FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationThu, 16 Dec 2010 18:17:52 +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/16/t1292523339i7ch1l8fq4schrg.htm/, Retrieved Fri, 03 May 2024 04:15:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111138, Retrieved Fri, 03 May 2024 04:15:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

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)] [] [2010-12-14 19:33:19] [1908ef7bb1a3d37a854f5aaad1a1c348]
-   P       [Recursive Partitioning (Regression Trees)] [WS10 - Review] [2010-12-16 18:17:52] [cfd788255f1b1b5389e58d7f218c70bf] [Current]
-             [Recursive Partitioning (Regression Trees)] [WS10 - Review] [2010-12-16 18:20:13] [4a7069087cf9e0eda253aeed7d8c30d6]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6	4	15	10	4	4	1
11	9	9	19	7	7	1
9	9	12	15	4	4	1
14	6	16	12	5	4	1
12	8	16	14	5	6	1
18	11	15	13	4	4	1
15	10	16	11	4	5	1
12	13	13	18	5	5	1
15	10	18	12	5	4	1
13	6	17	15	3	4	1
10	8	14	15	7	7	1
13	5	13	9	4	5	1
17	9	15	11	6	5	1
15	11	15	16	5	4	1
13	11	13	17	7	7	1
17	9	13	11	5	5	1
21	7	16	13	5	5	1
12	6	14	9	4	4	1
15	6	18	11	4	4	1
16	10	16	12	7	7	1
11	4	17	13	5	8	1
9	9	15	13	2	2	1
14	10	11	13	4	3	1
14	13	11	14	5	7	1
12	8	15	9	4	5	1
15	10	15	9	4	4	1
11	5	12	15	4	4	1
11	8	17	10	4	4	1
13	9	14	15	5	6	1
12	7	17	13	4	6	1
24	20	10	24	4	4	1
11	8	15	13	4	4	1
12	7	7	22	2	4	1
13	6	9	9	5	5	1
11	10	14	12	5	7	1
14	11	11	16	7	8	1
16	12	15	10	7	7	1
12	7	16	13	4	4	1
21	12	17	11	4	4	1
6	6	15	13	4	2	1
14	9	15	10	2	4	1
16	5	16	11	5	4	1
18	11	16	9	4	4	1
13	10	12	14	2	4	1
11	7	15	11	4	5	1
16	8	17	10	4	5	1
11	9	19	11	5	5	1
11	8	15	12	1	1	1
20	13	14	14	4	5	1
10	7	16	21	5	7	1
12	7	15	13	5	7	1
14	9	12	12	7	7	1
12	9	18	12	4	4	1
12	8	13	11	4	4	1
12	7	14	14	4	4	1
13	10	15	12	2	2	1
12	7	11	12	5	4	1
9	7	15	11	4	4	1
14	10	14	15	4	4	1
12	8	16	11	4	4	1
18	5	14	22	5	7	1
17	8	18	10	3	4	1
15	9	14	11	5	5	1
8	11	13	15	4	4	1
12	8	14	11	4	4	1
10	4	17	10	5	5	1
18	16	12	14	4	7	1
15	9	16	14	6	7	1
16	10	15	11	7	8	1
17	11	16	10	5	5	1
7	8	14	12	4	4	1
12	8	17	10	5	7	1
15	6	14	12	4	1	1
13	8	16	15	4	4	1
16	14	12	11	3	4	1
18	12	13	17	2	7	1
11	11	19	8	1	1	1
13	8	11	17	4	4	1
11	8	15	13	4	2	1
13	7	12	16	4	4	1
14	9	14	13	1	1	1
18	12	11	15	4	3	1
15	6	15	14	4	4	1
9	4	12	18	5	5	1
11	6	14	14	4	4	1
17	7	13	10	6	6	1
5	4	9	20	4	4	2
20	10	12	16	4	5	2
12	6	15	10	7	7	2
11	5	17	8	7	7	2
12	8	14	14	4	4	2
13	8	11	23	5	4	2
9	11	13	9	4	2	2
9	5	10	11	3	5	2
12	7	12	10	5	7	2
12	7	15	12	5	4	2
11	8	13	10	4	4	2
17	7	13	12	7	4	2
12	7	12	14	4	4	2
8	5	9	20	4	1	2
15	4	16	8	1	1	2
9	8	17	10	5	5	2
13	6	13	11	4	4	2
9	6	10	15	4	4	2
15	9	13	12	5	5	2
14	6	16	9	4	4	2
9	6	15	13	4	5	2
8	9	16	8	4	4	2
11	8	11	11	6	3	2
16	7	15	12	6	6	2
18	10	17	11	2	2	2
12	5	14	15	1	1	2
14	8	18	7	4	3	2
16	9	14	14	4	4	2
24	20	14	10	2	2	2
11	8	12	11	4	4	2
9	6	11	13	4	4	2
17	8	14	14	3	3	2
11	10	16	14	4	3	2
11	8	17	11	4	3	2
10	6	14	13	4	4	2
12	8	14	13	4	4	2
10	8	12	12	4	4	2
10	8	12	12	5	4	2
13	8	11	18	3	4	2
14	9	15	13	7	7	2
8	7	14	14	4	4	2
11	12	10	15	4	4	2
10	8	13	11	4	4	2
7	4	15	10	4	4	2
9	6	15	12	5	6	2
11	10	16	10	4	4	2
7	5	8	20	4	4	2
15	8	9	19	5	4	2
11	8	15	11	5	8	2
13	9	11	13	4	1	2
12	6	15	9	4	4	2
11	5	16	10	7	7	2
8	4	16	12	4	3	2
12	9	15	14	2	2	2
9	5	13	11	3	5	2
12	9	15	8	5	4	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'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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111138&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111138&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111138&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C13934
C2960

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 39 & 34 \tabularnewline
C2 & 9 & 60 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111138&T=1

[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]39[/C][C]34[/C][/ROW]
[ROW][C]C2[/C][C]9[/C][C]60[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111138&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111138&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)
C1C2
C13934
C2960


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 3 ; par2 = quantiles ; par3 = 2 ; 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')
}