Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 22 »

*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 20:22:32 +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/t12930492672jc7rn0y22686vd.htm/, Retrieved Wed, 22 Dec 2010 21:21:08 +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/t12930492672jc7rn0y22686vd.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 «

0.01244 0.00149 0.01848 0.00338 0.00099 -0.01826 -0.01025 0.04860 0.04399 -0.03429 0.00779 0.01150 0.01244 0.00149 0.01848 0.00338 0.00099 -0.03086 -0.01025 0.04860 0.04399 -0.03429 -0.00793 0.01150 0.01244 0.00149 0.01848 0.00338 0.04033 -0.03086 -0.01025 0.04860 0.04399 -0.01514 -0.00793 0.01150 0.01244 0.00149 0.01848 -0.02352 0.04033 -0.03086 -0.01025 0.04860 0.01778 -0.01514 -0.00793 0.01150 0.01244 0.00149 0.00573 -0.02352 0.04033 -0.03086 -0.01025 0.00634 0.01778 -0.01514 -0.00793 0.01150 0.01244 0.01805 0.00573 -0.02352 0.04033 -0.03086 0.00770 0.00634 0.01778 -0.01514 -0.00793 0.01150 -0.01887 0.01805 0.00573 -0.02352 0.04033 0.00692 0.00770 0.00634 0.01778 -0.01514 -0.00793 0.04363 -0.01887 0.01805 0.00573 -0.02352 0.00029 0.00692 0.00770 0.00634 0.01778 -0.01514 0.02875 0.04363 -0.01887 0.01805 0.00573 0.02487 0.00029 0.00692 0.00770 0.00634 0.01778 -0.00393 0.02875 0.04363 -0.01887 0.01805 0.01708 0.02487 0.00029 0.00692 0.00770 0.00634 0.05280 -0.00393 0.02875 0.04363 -0.01887 0.02540 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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.594
R-squared	0.3528
RMSE	0.02

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	0.01244	0.00971397727272727	0.00272602272727273
2	0.0115	0.00971397727272727	0.00178602272727273
3	-0.00793	0.00971397727272727	-0.0176439772727273
4	-0.01514	0.00971397727272727	-0.0248539772727273
5	0.01778	0.00971397727272727	0.00806602272727273
6	0.00634	0.00971397727272727	-0.00337397727272727
7	0.0077	0.00971397727272727	-0.00201397727272727
8	0.00692	0.00971397727272727	-0.00279397727272727
9	0.00029	0.00971397727272727	-0.00942397727272727
10	0.02487	0.00971397727272727	0.0151560227272727
11	0.01708	0.00971397727272727	0.00736602272727273
12	0.0254	0.00971397727272727	0.0156860227272727
13	0.02935	0.00971397727272727	0.0196360227272727
14	0.02615	0.00971397727272727	0.0164360227272727
15	0.00424	0.00971397727272727	-0.00547397727272727
16	-0.00032	0.00971397727272727	-0.0100339772727273
17	-0.02353	-0.0468714285714286	0.0233414285714286
18	0.01387	0.00971397727272727	0.00415602272727273
19	0.01286	0.00971397727272727	0.00314602272727273
20	-0.00609	0.00971397727272727	-0.0158039772727273
21	0.00635	0.00971397727272727	-0.00336397727272727
22	0.02049	0.00971397727272727	0.0107760227272727
23	0.00332	0.00971397727272727	-0.00639397727272727
24	0.00409	0.00971397727272727	-0.00562397727272727
25	0.02753	0.00971397727272727	0.0178160227272727
26	0.01205	0.00971397727272727	0.00233602272727273
27	0.01773	0.00971397727272727	0.00801602272727273
28	-0.00897	0.00971397727272727	-0.0186839772727273
29	-0.01226	0.00971397727272727	-0.0219739772727273
30	0.00644	0.00971397727272727	-0.00327397727272727
31	-0.00059	0.00971397727272727	-0.0103039772727273
32	0.01707	0.00971397727272727	0.00735602272727272
33	-0.00104	0.00971397727272727	-0.0107539772727273
34	0.01272	0.00971397727272727	0.00300602272727273
35	0.01859	0.00971397727272727	0.00887602272727273
36	0.03238	0.00971397727272727	0.0226660227272727
37	0.03132	0.00971397727272727	0.0216060227272727
38	0.01412	0.00971397727272727	0.00440602272727273
39	0.00588	0.00971397727272727	-0.00383397727272727
40	0.05686	0.00971397727272727	0.0471460227272727
41	0.05681	0.00971397727272727	0.0470960227272727
42	-0.04078	0.00971397727272727	-0.0504939772727273
43	0.02507	0.00971397727272727	0.0153560227272727
44	0.006	0.00971397727272727	-0.00371397727272727
45	0.00249	0.00971397727272727	-0.00722397727272727
46	0.01885	0.00971397727272727	0.00913602272727273
47	0.00125	0.00971397727272727	-0.00846397727272727
48	0.00695	0.00971397727272727	-0.00276397727272727
49	-0.01563	0.00971397727272727	-0.0253439772727273
50	0.00814	0.00971397727272727	-0.00157397727272727
51	0.02368	0.00971397727272727	0.0139660227272727
52	0.04099	0.00971397727272727	0.0312760227272727
53	0.00731	0.00971397727272727	-0.00240397727272727
54	-0.0173	0.00971397727272727	-0.0270139772727273
55	-0.00183	0.00971397727272727	-0.0115439772727273
56	-0.0383	0.00971397727272727	-0.0480139772727273
57	-0.01249	0.00971397727272727	-0.0222039772727273
58	0.01229	0.00971397727272727	0.00257602272727273
59	-0.01747	0.00971397727272727	-0.0271839772727273
60	-0.02645	0.00971397727272727	-0.0361639772727273
61	0.04038	0.00971397727272727	0.0306660227272727
62	0.02925	0.00971397727272727	0.0195360227272727
63	0.0227	0.00971397727272727	0.0129860227272727
64	-0.0046	0.00971397727272727	-0.0143139772727273
65	-0.01894	0.00971397727272727	-0.0286539772727273
66	-0.00966	0.00971397727272727	-0.0193739772727273
67	0.00392	0.00971397727272727	-0.00579397727272727
68	-0.03105	-0.0468714285714286	0.0158214285714286
69	-0.0279	0.00971397727272727	-0.0376139772727273
70	-0.09625	-0.0468714285714286	-0.0493785714285714
71	-0.05388	-0.0468714285714286	-0.00700857142857143
72	-0.05034	-0.0468714285714286	-0.00346857142857143
73	-0.02846	-0.0468714285714286	0.0184114285714286
74	-0.01454	0.00971397727272727	-0.0242539772727273
75	0.01284	0.00971397727272727	0.00312602272727273
76	0.03762	0.00971397727272727	0.0279060227272727
77	0.01973	0.00971397727272727	0.0100160227272727
78	0.03178	0.00971397727272727	0.0220660227272727
79	0.01329	0.00971397727272727	0.00357602272727273
80	0.05094	0.00971397727272727	0.0412260227272727
81	-0.00804	0.00971397727272727	-0.0177539772727273
82	0.01116	0.00971397727272727	0.00144602272727273
83	0.01128	0.00971397727272727	0.00156602272727273
84	0.02227	0.00971397727272727	0.0125560227272727
85	0.01494	0.00971397727272727	0.00522602272727273
86	-0.02514	0.00971397727272727	-0.0348539772727273
87	0.02975	0.00971397727272727	0.0200360227272727
88	0.05216	0.00971397727272727	0.0424460227272727
89	-0.04459	-0.0468714285714286	0.00228142857142857
90	-0.02212	0.00971397727272727	-0.0318339772727273
91	0.03171	0.00971397727272727	0.0219960227272727
92	0.02985	0.00971397727272727	0.0201360227272727
93	0.01545	0.00971397727272727	0.00573602272727273
94	0.0114	0.00971397727272727	0.00168602272727273
95	0.00238	0.00971397727272727	-0.00733397727272727

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/22mst1293049346.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/22mst1293049346.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/32mst1293049346.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/32mst1293049346.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/4cvrw1293049346.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930492672jc7rn0y22686vd/4cvrw1293049346.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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
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.

FreeStatistics.org is powered by