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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: Fri, 10 Dec 2010 15:32:27 +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/10/t1291996423is6mzj8uprkao20.htm/, Retrieved Fri, 10 Dec 2010 16:53:46 +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/10/t1291996423is6mzj8uprkao20.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 «

23 13 14 22 11 23 8 1 6 15 20 12 7 20 22 24 4 2 5 23 26 26 22 25 23 24 7 2 20 26 19 16 12 23 21 21 4 2 12 19 17 18 15 20 19 21 4 2 11 19 17 12 9 22 12 19 5 2 12 16 21 18 20 18 24 12 15 1 11 23 18 20 10 22 21 21 5 1 9 22 16 18 12 23 21 25 7 2 13 19 26 24 23 28 26 27 4 2 9 24 20 17 10 19 18 21 4 1 14 19 14 19 11 26 21 27 7 1 12 25 22 12 20 27 22 20 8 1 18 23 23 25 11 23 26 16 4 2 9 31 25 23 22 27 20 26 8 1 15 29 24 22 19 23 20 24 4 2 12 18 24 23 20 23 26 25 5 2 12 17 16 16 16 19 27 25 16 1 12 22 16 16 12 21 27 27 7 1 15 21 20 15 14 25 16 23 4 2 11 24 20 24 14 22 26 22 6 1 13 22 15 18 9 13 20 10 4 1 10 16 22 23 19 12 25 25 5 2 17 22 20 18 17 20 16 18 4 1 13 21 20 19 14 24 20 21 4 1 17 25 24 17 19 23 20 20 6 1 15 22 27 22 20 25 24 18 4 1 13 24 25 22 20 28 24 25 4 1 17 25 13 8 9 24 22 28 4 1 21 29 15 12 10 18 18 27 8 1 12 19 19 22 6 19 21 20 5 2 12 29 20 16 15 24 17 20 4 1 15 25 11 12 9 22 15 20 10 2 8 19 28 28 24 28 28 27 4 2 15 27 21 15 11 24 23 23 4 1 16 25 25 17 4 28 19 23 4 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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.5401
R-squared	0.2917
RMSE	3.6098

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11	16.25	-5.25
2	22	16.25	5.75
3	23	23.9666666666667	-0.966666666666665
4	21	20.4361702127660	0.563829787234042
5	19	20.4361702127660	-1.43617021276596
6	12	16.25	-4.25
7	24	20.4361702127660	3.56382978723404
8	21	20.4361702127660	0.563829787234042
9	21	20.4361702127660	0.563829787234042
10	26	23.9666666666667	2.03333333333333
11	18	20.4361702127660	-2.43617021276596
12	21	20.4361702127660	0.563829787234042
13	22	16.25	5.75
14	26	23.9666666666667	2.03333333333333
15	20	23.9666666666667	-3.96666666666667
16	20	20.4361702127660	-0.436170212765958
17	26	23.9666666666667	2.03333333333333
18	27	20.4361702127660	6.56382978723404
19	27	20.4361702127660	6.56382978723404
20	16	20.4361702127660	-4.43617021276596
21	26	23.9666666666667	2.03333333333333
22	20	20.4361702127660	-0.436170212765958
23	25	23.9666666666667	1.03333333333333
24	16	20.4361702127660	-4.43617021276596
25	20	20.4361702127660	-0.436170212765958
26	20	20.4361702127660	-0.436170212765958
27	24	20.4361702127660	3.56382978723404
28	24	20.4361702127660	3.56382978723404
29	22	16.25	5.75
30	18	16.25	1.75
31	21	20.4361702127660	0.563829787234042
32	17	20.4361702127660	-3.43617021276596
33	15	16.25	-1.25
34	28	23.9666666666667	4.03333333333333
35	23	20.4361702127660	2.56382978723404
36	19	20.4361702127660	-1.43617021276596
37	15	20.4361702127660	-5.43617021276596
38	26	23.9666666666667	2.03333333333333
39	20	23.9666666666667	-3.96666666666667
40	11	16.25	-5.25
41	17	20.4361702127660	-3.43617021276596
42	16	20.4361702127660	-4.43617021276596
43	21	20.4361702127660	0.563829787234042
44	18	20.4361702127660	-2.43617021276596
45	17	20.4361702127660	-3.43617021276596
46	21	20.4361702127660	0.563829787234042
47	18	20.4361702127660	-2.43617021276596
48	16	16.25	-0.25
49	13	16.25	-3.25
50	28	23.9666666666667	4.03333333333333
51	25	16.25	8.75
52	24	20.4361702127660	3.56382978723404
53	15	20.4361702127660	-5.43617021276596
54	21	20.4361702127660	0.563829787234042
55	11	16.25	-5.25
56	27	20.4361702127660	6.56382978723404
57	23	20.4361702127660	2.56382978723404
58	21	20.4361702127660	0.563829787234042
59	16	20.4361702127660	-4.43617021276596
60	20	16.25	3.75
61	21	23.9666666666667	-2.96666666666667
62	10	16.25	-6.25
63	18	23.9666666666667	-5.96666666666667
64	20	20.4361702127660	-0.436170212765958
65	21	23.9666666666667	-2.96666666666667
66	24	23.9666666666667	0.033333333333335
67	26	20.4361702127660	5.56382978723404
68	23	20.4361702127660	2.56382978723404
69	22	20.4361702127660	1.56382978723404
70	13	16.25	-3.25
71	27	23.9666666666667	3.03333333333333
72	24	20.4361702127660	3.56382978723404
73	19	20.4361702127660	-1.43617021276596
74	17	20.4361702127660	-3.43617021276596
75	16	20.4361702127660	-4.43617021276596
76	20	20.4361702127660	-0.436170212765958
77	8	16.25	-8.25
78	16	20.4361702127660	-4.43617021276596
79	17	20.4361702127660	-3.43617021276596
80	23	23.9666666666667	-0.966666666666665
81	18	20.4361702127660	-2.43617021276596
82	24	23.9666666666667	0.033333333333335
83	17	16.25	0.75
84	20	20.4361702127660	-0.436170212765958
85	22	20.4361702127660	1.56382978723404
86	22	20.4361702127660	1.56382978723404
87	20	20.4361702127660	-0.436170212765958
88	18	20.4361702127660	-2.43617021276596
89	21	20.4361702127660	0.563829787234042
90	23	20.4361702127660	2.56382978723404
91	28	23.9666666666667	4.03333333333333
92	19	20.4361702127660	-1.43617021276596
93	22	20.4361702127660	1.56382978723404
94	17	20.4361702127660	-3.43617021276596
95	25	23.9666666666667	1.03333333333333
96	22	20.4361702127660	1.56382978723404
97	21	20.4361702127660	0.563829787234042
98	15	20.4361702127660	-5.43617021276596
99	20	20.4361702127660	-0.436170212765958
100	25	20.4361702127660	4.56382978723404
101	21	20.4361702127660	0.563829787234042
102	24	23.9666666666667	0.033333333333335
103	23	20.4361702127660	2.56382978723404
104	22	23.9666666666667	-1.96666666666667
105	14	20.4361702127660	-6.43617021276596
106	11	20.4361702127660	-9.43617021276596
107	22	16.25	5.75
108	22	20.4361702127660	1.56382978723404
109	6	16.25	-10.25
110	15	20.4361702127660	-5.43617021276596
111	26	20.4361702127660	5.56382978723404
112	26	23.9666666666667	2.03333333333333
113	20	16.25	3.75
114	26	20.4361702127660	5.56382978723404
115	15	16.25	-1.25
116	25	20.4361702127660	4.56382978723404
117	22	23.9666666666667	-1.96666666666667
118	20	20.4361702127660	-0.436170212765958
119	18	16.25	1.75
120	23	16.25	6.75
121	22	20.4361702127660	1.56382978723404
122	23	20.4361702127660	2.56382978723404
123	17	20.4361702127660	-3.43617021276596
124	20	16.25	3.75
125	21	20.4361702127660	0.563829787234042
126	23	23.9666666666667	-0.966666666666665
127	25	23.9666666666667	1.03333333333333
128	25	20.4361702127660	4.56382978723404
129	21	20.4361702127660	0.563829787234042
130	22	20.4361702127660	1.56382978723404
131	18	20.4361702127660	-2.43617021276596
132	18	20.4361702127660	-2.43617021276596
133	18	23.9666666666667	-5.96666666666667
134	21	20.4361702127660	0.563829787234042
135	21	20.4361702127660	0.563829787234042
136	25	20.4361702127660	4.56382978723404
137	24	20.4361702127660	3.56382978723404
138	24	20.4361702127660	3.56382978723404
139	28	23.9666666666667	4.03333333333333
140	24	23.9666666666667	0.033333333333335
141	22	23.9666666666667	-1.96666666666667
142	22	20.4361702127660	1.56382978723404
143	20	20.4361702127660	-0.436170212765958
144	25	20.4361702127660	4.56382978723404
145	13	20.4361702127660	-7.43617021276596
146	21	20.4361702127660	0.563829787234042
147	23	20.4361702127660	2.56382978723404
148	18	20.4361702127660	-2.43617021276596

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/2ax6r1291995139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/2ax6r1291995139.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/3ax6r1291995139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/3ax6r1291995139.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/4exne1291995139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291996423is6mzj8uprkao20/4exne1291995139.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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')

}

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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We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

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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