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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 computationSat, 18 Dec 2010 22:16:51 +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/18/t1292710484z5b0rtm4rn5q9ow.htm/, Retrieved Tue, 30 Apr 2024 08:03:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112213, Retrieved Tue, 30 Apr 2024 08:03:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

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] [2010-12-12 17:02:26] [87116ee6ef949037dfa02b8eb1a3bf97]
-   PD      [Recursive Partitioning (Regression Trees)] [RP - cross valida...] [2010-12-18 22:16:51] [66b4703b90a9701067ac75b10c82aca9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	11	11	26	9	2	1	1
18	12	8	20	9	1	1	1
11	15	12	21	9	4	1	1
12	10	10	31	14	1	1	2
16	12	7	21	8	5	2	1
18	11	6	18	8	1	1	1
14	5	8	26	11	1	1	1
14	16	16	22	10	1	1	1
15	11	8	22	9	1	1	1
15	15	16	29	15	1	1	1
17	12	7	15	14	2	1	2
19	9	11	16	11	1	1	1
10	11	16	24	14	3	2	2
18	15	16	17	6	1	1	1
14	12	12	19	20	1	1	2
14	16	13	22	9	1	1	2
17	14	19	31	10	1	1	1
14	11	7	28	8	1	1	2
16	10	8	38	11	2	1	1
18	7	12	26	14	4	2	2
14	11	13	25	11	1	1	1
12	10	11	25	16	2	1	1
17	11	8	29	14	1	1	2
9	16	16	28	11	2	4	1
16	14	15	15	11	3	1	2
14	12	11	18	12	1	1	1
11	12	12	21	9	1	2	2
16	11	7	25	7	1	2	1
13	6	9	23	13	1	1	2
17	14	15	23	10	1	1	1
15	9	6	19	9	2	1	1
14	15	14	18	9	1	1	2
16	12	14	18	13	1	1	2
9	12	7	26	16	1	1	2
15	9	15	18	12	1	1	2
17	13	14	18	6	1	1	1
13	15	17	28	14	1	1	2
15	11	14	17	14	1	1	2
16	10	5	29	10	2	2	1
16	13	14	12	4	1	1	2
12	16	8	28	12	1	1	1
11	13	8	20	14	1	1	1
15	14	13	17	9	2	1	1
17	14	14	17	9	1	1	1
13	16	16	20	10	1	1	2
16	9	11	31	14	1	1	1
14	8	10	21	10	1	1	2
11	8	10	19	9	1	1	2
12	12	10	23	14	1	1	1
12	10	8	15	8	4	1	2
15	16	14	24	9	2	1	1
16	13	14	28	8	1	1	1
15	11	12	16	9	1	1	1
12	14	13	19	9	4	3	2
12	15	5	21	9	2	2	1
8	8	10	21	15	1	1	2
13	9	6	20	8	1	1	2
11	17	15	16	10	1	1	1
14	9	12	25	8	1	1	1
15	13	16	30	14	1	1	1
10	6	15	29	11	1	1	2
11	13	12	22	10	2	1	1
12	8	8	19	12	1	1	2
15	12	14	33	14	1	1	1
15	13	14	17	9	2	1	2
14	14	13	9	13	1	1	2
16	11	12	14	15	2	2	1
15	15	15	15	8	2	1	1
15	7	8	12	7	4	1	2
13	16	16	21	10	1	1	2
17	16	14	20	10	1	1	1
13	14	13	29	13	3	2	1
15	11	15	33	11	1	1	2
13	13	7	21	8	1	1	2
15	13	5	15	12	1	1	2
16	7	7	19	9	1	1	2
15	15	13	23	10	1	1	1
16	11	14	20	11	1	1	2
15	15	14	20	11	1	1	1
14	13	13	18	10	1	1	1
15	11	11	31	16	4	1	2
7	12	15	18	16	1	1	1
17	10	13	13	8	1	1	1
13	12	14	9	6	2	1	1
15	12	13	20	11	1	1	1
14	12	9	18	12	1	1	1
13	14	8	23	14	1	2	1
16	6	6	17	9	1	1	1
12	14	13	17	11	1	1	1
14	15	16	16	8	1	1	1
17	8	7	31	8	1	1	2
15	12	11	15	7	1	1	2
17	10	8	28	16	1	1	1
12	15	13	26	13	1	1	2
16	11	5	20	8	1	2	1
11	9	8	19	11	1	2	2
15	14	10	25	14	5	1	1
9	10	9	18	10	1	1	2
16	16	16	20	10	1	1	1
10	5	4	33	14	1	1	2
10	8	4	24	14	3	3	1
15	13	11	22	10	1	1	1
11	16	14	32	12	1	1	1
13	16	15	31	9	1	1	1
14	14	17	13	16	1	1	2
18	14	10	18	8	1	1	1
16	10	15	17	9	1	1	2
14	9	11	29	16	1	1	1
14	14	15	22	13	2	1	1
14	8	10	18	13	4	1	1
14	8	9	22	8	4	3	1
12	16	14	25	14	1	1	1
14	12	15	20	11	1	1	1
15	9	9	20	9	1	1	1
15	15	12	17	8	4	3	1
13	12	10	26	13	2	3	1
17	14	16	10	10	1	1	2
17	12	15	15	8	1	2	1
19	16	14	20	7	1	1	1
15	12	12	14	11	1	1	1
13	14	15	16	11	1	1	2
9	8	9	23	14	1	2	2
15	15	12	11	6	2	2	1
15	16	15	19	10	4	1	2
16	12	6	30	9	4	1	1
11	4	4	21	12	1	1	2
14	8	8	20	11	1	1	2
11	11	10	22	14	1	1	1
15	4	6	30	12	2	3	1
13	14	12	25	14	1	1	2
16	14	14	23	14	1	1	2
14	13	11	23	8	3	1	1
15	14	15	21	11	2	1	2
16	7	13	30	12	2	1	1
16	19	15	22	9	1	1	1
11	12	16	32	16	1	1	2
13	10	4	22	11	2	2	1
16	14	15	15	11	3	1	2
12	16	12	21	12	1	1	1
9	11	15	27	15	1	1	1
13	16	15	22	13	1	2	1
13	12	14	9	6	2	1	1
14	12	14	29	11	2	1	1
19	16	14	20	7	1	1	1
13	12	11	16	8	1	1	1

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=112213&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=112213&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112213&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






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C14332500.63440270.597
C22613750.589640240.375
Overall--0.6126--0.4885

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 433 & 250 & 0.634 & 40 & 27 & 0.597 \tabularnewline
C2 & 261 & 375 & 0.5896 & 40 & 24 & 0.375 \tabularnewline
Overall & - & - & 0.6126 & - & - & 0.4885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112213&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]433[/C][C]250[/C][C]0.634[/C][C]40[/C][C]27[/C][C]0.597[/C][/ROW]
[ROW][C]C2[/C][C]261[/C][C]375[/C][C]0.5896[/C][C]40[/C][C]24[/C][C]0.375[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6126[/C][C]-[/C][C]-[/C][C]0.4885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112213&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112213&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
C14332500.63440270.597
C22613750.589640240.375
Overall--0.6126--0.4885






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C13639
C21852

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 36 & 39 \tabularnewline
C2 & 18 & 52 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112213&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]36[/C][C]39[/C][/ROW]
[ROW][C]C2[/C][C]18[/C][C]52[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112213&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112213&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
C13639
C21852


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 1 ; 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')
}