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Description of Statistical Computation

Author's title

Paper: Recursive Partitioning (with categorization, 2 classes based on quan...

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 10:24:09 +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/t12919766290lqy1vle9cvuaji.htm/, Retrieved Mon, 29 Apr 2024 12:06:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107499, Retrieved Mon, 29 Apr 2024 12:06:29 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact222

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)] [Paper: Recursive ...] [2010-12-10 10:24:09] [380f6bceef280be3d93cc6fafd18141e] [Current]
-           [Recursive Partitioning (Regression Trees)] [ws10 RP (Cross Va...] [2010-12-13 09:04:11] [e4076051fbfb461c886b1e223cd7862f]
-           [Recursive Partitioning (Regression Trees)] [ws10 RP (Cross Va...] [2010-12-13 09:04:11] [e4076051fbfb461c886b1e223cd7862f]
-           [Recursive Partitioning (Regression Trees)] [ws10 RP (Cross Va...] [2010-12-13 09:04:11] [e4076051fbfb461c886b1e223cd7862f]
-           [Recursive Partitioning (Regression Trees)] [ws10 RP (Cross Va...] [2010-12-13 09:10:46] [e4076051fbfb461c886b1e223cd7862f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24	14	11	12	24	26	14	2
25	11	7	8	25	23	18	2
17	6	17	8	30	25	11	2
18	12	10	8	19	23	12	1
18	8	12	9	22	19	16	2
16	10	12	7	22	29	18	2
20	10	11	4	25	25	14	2
16	11	11	11	23	21	14	2
18	16	12	7	17	22	15	2
17	11	13	7	21	25	15	2
23	13	14	12	19	24	17	1
30	12	16	10	19	18	19	2
23	8	11	10	15	22	10	1
18	12	10	8	16	15	16	2
15	11	11	8	23	22	18	2
12	4	15	4	27	28	14	1
21	9	9	9	22	20	14	1
15	8	11	8	14	12	17	2
20	8	17	7	22	24	14	1
31	14	17	11	23	20	16	2
27	15	11	9	23	21	18	1
34	16	18	11	21	20	11	2
21	9	14	13	19	21	14	2
31	14	10	8	18	23	12	2
19	11	11	8	20	28	17	1
16	8	15	9	23	24	9	2
20	9	15	6	25	24	16	1
21	9	13	9	19	24	14	2
22	9	16	9	24	23	15	2
17	9	13	6	22	23	11	1
24	10	9	6	25	29	16	2
25	16	18	16	26	24	13	1
26	11	18	5	29	18	17	2
25	8	12	7	32	25	15	2
17	9	17	9	25	21	14	1
32	16	9	6	29	26	16	1
33	11	9	6	28	22	9	1
13	16	12	5	17	22	15	1
32	12	18	12	28	22	17	2
25	12	12	7	29	23	13	1
29	14	18	10	26	30	15	1
22	9	14	9	25	23	16	2
18	10	15	8	14	17	16	1
17	9	16	5	25	23	12	1
20	10	10	8	26	23	12	2
15	12	11	8	20	25	11	2
20	14	14	10	18	24	15	2
33	14	9	6	32	24	15	2
29	10	12	8	25	23	17	2
23	14	17	7	25	21	13	1
26	16	5	4	23	24	16	2
18	9	12	8	21	24	14	1
20	10	12	8	20	28	11	1
11	6	6	4	15	16	12	2
28	8	24	20	30	20	12	1
26	13	12	8	24	29	15	2
22	10	12	8	26	27	16	2
17	8	14	6	24	22	15	2
12	7	7	4	22	28	12	1
14	15	13	8	14	16	12	2
17	9	12	9	24	25	8	1
21	10	13	6	24	24	13	1
19	12	14	7	24	28	11	2
18	13	8	9	24	24	14	2
10	10	11	5	19	23	15	2
29	11	9	5	31	30	10	1
31	8	11	8	22	24	11	2
19	9	13	8	27	21	12	1
9	13	10	6	19	25	15	2
20	11	11	8	25	25	15	1
28	8	12	7	20	22	14	1
19	9	9	7	21	23	16	2
30	9	15	9	27	26	15	2
29	15	18	11	23	23	15	1
26	9	15	6	25	25	13	1
23	10	12	8	20	21	12	2
13	14	13	6	21	25	17	2
21	12	14	9	22	24	13	2
19	12	10	8	23	29	15	1
28	11	13	6	25	22	13	1
23	14	13	10	25	27	15	1
18	6	11	8	17	26	16	1
21	12	13	8	19	22	15	2
20	8	16	10	25	24	16	1
23	14	8	5	19	27	15	2
21	11	16	7	20	24	14	2
21	10	11	5	26	24	15	1
15	14	9	8	23	29	14	2
28	12	16	14	27	22	13	2
19	10	12	7	17	21	7	2
26	14	14	8	17	24	17	2
10	5	8	6	19	24	13	2
16	11	9	5	17	23	15	2
22	10	15	6	22	20	14	2
19	9	11	10	21	27	13	2
31	10	21	12	32	26	16	2
31	16	14	9	21	25	12	2
29	13	18	12	21	21	14	2
19	9	12	7	18	21	17	1
22	10	13	8	18	19	15	1
23	10	15	10	23	21	17	2
15	7	12	6	19	21	12	1
20	9	19	10	20	16	16	2
18	8	15	10	21	22	11	1
23	14	11	10	20	29	15	2
25	14	11	5	17	15	9	1
21	8	10	7	18	17	16	2
24	9	13	10	19	15	15	1
25	14	15	11	22	21	10	1
17	14	12	6	15	21	10	2
13	8	12	7	14	19	15	2
28	8	16	12	18	24	11	2
21	8	9	11	24	20	13	2
25	7	18	11	35	17	14	1
9	6	8	11	29	23	18	2
16	8	13	5	21	24	16	1
19	6	17	8	25	14	14	2
17	11	9	6	20	19	14	2
25	14	15	9	22	24	14	2
20	11	8	4	13	13	14	2
29	11	7	4	26	22	12	2
14	11	12	7	17	16	14	2
22	14	14	11	25	19	15	2
15	8	6	6	20	25	15	2
19	20	8	7	19	25	15	2
20	11	17	8	21	23	13	2
15	8	10	4	22	24	17	1
20	11	11	8	24	26	17	2
18	10	14	9	21	26	19	2
33	14	11	8	26	25	15	2
22	11	13	11	24	18	13	1
16	9	12	8	16	21	9	1
17	9	11	5	23	26	15	2
16	8	9	4	18	23	15	1
21	10	12	8	16	23	15	1
26	13	20	10	26	22	16	2
18	13	12	6	19	20	11	1
18	12	13	9	21	13	14	1
17	8	12	9	21	24	11	2
22	13	12	13	22	15	15	2
30	14	9	9	23	14	13	1
30	12	15	10	29	22	15	2
24	14	24	20	21	10	16	1
21	15	7	5	21	24	14	2
21	13	17	11	23	22	15	1
29	16	11	6	27	24	16	2
31	9	17	9	25	19	16	2
20	9	11	7	21	20	11	1
16	9	12	9	10	13	12	1
22	8	14	10	20	20	9	1
20	7	11	9	26	22	16	2
28	16	16	8	24	24	13	2
38	11	21	7	29	29	16	1
22	9	14	6	19	12	12	2
20	11	20	13	24	20	9	2
17	9	13	6	19	21	13	2
28	14	11	8	24	24	13	2
22	13	15	10	22	22	14	2
31	16	19	16	17	20	19	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107499&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107499&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107499&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C16371320.828374170.8132
C23273200.494645380.4578
Overall--0.6758--0.6437

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 637 & 132 & 0.8283 & 74 & 17 & 0.8132 \tabularnewline
C2 & 327 & 320 & 0.4946 & 45 & 38 & 0.4578 \tabularnewline
Overall & - & - & 0.6758 & - & - & 0.6437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107499&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]637[/C][C]132[/C][C]0.8283[/C][C]74[/C][C]17[/C][C]0.8132[/C][/ROW]
[ROW][C]C2[/C][C]327[/C][C]320[/C][C]0.4946[/C][C]45[/C][C]38[/C][C]0.4578[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6758[/C][C]-[/C][C]-[/C][C]0.6437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107499&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107499&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
C16371320.828374170.8132
C23273200.494645380.4578
Overall--0.6758--0.6437






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C17313
C23934

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 73 & 13 \tabularnewline
C2 & 39 & 34 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107499&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]13[/C][/ROW]
[ROW][C]C2[/C][C]39[/C][C]34[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107499&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107499&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
C17313
C23934


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')
}