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*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: Wed, 22 Dec 2010 10:41:46 +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/22/t1293014428k6x4n4d6hqvtj53.htm/, Retrieved Wed, 22 Dec 2010 11:40:32 +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/22/t1293014428k6x4n4d6hqvtj53.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 «
162556 162556 1081 1081 213118 213118 230380558 6282929 29790 29790 309 309 81767 81767 25266003 4324047 87550 87550 458 458 153198 153198 70164684 4108272 84738 0 588 0 -26007 0 -15292116 -1212617 54660 54660 299 299 126942 126942 37955658 1485329 42634 42634 156 156 157214 157214 24525384 1779876 40949 0 481 0 129352 0 62218312 1367203 42312 42312 323 323 234817 234817 75845891 2519076 37704 37704 452 452 60448 60448 27322496 912684 16275 16275 109 109 47818 47818 5212162 1443586 25830 0 115 0 245546 0 28237790 1220017 12679 0 110 0 48020 0 5282200 984885 18014 18014 239 239 -1710 -1710 -408690 1457425 43556 0 247 0 32648 0 8064056 -572920 24524 24524 497 497 95350 95350 47388950 929144 6532 0 103 0 151352 0 15589256 1151176 7123 0 109 0 288170 0 31410530 790090 20813 20813 502 502 114337 114337 57397174 774497 37597 37597 248 248 37884 37884 9395232 990576 17821 0 373 0 122844 0 45820812 454195 12988 12988 119 119 82340 82340 9798460 876607 22330 22330 84 84 79801 79801 6703284 711969 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 time22 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.


Goodness of Fit
Correlation0.7658
R-squared0.5865
RMSE287379.1411


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
162829292800348.6253482580.375
243240472800348.6251523698.375
341082722800348.6251307923.375
4-1212617381828-1594445
514853292800348.625-1315019.625
617798762800348.625-1020472.625
71367203802777564426
825190762800348.625-281272.625
99126842800348.625-1887664.625
1014435863818281061758
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12984885381828603057
1314574253818281075597
14-572920381828-954748
15929144802777126367
161151176706603.625444572.375
17790090706603.62583486.375
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199905762800348.625-1809772.625
20454195385829.19047619068365.8095238095
21876607498898.76377708.24
22711969802777-90808
23702380706603.625-4223.625
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25450033385829.19047619064203.8095238095
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27588864381828207036
28-37216263925.818181818-301141.818181818
29783310263925.818181818519384.181818182
3046735938182885531
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32608419498898.76109520.24
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38697458498898.76198559.24
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367325560325559.7050359710.294964028755203
368325560325559.7050359710.294964028755203
369325560325559.7050359710.294964028755203
370325560325559.7050359710.294964028755203
371325560325559.7050359710.294964028755203
372318519331959.363636364-13440.3636363636
37334322232403819184
374317234324038-6804
375325560325559.7050359710.294964028755203
376325560325559.7050359710.294964028755203
377314025331959.363636364-17934.3636363636
378320249315048.7083333335200.29166666669
379325560325559.7050359710.294964028755203
380325560325559.7050359710.294964028755203
381325560325559.7050359710.294964028755203
382349365331959.36363636417405.6363636364
383289197331959.363636364-42762.3636363636
384325560325559.7050359710.294964028755203
385329245331959.363636364-2714.36363636365
386240869263925.818181818-23056.8181818182
387327182321470.1052631585711.89473684208
388322876315048.7083333337827.29166666669
389323117321470.1052631581646.89473684208
390306351331959.363636364-25608.3636363636
391335137329643.2857142865493.71428571426
392308271315048.708333333-6777.70833333331
393301731315048.708333333-13317.7083333333
394382409315048.70833333367360.2916666667
395279230498898.76-219668.76
396298731315048.708333333-16317.7083333333
397243650263925.818181818-20275.8181818182
398532682498898.7633783.24
399319771315048.7083333334722.29166666669
400171493263925.818181818-92432.8181818182
401347262315048.70833333332213.2916666667
402343945315048.70833333328896.2916666667
403311874315048.708333333-3174.70833333331
404302211331959.363636364-29748.3636363636
405316708315048.7083333331659.29166666669
406333463331959.3636363641503.63636363635
407344282331959.36363636412322.6363636364
408319635315048.7083333334586.29166666669
409301186315048.708333333-13862.7083333333
410300381315048.708333333-14667.7083333333
411318765331959.363636364-13194.3636363636
412286146263925.81818181822220.1818181818
413306844331959.363636364-25115.3636363636
414307705263925.81818181843779.1818181818
415312448315048.708333333-2600.70833333331
416299715315048.708333333-15333.7083333333
417373399263925.818181818109473.181818182
418299446331959.363636364-32513.3636363636
419325586331959.363636364-6373.36363636365
420291221315048.708333333-23827.7083333333
421261173315048.708333333-53875.7083333333
422255027263925.818181818-8898.81818181818
423-78375263925.818181818-342300.818181818
424-58143263925.818181818-322068.818181818
425227033263925.818181818-36892.8181818182
426235098263925.818181818-28827.8181818182
42721267263925.818181818-242658.818181818
428238675802777-564102
429197687263925.818181818-66238.8181818182
43041834138182836513
431-297706381828-679534
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/2rgkq1293014483.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/2rgkq1293014483.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/3rgkq1293014483.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/3rgkq1293014483.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/4jp1b1293014483.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293014428k6x4n4d6hqvtj53/4jp1b1293014483.ps (open in new window)


 
Parameters (Session):
par1 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;
 
Parameters (R input):
par1 = 8 ; 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')
}
 




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Software written by Ed van Stee & Patrick Wessa


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