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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: Tue, 14 Dec 2010 17:35:33 +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/14/t129234801226ibvnxdeaq006j.htm/, Retrieved Tue, 14 Dec 2010 18:33:32 +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/14/t129234801226ibvnxdeaq006j.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 «

1606 6 3,74 16 1391 1634 6,81 4,17 29 1621 2013 9,75 4,84 22 1837 1654 6,96 4,21 30 2132 1003 3,94 3,93 20 1489 1029 5 4,9 39 1817 1052 4,9 4,7 18 1586 1653 5,7 3,5 9,6 1565 1918 6,5 3,4 10,2 1787 1926 7,1 3,7 20,2 1804 1862 7,5 4 50 1763 1816 7,8 4,3 120 1675 1712 7 4,1 19,8 1575 1646 7,4 4,5 18 1524 1555 8,55 5,5 3 1686 1402 7,43 5,3 11 1800 1047 4,7 4,5 15 1442 891 4,7 5,3 27 1345 940 5,3 5,6 28 1500 1372 6,2 4,5 14 1556 2012 7,4 3,7 5,6 2012 1879 7,5 4 6,5 1618 1667 7,32 4,4 8,5 1487 1856 8,15 4,4 87,9 1607 1771 7,24 4,1 5,8 1308 1721 7,4 4,3 25,2 1429 1773 9,4 5,3 7,5 1596 1507 8,9 5,9 13,7 1884 1033 4,5 4,4 34 1262 1011 4,9 4,9 17 1283 1111 5,6 5,1 9 1346 1736 6,4 3,7 9,2 1505 1865 6 3,2 5 1151 2078 6,9 3,3 24 1600 1947 6,7 3,5 40 1420 1428 5,4 3,8 86,5 1073 1500 5,6 3,8 0,54 1076 1950 6,9 3,5 14 1510 1591 6,9 4,3 4,8 1345 1613 7 4,3 28 1631 1077 4 3,7 16 1135 880 3,7 4,2 5,8 1009 1128 4,9 4,3 16 1155 1320 5 3,8 9,1 1184 1692 5,7 3,4 6 1285 1575 6,1 3,9 17 12 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	29.5907

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	16	27.2106666666667	-11.2106666666667
2	29	27.2106666666667	1.78933333333333
3	22	27.2106666666667	-5.21066666666667
4	30	27.2106666666667	2.78933333333333
5	20	27.2106666666667	-7.21066666666667
6	39	27.2106666666667	11.7893333333333
7	18	27.2106666666667	-9.21066666666667
8	9.6	27.2106666666667	-17.6106666666667
9	10.2	27.2106666666667	-17.0106666666667
10	20.2	27.2106666666667	-7.01066666666667
11	50	27.2106666666667	22.7893333333333
12	120	27.2106666666667	92.7893333333333
13	19.8	27.2106666666667	-7.41066666666667
14	18	27.2106666666667	-9.21066666666667
15	3	27.2106666666667	-24.2106666666667
16	11	27.2106666666667	-16.2106666666667
17	15	27.2106666666667	-12.2106666666667
18	27	27.2106666666667	-0.210666666666668
19	28	27.2106666666667	0.789333333333332
20	14	27.2106666666667	-13.2106666666667
21	5.6	27.2106666666667	-21.6106666666667
22	6.5	27.2106666666667	-20.7106666666667
23	8.5	27.2106666666667	-18.7106666666667
24	87.9	27.2106666666667	60.6893333333333
25	5.8	27.2106666666667	-21.4106666666667
26	25.2	27.2106666666667	-2.01066666666667
27	7.5	27.2106666666667	-19.7106666666667
28	13.7	27.2106666666667	-13.5106666666667
29	34	27.2106666666667	6.78933333333333
30	17	27.2106666666667	-10.2106666666667
31	9	27.2106666666667	-18.2106666666667
32	9.2	27.2106666666667	-18.0106666666667
33	5	27.2106666666667	-22.2106666666667
34	24	27.2106666666667	-3.21066666666667
35	40	27.2106666666667	12.7893333333333
36	86.5	27.2106666666667	59.2893333333333
37	0.54	27.2106666666667	-26.6706666666667
38	14	27.2106666666667	-13.2106666666667
39	4.8	27.2106666666667	-22.4106666666667
40	28	27.2106666666667	0.789333333333332
41	16	27.2106666666667	-11.2106666666667
42	5.8	27.2106666666667	-21.4106666666667
43	16	27.2106666666667	-11.2106666666667
44	9.1	27.2106666666667	-18.1106666666667
45	6	27.2106666666667	-21.2106666666667
46	17	27.2106666666667	-10.2106666666667
47	26	27.2106666666667	-1.21066666666667
48	99.6	27.2106666666667	72.3893333333333
49	41	27.2106666666667	13.7893333333333
50	72	27.2106666666667	44.7893333333333
51	23	27.2106666666667	-4.21066666666667
52	42	27.2106666666667	14.7893333333333
53	40	27.2106666666667	12.7893333333333
54	18	27.2106666666667	-9.21066666666667
55	45	27.2106666666667	17.7893333333333
56	18	27.2106666666667	-9.21066666666667
57	2	27.2106666666667	-25.2106666666667
58	10	27.2106666666667	-17.2106666666667
59	13.6	27.2106666666667	-13.6106666666667
60	160	27.2106666666667	132.789333333333

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/2ei6v1292348126.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/2ei6v1292348126.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/3ei6v1292348126.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/3ei6v1292348126.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/4h14j1292348126.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129234801226ibvnxdeaq006j/4h14j1292348126.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 4 ; par2 = none ; 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')

}

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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