Home » date » 2010 » Dec » 13 »

*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: Mon, 13 Dec 2010 20:56:52 +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/13/t1292273694ws27sddgqyxu6f9.htm/, Retrieved Mon, 13 Dec 2010 21:54:57 +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/13/t1292273694ws27sddgqyxu6f9.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 «
0 13 13 14 13 3 0 12 12 8 13 5 1 15 10 12 16 6 1 12 9 7 12 6 1 10 10 10 11 5 1 12 12 7 12 3 0 15 13 16 18 8 1 9 12 11 11 4 0 12 12 14 14 4 0 11 6 6 9 4 1 11 5 16 14 6 0 11 12 11 12 6 0 15 11 16 11 5 1 7 14 12 12 4 0 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 0 14 11 15 16 6 0 10 11 7 9 4 0 6 7 9 11 4 0 11 9 7 13 2 0 15 11 14 15 7 0 11 11 15 10 5 0 12 12 7 11 4 1 14 12 15 13 6 1 15 11 17 16 6 0 9 11 15 15 7 1 13 8 14 14 5 1 13 9 14 14 6 1 16 12 8 14 4 1 13 10 8 8 4 0 12 10 14 13 7 1 14 12 14 15 7 1 11 8 8 13 4 0 9 12 11 11 4 0 16 11 16 15 6 1 12 12 10 15 6 0 10 7 8 9 5 1 13 11 14 13 6 1 16 11 16 16 7 1 14 12 13 13 6 1 15 9 5 11 3 1 5 15 8 12 3 0 8 11 10 12 4 0 11 11 8 12 6 1 16 11 13 14 7 1 17 11 15 14 5 1 9 15 6 8 4 1 9 11 12 13 5 1 13 12 16 16 6 1 10 12 5 13 6 0 6 9 15 11 6 1 12 12 12 14 5 1 8 12 8 13 4 1 14 13 13 13 5 1 12 11 14 13 5 0 11 9 12 12 4 0 16 9 16 16 6 1 8 11 10 15 2 0 15 11 15 15 8 1 7 12 8 12 3 0 16 12 16 14 6 1 14 9 19 12 6 1 16 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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132


Goodness of Fit
Correlation0.6099
R-squared0.372
RMSE2.1109


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11312.83333333333330.166666666666666
21311.32142857142861.67857142857143
31612.26666666666673.73333333333333
41211.32142857142860.678571428571429
51112.9047619047619-1.90476190476191
61211.32142857142860.678571428571429
71815.14705882352942.85294117647059
81112.9047619047619-1.90476190476191
91412.90476190476191.09523809523809
10911.3214285714286-2.32142857142857
111412.90476190476191.09523809523809
121212.9047619047619-0.904761904761905
131112.8333333333333-1.83333333333333
141212.9047619047619-0.904761904761905
151311.32142857142861.67857142857143
161112.9047619047619-1.90476190476191
171212.9047619047619-0.904761904761905
181615.14705882352940.852941176470589
19911.3214285714286-2.32142857142857
201112.9047619047619-1.90476190476191
211311.32142857142861.67857142857143
221515.1470588235294-0.147058823529411
231012.9047619047619-2.90476190476191
241111.3214285714286-0.321428571428571
251315.1470588235294-2.14705882352941
261615.14705882352940.852941176470589
271512.90476190476192.09523809523809
281412.26666666666671.73333333333333
291412.26666666666671.73333333333333
301412.83333333333331.16666666666667
31812.2666666666667-4.26666666666667
321312.90476190476190.095238095238095
331515.1470588235294-0.147058823529411
341311.32142857142861.67857142857143
351112.9047619047619-1.90476190476191
361515.1470588235294-0.147058823529411
371512.90476190476192.09523809523809
38911.3214285714286-2.32142857142857
391315.1470588235294-2.14705882352941
401615.14705882352940.852941176470589
411315.1470588235294-2.14705882352941
421112.2666666666667-1.26666666666667
431211.32142857142860.678571428571429
441212.9047619047619-0.904761904761905
451211.32142857142860.678571428571429
461415.1470588235294-1.14705882352941
471412.83333333333331.16666666666667
48811.3214285714286-3.32142857142857
491312.90476190476190.095238095238095
501615.14705882352940.852941176470589
511311.32142857142861.67857142857143
521112.9047619047619-1.90476190476191
531412.90476190476191.09523809523809
541311.32142857142861.67857142857143
551312.83333333333330.166666666666666
561312.90476190476190.095238095238095
571212.9047619047619-0.904761904761905
581612.26666666666673.73333333333333
591512.90476190476192.09523809523809
601515.1470588235294-0.147058823529411
611211.32142857142860.678571428571429
621415.1470588235294-1.14705882352941
631212.2666666666667-0.266666666666667
641515.1470588235294-0.147058823529411
651211.32142857142860.678571428571429
661312.83333333333330.166666666666666
671212.9047619047619-0.904761904761905
681212.8333333333333-0.833333333333334
691315.1470588235294-2.14705882352941
70511.3214285714286-6.32142857142857
711312.83333333333330.166666666666666
721312.83333333333330.166666666666666
731412.90476190476191.09523809523809
741712.90476190476194.09523809523809
751312.90476190476190.095238095238095
761312.90476190476190.095238095238095
771212.9047619047619-0.904761904761905
781312.90476190476190.095238095238095
791412.83333333333331.16666666666667
801111.3214285714286-0.321428571428571
811211.32142857142860.678571428571429
821212.2666666666667-0.266666666666667
831615.14705882352940.852941176470589
841212.9047619047619-0.904761904761905
851212.8333333333333-0.833333333333334
861212.9047619047619-0.904761904761905
871011.3214285714286-1.32142857142857
881512.90476190476192.09523809523809
891515.1470588235294-0.147058823529411
901212.9047619047619-0.904761904761905
911615.14705882352940.852941176470589
921512.90476190476192.09523809523809
931615.14705882352940.852941176470589
941312.26666666666670.733333333333333
951212.9047619047619-0.904761904761905
961111.3214285714286-0.321428571428571
971311.32142857142861.67857142857143
981011.3214285714286-1.32142857142857
991512.90476190476192.09523809523809
1001315.1470588235294-2.14705882352941
1011615.14705882352940.852941176470589
1021515.1470588235294-0.147058823529411
1031815.14705882352942.85294117647059
1041311.32142857142861.67857142857143
1051011.3214285714286-1.32142857142857
1061615.14705882352940.852941176470589
1071312.90476190476190.095238095238095
1081515.1470588235294-0.147058823529411
1091412.90476190476191.09523809523809
1101512.90476190476192.09523809523809
1111412.90476190476191.09523809523809
1121312.90476190476190.095238095238095
1131311.32142857142861.67857142857143
1141515.1470588235294-0.147058823529411
1151615.14705882352940.852941176470589
1161415.1470588235294-1.14705882352941
117610.24-4.24
1181410.243.76
1191112.2666666666667-1.26666666666667
1201210.241.76
1211212.8333333333333-0.833333333333334
122410.24-6.24
1231615.14705882352940.852941176470589
1241212.2666666666667-0.266666666666667
1251410.243.76
1261310.242.76
127510.24-5.24
1281610.245.76
1291112.2666666666667-1.26666666666667
130810.24-2.24
1311515.1470588235294-0.147058823529411
132610.24-4.24
1331410.243.76
1341212.2666666666667-0.266666666666667
1351510.244.76
1361412.90476190476191.09523809523809
137410.24-6.24
1381515.1470588235294-0.147058823529411
1391012.2666666666667-2.26666666666667
1401012.9047619047619-2.90476190476191
1411311.32142857142861.67857142857143
142510.24-5.24
1431515.1470588235294-0.147058823529411
1441212.2666666666667-0.266666666666667
1451210.241.76
1461615.14705882352940.852941176470589
147410.24-6.24
1481410.243.76
1491110.240.76
1501310.242.76
1511210.241.76
1521010.24-0.24
153910.24-1.24
1541010.24-0.24
1551210.241.76
1561310.242.76
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/2lhvl1292273804.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/2lhvl1292273804.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/3lhvl1292273804.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/3lhvl1292273804.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/4eqc61292273804.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292273694ws27sddgqyxu6f9/4eqc61292273804.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;
 
Parameters (R input):
par1 = 5 ; par2 = none ; par3 = 3 ; 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')
}
 





Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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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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