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Regression tree

*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 08:20:20 +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/t1293005929itmrxhbk3df077n.htm/, Retrieved Wed, 22 Dec 2010 09:18:50 +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/t1293005929itmrxhbk3df077n.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 «
21454 -11,5 0,012095933 8,02 8,3 20780 23899 -11 0,017384968 8,03 8,2 19815 24939 -14,9 0,017547503 8,45 8 19761 23580 -16,2 0,014844804 7,74 7,9 21454 24562 -14,4 0,010364842 7,26 7,6 23899 24696 -17,3 0,016214531 7,9 7,6 24939 23785 -15,7 0,014814047 7,34 8,3 23580 23812 -12,6 0,017823834 6,91 8,4 24562 21917 -9,4 0,017980779 7,22 8,4 24696 19713 -8,1 0,015828678 7,47 8,4 23785 19282 -5,4 0,018533858 7,16 8,4 23812 18788 -4,6 0,017385905 8,09 8,6 21917 21453 -4,9 0,015866474 7,91 8,9 19713 24482 -4 0,012585695 7,74 8,8 19282 27474 -3,1 0,011326531 8,01 8,3 18788 27264 -1,3 0,019230769 7,56 7,5 21453 27349 0 0,026056627 7,56 7,2 24482 30632 -0,4 0,022604071 8,06 7,4 27474 29429 3 0,024091466 8,06 8,8 27264 30084 0,4 0,022602321 7,87 9,3 27349 26290 1,2 0,020302507 7,97 9,3 30632 24379 0,6 0,028617986 7,89 8,7 29429 23335 -1,3 0,025515909 7,83 8,2 30084 21346 -3,2 0,022785068 8,17 8,3 26290 21106 -1,8 0,022515213 8,84 8,5 24379 24514 -3,6 0,025666936 8,44 8,6 23335 28353 -4,2 0,03067 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
Correlation0.8914
R-squared0.7946
RMSE3730.3825


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
12145424626.2962962963-3172.2962962963
22389924626.2962962963-727.296296296296
32493924626.2962962963312.703703703704
42358024626.2962962963-1046.2962962963
52456224626.2962962963-64.2962962962956
62469624626.296296296369.7037037037044
72378524626.2962962963-841.296296296296
82381233509.1428571429-9697.14285714286
92191724626.2962962963-2709.2962962963
101971324626.2962962963-4913.2962962963
111928224626.2962962963-5344.2962962963
121878824626.2962962963-5838.2962962963
132145324626.2962962963-3173.2962962963
142448224626.2962962963-144.296296296296
152747424626.29629629632847.7037037037
162726424626.29629629632637.7037037037
172734924626.29629629632722.7037037037
183063224626.29629629636005.7037037037
192942924626.29629629634802.7037037037
203008424626.29629629635457.7037037037
212629024626.29629629631663.7037037037
222437924626.2962962963-247.296296296296
232333524626.2962962963-1291.2962962963
242134624626.2962962963-3280.2962962963
252110624626.2962962963-3520.2962962963
262451424626.2962962963-112.296296296296
272835324626.29629629633726.7037037037
283080524626.29629629636178.7037037037
293134833509.1428571429-2161.14285714286
303455633509.14285714291046.85714285714
313385536645.0333333333-2790.03333333333
323478736645.0333333333-1858.03333333333
333252936645.0333333333-4116.03333333333
342999836645.0333333333-6647.03333333333
352925736645.0333333333-7388.03333333333
362815536645.0333333333-8490.03333333333
373046633509.1428571429-3043.14285714286
383570433509.14285714292194.85714285714
393932733509.14285714295817.85714285714
403935133509.14285714295841.85714285714
414223436645.03333333335588.96666666667
424363041307.18181818182322.81818181818
434372241307.18181818182414.81818181818
444312141307.18181818181813.81818181818
453798541307.1818181818-3322.18181818182
463713541307.1818181818-4172.18181818182
473464641307.1818181818-6661.18181818182
483302636645.0333333333-3619.03333333333
493508736645.0333333333-1558.03333333333
503884636645.03333333332200.96666666667
514201336645.03333333335367.96666666667
524390836645.03333333337262.96666666667
534286841307.18181818181560.81818181818
544442341307.18181818183115.81818181818
554416741307.18181818182859.81818181818
564363641307.18181818182328.81818181818
574438241307.18181818183074.81818181818
584214241307.1818181818834.818181818184
594345241307.18181818182144.81818181818
603691241307.1818181818-4395.18181818182
614241341307.18181818181105.81818181818
624534447160.5-1816.5
634487336645.03333333338227.96666666667
644751047160.5349.5
654955447160.52393.5
664736947160.5208.5
674599847160.5-1162.5
684814047160.5979.5
694844147160.51280.5
704492847160.5-2232.5
714045441307.1818181818-853.181818181816
723866141307.1818181818-2646.18181818182
733724641307.1818181818-4061.18181818182
743684341307.1818181818-4464.18181818182
753642436645.0333333333-221.033333333333
763759436645.0333333333948.966666666667
773814436645.03333333331498.96666666667
783873736645.03333333332091.96666666667
793456036645.0333333333-2085.03333333333
803608036645.0333333333-565.033333333333
813350836645.0333333333-3137.03333333333
823546236645.0333333333-1183.03333333333
833337436645.0333333333-3271.03333333333
843211036645.0333333333-4535.03333333333
853553336645.0333333333-1112.03333333333
863553236645.0333333333-1113.03333333333
873790336645.03333333331257.96666666667
883676336645.0333333333117.966666666667
894039936645.03333333333753.96666666667
904416436645.03333333337518.96666666667
914449636645.03333333337850.96666666667
924311041307.18181818181802.81818181818
934388041307.18181818182572.81818181818
944393041307.18181818182622.81818181818
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/2e8ah1293006012.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/2e8ah1293006012.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/3e8ah1293006012.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/3e8ah1293006012.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/47i921293006012.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293005929itmrxhbk3df077n/47i921293006012.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
 
Parameters (R input):
par1 = 1 ; 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

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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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