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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 19:33:19 +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/t1292355065zlv8gly3h14e4lo.htm/, Retrieved Tue, 14 Dec 2010 20:31:05 +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/t1292355065zlv8gly3h14e4lo.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 «

6 4 15 10 4 4 1 11 9 9 19 7 7 1 9 9 12 15 4 4 1 14 6 16 12 5 4 1 12 8 16 14 5 6 1 18 11 15 13 4 4 1 15 10 16 11 4 5 1 12 13 13 18 5 5 1 15 10 18 12 5 4 1 13 6 17 15 3 4 1 10 8 14 15 7 7 1 13 5 13 9 4 5 1 17 9 15 11 6 5 1 15 11 15 16 5 4 1 13 11 13 17 7 7 1 17 9 13 11 5 5 1 21 7 16 13 5 5 1 12 6 14 9 4 4 1 15 6 18 11 4 4 1 16 10 16 12 7 7 1 11 4 17 13 5 8 1 9 9 15 13 2 2 1 14 10 11 13 4 3 1 14 13 11 14 5 7 1 12 8 15 9 4 5 1 15 10 15 9 4 4 1 11 5 12 15 4 4 1 11 8 17 10 4 4 1 13 9 14 15 5 6 1 12 7 17 13 4 6 1 24 20 10 24 4 4 1 11 8 15 13 4 4 1 12 7 7 22 2 4 1 13 6 9 9 5 5 1 11 10 14 12 5 7 1 14 11 11 16 7 8 1 16 12 15 10 7 7 1 12 7 16 13 4 4 1 21 12 17 11 4 4 1 6 6 15 13 4 2 1 14 9 15 10 2 4 1 16 5 16 11 5 4 1 18 11 16 9 4 4 1 13 10 12 14 2 4 1 11 7 15 11 4 5 1 16 8 17 10 4 5 1 11 9 19 11 5 5 1 11 8 15 12 1 1 1 20 13 14 14 4 5 1 10 7 16 21 5 7 1 12 7 15 13 5 7 1 14 9 12 12 7 7 1 12 9 18 12 4 4 1 12 8 13 11 4 4 1 12 7 14 14 4 4 1 13 10 15 12 2 2 1 12 7 11 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4808
R-squared	0.2311
RMSE	2.0705

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	14.6545454545455	0.345454545454546
2	9	11.9375	-2.9375
3	12	11.9375	0.0625
4	16	14.6545454545455	1.34545454545455
5	16	14.6545454545455	1.34545454545455
6	15	14.6545454545455	0.345454545454546
7	16	14.6545454545455	1.34545454545455
8	13	11.9375	1.0625
9	18	14.6545454545455	3.34545454545455
10	17	11.9375	5.0625
11	14	11.9375	2.0625
12	13	14.6545454545455	-1.65454545454545
13	15	14.6545454545455	0.345454545454546
14	15	11.9375	3.0625
15	13	11.9375	1.0625
16	13	14.6545454545455	-1.65454545454545
17	16	14.6545454545455	1.34545454545455
18	14	14.6545454545455	-0.654545454545454
19	18	14.6545454545455	3.34545454545455
20	16	14.6545454545455	1.34545454545455
21	17	14.6545454545455	2.34545454545455
22	15	14.6545454545455	0.345454545454546
23	11	14.6545454545455	-3.65454545454545
24	11	14.6545454545455	-3.65454545454545
25	15	14.6545454545455	0.345454545454546
26	15	14.6545454545455	0.345454545454546
27	12	11.9375	0.0625
28	17	14.6545454545455	2.34545454545455
29	14	11.9375	2.0625
30	17	14.6545454545455	2.34545454545455
31	10	11.9375	-1.9375
32	15	14.6545454545455	0.345454545454546
33	7	11.9375	-4.9375
34	9	14.6545454545455	-5.65454545454545
35	14	14.6545454545455	-0.654545454545454
36	11	11.9375	-0.9375
37	15	14.6545454545455	0.345454545454546
38	16	14.6545454545455	1.34545454545455
39	17	14.6545454545455	2.34545454545455
40	15	14.6545454545455	0.345454545454546
41	15	14.6545454545455	0.345454545454546
42	16	14.6545454545455	1.34545454545455
43	16	14.6545454545455	1.34545454545455
44	12	14.6545454545455	-2.65454545454545
45	15	14.6545454545455	0.345454545454546
46	17	14.6545454545455	2.34545454545455
47	19	14.6545454545455	4.34545454545455
48	15	14.6545454545455	0.345454545454546
49	14	14.6545454545455	-0.654545454545454
50	16	11.9375	4.0625
51	15	14.6545454545455	0.345454545454546
52	12	14.6545454545455	-2.65454545454545
53	18	14.6545454545455	3.34545454545455
54	13	14.6545454545455	-1.65454545454545
55	14	14.6545454545455	-0.654545454545454
56	15	14.6545454545455	0.345454545454546
57	11	14.6545454545455	-3.65454545454545
58	15	14.6545454545455	0.345454545454546
59	14	11.9375	2.0625
60	16	14.6545454545455	1.34545454545455
61	14	11.9375	2.0625
62	18	14.6545454545455	3.34545454545455
63	14	14.6545454545455	-0.654545454545454
64	13	11.9375	1.0625
65	14	14.6545454545455	-0.654545454545454
66	17	14.6545454545455	2.34545454545455
67	12	14.6545454545455	-2.65454545454545
68	16	14.6545454545455	1.34545454545455
69	15	14.6545454545455	0.345454545454546
70	16	14.6545454545455	1.34545454545455
71	14	14.6545454545455	-0.654545454545454
72	17	14.6545454545455	2.34545454545455
73	14	14.6545454545455	-0.654545454545454
74	16	11.9375	4.0625
75	12	14.6545454545455	-2.65454545454545
76	13	11.9375	1.0625
77	19	14.6545454545455	4.34545454545455
78	11	11.9375	-0.9375
79	15	14.6545454545455	0.345454545454546
80	12	11.9375	0.0625
81	14	14.6545454545455	-0.654545454545454
82	11	11.9375	-0.9375
83	15	14.6545454545455	0.345454545454546
84	12	11.9375	0.0625
85	14	14.6545454545455	-0.654545454545454
86	13	14.6545454545455	-1.65454545454545
87	9	11.9375	-2.9375
88	12	11.9375	0.0625
89	15	14.6545454545455	0.345454545454546
90	17	14.6545454545455	2.34545454545455
91	14	14.6545454545455	-0.654545454545454
92	11	11.9375	-0.9375
93	13	14.6545454545455	-1.65454545454545
94	10	14.6545454545455	-4.65454545454545
95	12	14.6545454545455	-2.65454545454545
96	15	14.6545454545455	0.345454545454546
97	13	14.6545454545455	-1.65454545454545
98	13	14.6545454545455	-1.65454545454545
99	12	14.6545454545455	-2.65454545454545
100	9	11.9375	-2.9375
101	16	14.6545454545455	1.34545454545455
102	17	14.6545454545455	2.34545454545455
103	13	14.6545454545455	-1.65454545454545
104	10	11.9375	-1.9375
105	13	14.6545454545455	-1.65454545454545
106	16	14.6545454545455	1.34545454545455
107	15	14.6545454545455	0.345454545454546
108	16	14.6545454545455	1.34545454545455
109	11	14.6545454545455	-3.65454545454545
110	15	14.6545454545455	0.345454545454546
111	17	14.6545454545455	2.34545454545455
112	14	11.9375	2.0625
113	18	14.6545454545455	3.34545454545455
114	14	14.6545454545455	-0.654545454545454
115	14	14.6545454545455	-0.654545454545454
116	12	14.6545454545455	-2.65454545454545
117	11	14.6545454545455	-3.65454545454545
118	14	14.6545454545455	-0.654545454545454
119	16	14.6545454545455	1.34545454545455
120	17	14.6545454545455	2.34545454545455
121	14	14.6545454545455	-0.654545454545454
122	14	14.6545454545455	-0.654545454545454
123	12	14.6545454545455	-2.65454545454545
124	12	14.6545454545455	-2.65454545454545
125	11	11.9375	-0.9375
126	15	14.6545454545455	0.345454545454546
127	14	14.6545454545455	-0.654545454545454
128	10	11.9375	-1.9375
129	13	14.6545454545455	-1.65454545454545
130	15	14.6545454545455	0.345454545454546
131	15	14.6545454545455	0.345454545454546
132	16	14.6545454545455	1.34545454545455
133	8	11.9375	-3.9375
134	9	11.9375	-2.9375
135	15	14.6545454545455	0.345454545454546
136	11	14.6545454545455	-3.65454545454545
137	15	14.6545454545455	0.345454545454546
138	16	14.6545454545455	1.34545454545455
139	16	14.6545454545455	1.34545454545455
140	15	14.6545454545455	0.345454545454546
141	13	14.6545454545455	-1.65454545454545
142	15	14.6545454545455	0.345454545454546

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/2lb3q1292355192.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/2lb3q1292355192.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/3lb3q1292355192.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/3lb3q1292355192.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/4e33b1292355192.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292355065zlv8gly3h14e4lo/4e33b1292355192.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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alert you about important changes or upgrades in resources or applications

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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