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Workshop 10 - Recursive Partitioning

*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 09:03:45 +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/t1292144510vbmfnxz3tzu1djo.htm/, Retrieved Sun, 12 Dec 2010 10:01:53 +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/t1292144510vbmfnxz3tzu1djo.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 24 14 11 12 24 26 0 25 11 7 8 25 23 0 17 6 17 8 30 25 1 18 12 10 8 19 23 1 18 8 12 9 22 19 1 16 10 12 7 22 29 1 20 10 11 4 25 25 1 16 11 11 11 23 21 1 18 16 12 7 17 22 1 17 11 13 7 21 25 0 23 13 14 12 19 24 0 30 12 16 10 19 18 1 23 8 11 10 15 22 1 18 12 10 8 16 15 1 15 11 11 8 23 22 1 12 4 15 4 27 28 0 21 9 9 9 22 20 1 15 8 11 8 14 12 1 20 8 17 7 22 24 0 31 14 17 11 23 20 0 27 15 11 9 23 21 1 34 16 18 11 21 20 1 21 9 14 13 19 21 1 31 14 10 8 18 23 1 19 11 11 8 20 28 0 16 8 15 9 23 24 1 20 9 15 6 25 24 1 21 9 13 9 19 24 1 22 9 16 9 24 23 1 17 9 13 6 22 23 1 24 10 9 6 25 29 0 25 16 18 16 26 24 0 26 11 18 5 29 18 1 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 1 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 1 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 0 20 10 10 8 26 23 1 15 12 11 8 20 25 1 20 14 14 10 18 24 1 33 14 9 6 32 24 0 29 10 12 8 25 23 1 23 14 17 7 25 21 0 26 16 5 4 23 24 1 18 9 12 8 21 2 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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132


Goodness of Fit
Correlation0.6299
R-squared0.3968
RMSE4.4307


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
12425.0714285714286-1.07142857142857
22520.42307692307694.57692307692308
31721.4615384615385-4.46153846153846
41820.4230769230769-2.42307692307692
51820.4230769230769-2.42307692307692
61618.24-2.24
72018.241.76
81620.4230769230769-4.42307692307692
91818.24-0.239999999999998
101718.24-1.24000000000000
112325.0714285714286-2.07142857142857
123020.42307692307699.57692307692308
132320.42307692307692.57692307692308
141820.4230769230769-2.42307692307692
151520.4230769230769-5.42307692307692
161221.4615384615385-9.46153846153846
172120.42307692307690.576923076923077
181520.4230769230769-5.42307692307692
192018.241.76
203125.07142857142865.92857142857143
212725.07142857142861.92857142857143
223425.07142857142868.92857142857143
232120.42307692307690.576923076923077
243125.07142857142865.92857142857143
251920.4230769230769-1.42307692307692
261620.4230769230769-4.42307692307692
272018.241.76
282120.42307692307690.576923076923077
292220.42307692307691.57692307692308
301718.24-1.24000000000000
312418.245.76
322529.8125-4.8125
332629.8125-3.8125
342521.46153846153853.53846153846154
351720.4230769230769-3.42307692307692
363229.81252.1875
373329.81253.1875
381318.24-5.24
393229.81252.1875
402529.8125-4.8125
412929.8125-0.8125
422220.42307692307691.57692307692308
431820.4230769230769-2.42307692307692
441718.24-1.24000000000000
452021.4615384615385-1.46153846153846
461520.4230769230769-5.42307692307692
472025.0714285714286-5.07142857142857
483329.81253.1875
492920.42307692307698.57692307692308
502318.244.76
512618.247.76
521820.4230769230769-2.42307692307692
532020.4230769230769-0.423076923076923
541118.24-7.24
552821.46153846153856.53846153846154
562625.07142857142860.928571428571427
572221.46153846153850.53846153846154
581718.24-1.24000000000000
591218.24-6.24
601425.0714285714286-11.0714285714286
611720.4230769230769-3.42307692307692
622118.242.76
631918.240.760000000000002
641825.0714285714286-7.07142857142857
651018.24-8.24
662929.8125-0.8125
673120.423076923076910.5769230769231
681921.4615384615385-2.46153846153846
69918.24-9.24
702020.4230769230769-0.423076923076923
712818.249.76
721918.240.760000000000002
733021.46153846153858.53846153846154
742925.07142857142863.92857142857143
752618.247.76
762320.42307692307692.57692307692308
771318.24-5.24
782120.42307692307690.576923076923077
791920.4230769230769-1.42307692307692
802818.249.76
812325.0714285714286-2.07142857142857
821820.4230769230769-2.42307692307692
832120.42307692307690.576923076923077
842020.4230769230769-0.423076923076923
852318.244.76
862118.242.76
872121.4615384615385-0.46153846153846
881525.0714285714286-10.0714285714286
892829.8125-1.8125
901918.240.760000000000002
912625.07142857142860.928571428571427
921018.24-8.24
931618.24-2.24
942218.243.76
951920.4230769230769-1.42307692307692
963121.46153846153859.53846153846154
973125.07142857142865.92857142857143
982925.07142857142863.92857142857143
991918.240.760000000000002
1002220.42307692307691.57692307692308
1012320.42307692307692.57692307692308
1021518.24-3.24
1032020.4230769230769-0.423076923076923
1041820.4230769230769-2.42307692307692
1052325.0714285714286-2.07142857142857
1062518.246.76
1072118.242.76
1082420.42307692307693.57692307692308
1092525.0714285714286-0.071428571428573
1101718.24-1.24000000000000
1111318.24-5.24
1122820.42307692307697.57692307692308
1132120.42307692307690.576923076923077
1142521.46153846153853.53846153846154
115921.4615384615385-12.4615384615385
1161618.24-2.24
1171920.4230769230769-1.42307692307692
1181718.24-1.24000000000000
1192525.0714285714286-0.071428571428573
1202018.241.76
1212929.8125-0.8125
1221418.24-4.24
1232225.0714285714286-3.07142857142857
1241518.24-3.24
1251918.240.760000000000002
1262020.4230769230769-0.423076923076923
1271518.24-3.24
1282020.4230769230769-0.423076923076923
1291820.4230769230769-2.42307692307692
1303329.81253.1875
1312220.42307692307691.57692307692308
1321620.4230769230769-4.42307692307692
1331718.24-1.24000000000000
1341618.24-2.24
1352120.42307692307690.576923076923077
1362629.8125-3.8125
1371818.24-0.239999999999998
1381820.4230769230769-2.42307692307692
1391720.4230769230769-3.42307692307692
1402225.0714285714286-3.07142857142857
1413025.07142857142864.92857142857143
1423029.81250.1875
1432425.0714285714286-1.07142857142857
1442118.242.76
1452125.0714285714286-4.07142857142857
1462929.8125-0.8125
1473120.423076923076910.5769230769231
1482018.241.76
1491620.4230769230769-4.42307692307692
1502220.42307692307691.57692307692308
1512021.4615384615385-1.46153846153846
1522825.07142857142862.92857142857143
1533829.81258.1875
1542218.243.76
1552020.4230769230769-0.423076923076923
1561718.24-1.24000000000000
1572825.07142857142862.92857142857143
1582225.0714285714286-3.07142857142857
1593125.07142857142865.92857142857143
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/2tx9g1292144616.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/2tx9g1292144616.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/3tx9g1292144616.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/3tx9g1292144616.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/4moq11292144616.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292144510vbmfnxz3tzu1djo/4moq11292144616.ps (open in new window)


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