	15	4	1	11	6	2	6	2	6
11	10	12	14	5	0	18	6	6	7	5	7
12	8	15	13	7	1	13	2	6	3	3	3
11	12	7	13	6	1	13	NA	NA	NA	NA	NA
14	12	13	15	6	1	15	6	6	7	5	7
15	12	12	16	7	1	13	6	4	6	4	6
16	11	14	14	6	1	13	6	6	7	5	7
16	12	14	14	6	0	13	4	2	6	5	4
11	12	8	16	6	1	16	6	6	7	5	7
14	12	15	14	6	1	13	6	4	6	4	6
14	11	12	12	4	1	13	6	2	6	2	6
12	12	12	13	4	1	13	6	2	6	2	6
14	11	16	12	5	NA	NA	NA	NA	NA	NA	NA
8	11	9	12	4	1	9	NA	NA	NA	NA	NA
13	13	15	14	6	1	15	5	3	4	3	6
16	12	15	14	6	NA	NA	NA	NA	NA	NA	NA
12	12	6	14	5	1	13	6	2	6	3	6
16	12	14	16	8	0	13	2	6	3	3	3
12	12	15	13	6	1	13	6	2	6	2	6
11	8	10	14	5	1	15	5	3	4	3	6
4	8	6	4	4	1	13	6	4	6	4	6
16	12	14	16	8	1	13	6	4	6	4	6
15	11	12	13	6	NA	15	NA	NA	NA	NA	NA
10	12	8	16	4	0	16	5	3	4	3	6
13	13	11	15	6	1	13	5	3	4	3	6
15	12	13	14	6	NA	NA	NA	NA	NA	NA	NA
12	12	9	13	4	1	13	NA	NA	NA	NA	NA
14	11	15	14	6	0	13	6	6	7	5	7
7	12	13	12	3	1	16	5	5	5	6	4
19	12	15	15	6	1	NA	NA	NA	NA	NA	NA
12	10	14	14	5	1	13	6	2	6	2	6
12	11	16	13	4	1	13	6	3	4	5	5
13	12	14	14	6	1	13	6	2	6	2	6
15	12	14	16	4	1	16	6	2	6	2	6
8	10	10	6	4	NA	NA	NA	NA	NA	NA	NA
12	12	10	13	4	1	13	6	2	6	2	6
10	13	4	13	6	NA	13	6	2	6	5	5
8	12	8	14	5	0	13	6	2	6	2	6
10	15	15	15	6	1	13	6	4	6	4	6
15	11	16	14	6	NA	16	6	6	7	5	7
16	12	12	15	8	1	13	6	2	6	2	6
13	11	12	13	7	1	13	6	4	6	4	6
16	12	15	16	7	NA	13	6	6	7	5	7
9	11	9	12	4	1	16	6	2	6	2	6
14	10	12	15	6	1	13	6	6	7	5	7
14	11	14	12	6	1	13	6	2	6	2	6
12	11	11	14	2	NA	13	7	2	6	3	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110503&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]6 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=110503&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110503&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3
C146141
C219365
C321914

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 46 & 14 & 1 \tabularnewline
C2 & 19 & 36 & 5 \tabularnewline
C3 & 2 & 19 & 14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110503&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][/ROW]
[ROW][C]C1[/C][C]46[/C][C]14[/C][C]1[/C][/ROW]
[ROW][C]C2[/C][C]19[/C][C]36[/C][C]5[/C][/ROW]
[ROW][C]C3[/C][C]2[/C][C]19[/C][C]14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110503&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110503&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:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3
C146141
C219365
C321914


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = quantiles ; 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')
}