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RP geslacht

*The author of this computation has been verified*
R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)
Title produced by software: Recursive Partitioning (Regression Trees)
Date of computation: Sun, 12 Dec 2010 20:15:25 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6.htm/, Retrieved Sun, 12 Dec 2010 21:13:28 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 1 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


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


Goodness of Fit
Correlation0.4266
R-squared0.182
RMSE0.4423


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.7777777777777780.222222222222222
210.7777777777777780.222222222222222
310.7538461538461540.246153846153846
400.326923076923077-0.326923076923077
510.7538461538461540.246153846153846
610.7538461538461540.246153846153846
710.7538461538461540.246153846153846
810.7777777777777780.222222222222222
910.7777777777777780.222222222222222
1010.3269230769230770.673076923076923
1100.326923076923077-0.326923076923077
1210.7538461538461540.246153846153846
1300.326923076923077-0.326923076923077
1410.7777777777777780.222222222222222
1500.326923076923077-0.326923076923077
1600.326923076923077-0.326923076923077
1710.7777777777777780.222222222222222
1800.753846153846154-0.753846153846154
1910.7777777777777780.222222222222222
2000.326923076923077-0.326923076923077
2110.7777777777777780.222222222222222
2210.7777777777777780.222222222222222
2300.777777777777778-0.777777777777778
2410.7538461538461540.246153846153846
2500.753846153846154-0.753846153846154
2610.7538461538461540.246153846153846
2700.326923076923077-0.326923076923077
2810.7538461538461540.246153846153846
2900.326923076923077-0.326923076923077
3010.7538461538461540.246153846153846
3110.3269230769230770.673076923076923
3200.326923076923077-0.326923076923077
3300.326923076923077-0.326923076923077
3400.326923076923077-0.326923076923077
3500.326923076923077-0.326923076923077
3610.3269230769230770.673076923076923
3700.326923076923077-0.326923076923077
3800.753846153846154-0.753846153846154
3910.7777777777777780.222222222222222
4000.777777777777778-0.777777777777778
4110.7538461538461540.246153846153846
4210.3269230769230770.673076923076923
4310.7538461538461540.246153846153846
4410.3269230769230770.673076923076923
4500.777777777777778-0.777777777777778
4610.7538461538461540.246153846153846
4700.326923076923077-0.326923076923077
4800.753846153846154-0.753846153846154
4910.7777777777777780.222222222222222
5000.753846153846154-0.753846153846154
5110.7538461538461540.246153846153846
5210.7538461538461540.246153846153846
5310.7538461538461540.246153846153846
5400.753846153846154-0.753846153846154
5510.7538461538461540.246153846153846
5600.326923076923077-0.326923076923077
5700.326923076923077-0.326923076923077
5810.3269230769230770.673076923076923
5910.7777777777777780.222222222222222
6010.7538461538461540.246153846153846
6100.777777777777778-0.777777777777778
6210.7538461538461540.246153846153846
6300.326923076923077-0.326923076923077
6410.7777777777777780.222222222222222
6500.326923076923077-0.326923076923077
6600.326923076923077-0.326923076923077
6710.7538461538461540.246153846153846
6810.3269230769230770.673076923076923
6900.326923076923077-0.326923076923077
7000.326923076923077-0.326923076923077
7110.7777777777777780.222222222222222
7210.7538461538461540.246153846153846
7300.753846153846154-0.753846153846154
7400.753846153846154-0.753846153846154
7500.326923076923077-0.326923076923077
7600.326923076923077-0.326923076923077
7710.7538461538461540.246153846153846
7800.753846153846154-0.753846153846154
7910.3269230769230770.673076923076923
8010.3269230769230770.673076923076923
8100.326923076923077-0.326923076923077
8210.7538461538461540.246153846153846
8310.7538461538461540.246153846153846
8410.7777777777777780.222222222222222
8510.7777777777777780.222222222222222
8610.7538461538461540.246153846153846
8710.7538461538461540.246153846153846
8810.7777777777777780.222222222222222
8910.3269230769230770.673076923076923
9010.3269230769230770.673076923076923
9100.326923076923077-0.326923076923077
9200.753846153846154-0.753846153846154
9310.3269230769230770.673076923076923
9400.753846153846154-0.753846153846154
9510.7777777777777780.222222222222222
9600.326923076923077-0.326923076923077
9710.7538461538461540.246153846153846
9800.753846153846154-0.753846153846154
9910.3269230769230770.673076923076923
10000.777777777777778-0.777777777777778
10110.7538461538461540.246153846153846
10210.7538461538461540.246153846153846
10310.7538461538461540.246153846153846
10410.7538461538461540.246153846153846
10500.753846153846154-0.753846153846154
10610.7538461538461540.246153846153846
10700.326923076923077-0.326923076923077
10810.7777777777777780.222222222222222
10910.7538461538461540.246153846153846
11010.7538461538461540.246153846153846
11110.7538461538461540.246153846153846
11210.7538461538461540.246153846153846
11310.7538461538461540.246153846153846
11410.7538461538461540.246153846153846
11500.326923076923077-0.326923076923077
11610.3269230769230770.673076923076923
11700.326923076923077-0.326923076923077
11810.3269230769230770.673076923076923
11910.7538461538461540.246153846153846
12010.7538461538461540.246153846153846
12100.326923076923077-0.326923076923077
12200.326923076923077-0.326923076923077
12310.7538461538461540.246153846153846
12400.753846153846154-0.753846153846154
12510.7777777777777780.222222222222222
12600.753846153846154-0.753846153846154
12700.326923076923077-0.326923076923077
12810.7538461538461540.246153846153846
12910.7538461538461540.246153846153846
13000.326923076923077-0.326923076923077
13100.326923076923077-0.326923076923077
13210.7538461538461540.246153846153846
13300.326923076923077-0.326923076923077
13410.7538461538461540.246153846153846
13510.7538461538461540.246153846153846
13600.777777777777778-0.777777777777778
13710.7538461538461540.246153846153846
13800.753846153846154-0.753846153846154
13910.3269230769230770.673076923076923
14010.7538461538461540.246153846153846
14110.7538461538461540.246153846153846
14210.7777777777777780.222222222222222
14310.7538461538461540.246153846153846
14410.3269230769230770.673076923076923
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/261ue1292184918.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/261ue1292184918.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/361ue1292184918.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/361ue1292184918.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/4ztbz1292184918.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t12921848072vmm4a6ee3vglr6/4ztbz1292184918.ps (open in new window)


 
Parameters (Session):
par1 = 6 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
 
Parameters (R input):
par1 = 1 ; par2 = none ; 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')
}
 





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We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


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