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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, 17 Dec 2010 09:58:25 +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/17/t1292579815kktsusc8bibvg5o.htm/, Retrieved Mon, 06 May 2024 21:35:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111367, Retrieved Mon, 06 May 2024 21:35:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

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)] [] [2010-12-11 08:35:36] [f730b099f190102bcd41f590a8dae16d]
-   PD    [Recursive Partitioning (Regression Trees)] [] [2010-12-11 10:06:28] [f730b099f190102bcd41f590a8dae16d]
-           [Recursive Partitioning (Regression Trees)] [] [2010-12-11 10:24:39] [f730b099f190102bcd41f590a8dae16d]
-   PD        [Recursive Partitioning (Regression Trees)] [recursive partini...] [2010-12-15 19:17:31] [f730b099f190102bcd41f590a8dae16d]
-   P           [Recursive Partitioning (Regression Trees)] [recursive partini...] [2010-12-17 08:49:50] [f730b099f190102bcd41f590a8dae16d]
-   P               [Recursive Partitioning (Regression Trees)] [cross validation ...] [2010-12-17 09:58:25] [6ff6d3268c67efbfcd6d6506b34b66fb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12	18	9	51	15
15	11	9	42	14
12	16	8	46	10
15	15	15	47	18
9	19	11	33	11
11	18	8	47	12
11	14	9	32	15
15	18	6	53	17
11	14	11	33	7
10	12	16	37	18
11	16	7	49	18
11	9	15	43	11
14	17	10	43	12
13	17	6	46	11
16	12	12	42	16
13	11	14	40	14
14	17	9	42	13
9	16	14	44	17
12	12	14	46	13
13	16	8	45	12
16	14	10	49	12
15	12	9	43	9
5	14	11	37	18
11	15	9	45	14
17	11	10	45	12
9	14	8	31	12
13	15	14	33	9
10	16	10	44	12
12	15	14	38	11
11	16	15	33	13
16	9	11	47	13
15	15	8	48	6
14	17	10	54	21
16	17	10	43	11
9	15	9	54	9
14	13	13	44	18
15	15	10	45	15
15	15	11	44	11
13	14	10	47	14
12	7	16	43	12
12	13	6	33	8
12	15	11	46	11
14	13	14	47	17
6	16	9	47	16
14	12	11	43	13
12	14	12	44	13
16	15	9	47	13
14	15	14	47	15
10	17	8	46	12
16	16	10	47	12
15	14	8	46	15
10	16	11	36	21
8	10	14	30	24
13	15	10	49	15
16	13	9	55	17
11	16	8	52	16
14	18	8	47	15
9	14	16	33	11
14	14	13	44	15
8	14	13	42	12
8	14	8	55	14
11	15	9	42	12
12	14	11	46	20
14	15	9	46	17
16	12	14	33	11
16	19	7	53	11
12	15	11	44	12
12	16	9	53	15
12	17	8	44	10
11	11	14	35	14
4	15	12	40	16
16	11	12	44	18
15	15	6	46	6
10	17	16	45	16
13	14	8	53	11
12	14	12	48	10
7	16	12	55	15
19	16	9	47	14
12	14	11	43	7
12	13	13	47	12
10	13	11	47	13
16	12	12	44	14
13	11	10	42	13
16	13	13	51	12
9	15	9	54	11
12	13	8	51	13
13	15	9	42	12
10	12	14	41	10
12	17	14	49	9
11	10	14	42	11
7	18	14	41	14
11	14	8	41	24
14	16	11	43	11
6	13	13	33	14
15	14	9	42	12
12	9	16	37	5
15	13	14	42	11
9	15	12	43	10
13	16	4	33	15
12	16	13	44	8
11	17	14	52	18
16	13	10	45	10
10	12	8	36	11
14	12	9	43	12
8	8	15	32	7
16	14	9	45	16
9	13	8	45	17
6	10	11	49	9
12	11	9	44	13
8	12	12	41	10
14	14	13	44	10
8	11	9	37	13
7	15	7	40	7
16	13	10	50	13
11	15	11	47	9
13	13	8	33	9
5	10	14	33	9
11	15	16	45	14
11	16	11	43	8
7	16	9	0	11
13	15	12	46	11
12	14	20	36	8
9	11	11	42	11
10	9	10	41	15
12	15	7	46	12
8	17	8	48	11
11	15	14	45	12
14	14	16	11	12
4	11	12	33	13
15	15	8	47	12
14	13	11	42	9
14	17	10	55	11
8	9	14	40	8
16	15	10	46	12
15	12	13	45	20
14	15	11	46	16
12	11	16	38	9
8	14	10	40	12
8	14	11	42	17
10	16	9	53	11
14	16	11	43	11
14	13	14	41	15
14	16	14	51	11

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=111367&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=111367&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111367&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
C14672420.658734270.5574
C21984010.669426350.5738
Overall--0.6636--0.5656

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 467 & 242 & 0.6587 & 34 & 27 & 0.5574 \tabularnewline
C2 & 198 & 401 & 0.6694 & 26 & 35 & 0.5738 \tabularnewline
Overall & - & - & 0.6636 & - & - & 0.5656 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111367&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]467[/C][C]242[/C][C]0.6587[/C][C]34[/C][C]27[/C][C]0.5574[/C][/ROW]
[ROW][C]C2[/C][C]198[/C][C]401[/C][C]0.6694[/C][C]26[/C][C]35[/C][C]0.5738[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6636[/C][C]-[/C][C]-[/C][C]0.5656[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111367&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111367&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
C14672420.658734270.5574
C21984010.669426350.5738
Overall--0.6636--0.5656






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1734
C24818

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 73 & 4 \tabularnewline
C2 & 48 & 18 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111367&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]4[/C][/ROW]
[ROW][C]C2[/C][C]48[/C][C]18[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111367&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111367&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
C1734
C24818


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 4 ; 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')
}